FORT SMITH, Ark. (KNWA/KFTA) — The Arkansas Department of Health has confirmed a case of Legionella at a Fort Smith nursing home. The ADH says that the case was found at Methodist Village Senior Living.

ADH confirms case of Legionella at Fort Smith nursing home

Aktivolabs, a digital health science company on a mission to prevent chronic diseases globally, announced today it has joined the American Heart Association Center for Health Technology & Innovation (the Center) Innovators' Network.

Aktivolabs joins Innovators' Network at American Heart Association Center for Health Technology & Innovation

At Wellstar Health System in Marietta ... to improve patient and hospital team member safety while enhancing operational efficiency. The first four investments made by Catalyst by Wellstar target ways to improve care delivery, safety, decontamination ...

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The University’s commitment was driven by preserving health care in Iowa City, which we believe this process has done.” Mercy Hospital and its satellite clinics filed for Chapter 11 bankruptcy ...

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Just a week after Richmond Heights Mayor Kim Thomas expressed doubt as to whether a health and wellness center would be opened, as promised, by University Hospitals, she told City Council that UH has told her the center will,

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Kaiser ordered to revamp delivery of behavioral health care services

Today, Ōmcare announced that it is launching the Ōmcare Home Health Hub®, an all-in-one, customizable home health platform that allows care providers to extend care into the home via a secure infrastructure with one-touch access to telehealth services,

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Without community leaders, Waukesha wouldn’t hum along nearly as smoothly as it does. We’d miss their contributions in big and small ways.  Patch has partnered with T-Mobile to recognize these often unsung community leaders and celebrate their contributions.

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DaVita

davita

In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge
personal experience
specialist skills
marketing hype
ideology
casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments!

Evidence-Based Management in Business: Will it make a difference?

Most companies today are technology companies. They aim at digitizing their back-office and frontline operations, which requires extensive IT assistance. However, trying to pursue agile methods, many businesses choose to outsource their digital transformation to external consultants’ IT teams as the alternative to running their own. At the moment, the software development market offers the services of thousands of companies for all kinds of needs. To shortlist the candidates who will turn software into an enabler instead of a roadblock, you need to compile your list of business requirements first and take your time to carefully revise the players corresponding to these requirements. Here are the key criteria to consider: in order to evaluate your project budget Cost per hour to understand whether the vendor has experience in your industry or with similar solutions Portfolio and major customers on trustworthy platforms and recommendations from past clients Reviews to make sure the vendor’s team has worked with the platforms and tools you aim at Tech stack that prove the company has access to the required technological resources and up-to-date competencies Official partnerships and certifications if it’s of significance to the project Location If you need your software development partner to be based in a certain location, it significantly narrows down available options but doesn’t make the choice easier in any way as it still requires the same rigorous level of evaluation and verification. In case you need an onshore software partner based in Denver, Colorado, Explority has compiled a list of the top software engineering companies in Denver to prepare the ground for your further research. To pick the most prominent vendors among Denver software companies, we have scored them against five criteria: Portfolio Major customers Tech stack Pricing Reviews As a result, we have shortlisted 30 best companies, each with its own strengths, competencies and industry focus. Now it’s your turn to choose a partner fit for your project’s scope and requirements. 30 Best Software Companies in Denver 1. Iflexion Launched in 1999, Iflexion has grown to a team of 850+ professionals who have completed software development projects for more than 500 customers across different industries, with the focus on solutions for ISVs, media and entertainment, ecommerce, finance, education, and healthcare. Its key customers are PayPal, Philips, Expedia, Toyota, eBay, adidas, Shell, and more. With its ample development stack ranging from programming languages (.NET, Java, PHP, etc.) to platforms (Salesforce, SharePoint, Magento, etc.), Iflexion has delivered enterprise web and mobile apps, portals, intranets as well as BI, AI, and IoT solutions. Iflexion is listed as one of the top custom software development companies by Clutch and one of the best engineering firms in Denver by GoodFirms. 2. Gorilla Logic Since 2002, Gorilla Logic has been delivering nearshore software development and consulting services. The company has grown to 500+ employees who specialize in Java, Ruby, Python, .NET, Angular, React, iOS and Android development. They build enterprise web and mobile apps and deliver UI/UX development, DevOps, data science, IoT and QA services. Gorilla Logic is listed as a top app development company by Clutch and is a certified AWS and Microsoft partner. 3. MojoTech MojoTech was founded in 2008 and completed 150+ projects. The company combines engineering, design and product management competences and delivered software solutions for such customers as Shell, Schneider Electric, Pivotal, and more. MojoTech is proficient in full-stack engineering, mobile and web development, legacy upgrades, and cloud migration. The company was featured in The New York Times, The Wall Street Journal, and TechCrunch. 4. Computer Enterprises Computer Enterprises is a software development company that has been providing consulting and engineering services since 1992. It specializes in strategy roadmaps, tech enablement planning, UI/UX design, custom web and mobile apps, app maintenance, cloud solutions, DevOps, AI, big data, and BI. The company is certified by Microsoft, Google Cloud, Sitecore, Cloudera, Redgate, and AWS. 5. Woodridge Software Woodridge Software is an award-winning Colorado-based software development agency that specializes in bespoke web and mobile apps and custom integrations for startups and Fortune 500 enterprises. Since its launch in 2012, the company has been focusing on projects in finance, cybersecurity, education, and healthcare. It pays special attention to software security, runs rigorous security testing, and ensures their solutions’ compliance with major security standards. 6. Skookum Skookum and its team of designers and developers have been solving digital challenges and helping businesses modernize their technologies since 2005. The company focuses on design, software development, digital strategy, and innovative solutions such as web and mobile apps, APIs, IoT and cloud-based software. Skookum also has hands-on experience in launching R&D labs and environments from scratch to let customers explore emerging technologies, build prototypes, and accelerate time-to-market. 7. Bayshore Solutions Bayshore Solutions is a Denver software development agency founded in 1996. Its cross-functional teams have provided tailored web development, ecommerce, and hosting solutions for 2,600 customers across different domains, such as insurance, banking, logistics, and retail. The company offers full-cycle services, from digital strategy and web design and development to marketing, content management, and web hosting. Bayshore Solutions is a certified partner of nopCommerce, Sitefinity, HubSpot, and Google. 8. Chromedia Chromedia is a custom software development vendor in Denver that partners with companies of any scale, be it early-stage startups or large corporations. Founded in 2011, the company has delivered hundreds of projects, from customizing CRM and CMS platforms to building SaaS platforms and mobile apps from scratch. It also offers team augmentation services. Chromedia is a certified AWS partner; they are also experienced in PHP, .NET, C#, Ruby on Rails, Python, React, Angular, Swift, and more. 9. Cuttlesoft Launched in 2011, Cuttlesoft is a creative product development agency with a track record of hundreds of completed projects. The company mainly partners with small businesses to develop web, mobile, cloud and IoT solutions in IT, entertainment and business services domains, with a special focus on data security compliance. Cuttlesoft also specializes in DevOps enablement, data analysis, and visual design. Its tech stack includes Python, Ruby, JavaScript, React, iOS, Android, and more. 10. Electrifying Design Electrifying Design is a full-service software development agency in Denver founded in 2011. It specializes in web and mobile app engineering, database development, API integration, CRM and CMS implementation. Using its proficiency in PHP, Python, SQL, JavaScript, .NET, Django, Docker, and C#, the company delivered hundreds of projects including medical software, AI, big data and blockchain solutions, real estate apps, education platforms, and more. Electrifying Design has been recognized by Clutch and GoodFirms. 11. Techtonic Techtonic is an onshore software developer located in Denver. The company was established in 2006 and focuses on tailored software development, cloud-based app development, and solutions for delivery automation and workforce transformation. Its senior-level engineers are experienced in .NET, C#, AngularJS, React, JavaScript, Ruby on Rails, and more. Techtonic has found an efficient way to close the talent gap by launching a software development training program, accredited by the US Department of Labor, which helps source, train, and hire top-tier software engineers. 12. Rogue Wave Software Founded in 1989, Rogue Wave Software is one of the oldest and largest Colorado software companies that has partnered with thousands of global enterprises, including universities and organizations all over the world. It offers a wide range of services, from native apps to cloud solutions, across finance, healthcare, telecom, education, government, and other industries. The company’s expertise covers different languages, codebases, and platforms. Rogue Wave Software also has a portfolio of its own products. 13. Spire Digital Spire Digital is a Denver-based digital transformation firm established in 1998. It offers technology consulting, UI/UX design, custom software development, DevOps, digital marketing, and team augmentation. It’s skilled in engineering web, mobile, IoT, embedded, AR/VR, AI, and blockchain solutions using .NET, JavaScript, PHP, React, and more. Spire has partnered with hundreds of companies, 400+ technology startups included. Its key customers are Western Union, Berkshire Hathaway, Navy, Volkswagen, Experian, Lockheed Martin, MillerCoors, US Navy, and more. 14. Neon Rain Interactive Since its launch in 2002, Neon Rain Interactive has grown into a team of designers and developers who specialize in custom software, web and mobile apps as well as Drupal, WordPress, ERP and ecommerce sites. It has completed hundreds of projects for startups, SMBs, enterprises, educational and non-profit organizations, including such customers as United Way, YOLO Deals, Sprint, ACE Scholarships, and more. Denver Business Journal and Clutch have recognized Neon Rain Interactive as a top web designer and software developer. 15. Cardinal Peak Cardinal Peak is a software engineering firm that since 2002 has partnered with 800+ companies, from emerging leaders to Fortune 500 enterprises. Among them are Fujitsu, Samsung, Culligan, CableLabs, Clip Interactive, and more. Its team of engineers and designers specializes in hardware development, embedded software, security integrations, cloud and mobile solutions, and connected product design. The company also has hands-on experience in audio, video, voice and signal processing technologies and completed numerous projects using Amazon Alexa, AWS, Analog Devices, Audinate, NXP, etc. Top Software Engineering Companies in Denver: Runners-up 16. JetSoftPro JetSoftPro is a software development agency in Denver that has completed 270+ projects since 2014 across more than a dozen industries. It focuses on tailored web and mobile development, integration, R&D, UI/UX design, DevOps, security, QA, and maintenance. Its tech stack comprises .NET, C/C++, Python, Xamarin, Ruby, PHP, AngularJS, and Java. Recognized by the likes of Clutch, TechReviewer, and DQ, the company also expertly deals with innovative technologies, such as machine learning, chatbot development, blockchain, IoT, AR/VR. Its key customers feature Prezzo, bvblogic, Consolidate, innocode. 17. Devetry Devetry is a Denver-based software development firm launched in 2015. It’s skilled in planning and strategy, UI/UX design, DevOps, custom mobile and SaaS development as well as data science, machine learning, and chatbots. The company has delivered projects for SMBs and large organizations from industries such as energy, education, digital marketing, wellness, and cybersecurity. Devetry works with a wide range of technologies including React, Vue, AngularJS, Python, Ruby on Rails, .NET, and Flutter. Devetry has been recognized as a top developer by Clutch and UpCity. 18. 303 Software 303 Software was founded in 2006 to deliver consulting, design, development, testing and maintenance services for startups, enterprises, non-profits, and government organizations. It has built dozens of web and mobile apps and launched three B2B SaaS startups so far. It specializes in custom-developed apps that help automate processes of any complexity for a wide range of industries, such as healthcare, telecom, agriculture, and finance. 303 Software is a proven adept of agile planning and project management. 19. Brainspire Solutions Brainspire Solutions is an onshore technology consultancy that focuses on custom web and mobile app development, app integration, data and BI consulting, portals, IoT and cloud development for Colorado-based commercial customers and non-profits. It completed numerous projects across different verticals, such as construction, information technologies, energy, ecommerce, healthcare, and more. The company is an expert in .NET, Java, Xamarin, Ionic as well as NetSuite, SAP, Oracle, Salesforce, and Microsoft Dynamics 365. 20. Credera Founded in 1999, Credera has grown to become one of the largest consulting and software development companies in Colorado, with customers ranging from Fortune 500 companies to emerging startups. Its services cover planning and strategy, product design and development, technology enablement, commerce solutions, cloud implementation, and innovation acceleration. The company is an expert in PHP, JavaScript, C#, Python as well as SharePoint, Oracle, WordPress, Drupal, Magento, and Shopify. 21. Fruition Fruition is a digital marketing and technological consultancy that specializes in design, web development, and digital marketing for SMBs, with a focus on healthcare, travel, and retail. Its expertise encompass implementation and customization of open-source platforms, such as Drupal, WordPress, and Magento. Fruition has also developed its own tools, such as an enterprise-level SEO platform and a Google penalty checker. Fruition has been recognized as a top developer by Inc. 5000 and Clutch. 22. FortyAU FortyAU is a technology consulting company in Denver where you can find first-class local developers for devising strategies, developing websites, and engineering CMS-based or custom apps based on the latest technologies, such as AI and big data. The company delivers projects of any scale for entrepreneurs and Fortune 100 companies alike, with a special focus on healthcare and finance. It successfully delivered a basketball analytics app, a healthcare facilities collaboration app, an online patient scheduling app, and more projects. 23. Flyoutsourcing Flyoutsourcing is a custom software development company that was founded in 2016. It offers extensive onshore and offshore team augmentation services for startups and SMBs. It specializes in developing tailored web and mobile apps, cloud solutions, and enterprise software as well as DevOps enablement and software maintenance. Flyoutsourcing’s team has a good command of frontend (AngularJS, React), backend (Java, Python, PHP, .NET) and cloud (AWS, Heroku, Microsoft Azure) technologies. 24. Obsidian Global Since its inception in 2013, Obsidian Global has been specializing in cybersecurity technologies and DevSecOps, with a focus on the government and defense sectors. Its customers include the Department of Homeland Security, the Department of Defense, and Federal Civilian Departments. The company is also an expert in cloud services, mobile development, and ERP implementation based on agile planning and development. Obsidian Global is a certified partner of Amazon, Google Cloud Platform, and Microsoft. 25. 27Global Since 2008, 27Global has grown into a team of 300+ engineers developing custom software for businesses of different sizes, providing local quality at offshore prices. Following the Agile methodology, the company offers the services of custom web and mobile development, microservices engineering, DevOps enablement, and innovative transformation. 27Global is experienced in Java, .NET, Angular, Node.js, React as well as AWS, Docker, Microsoft Azure, and Salesforce. 26. Neudesic Neudesic is a technology company founded in 2002. It specializes in cutting-edge technologies and offers the services of cloud modernization, data asset management, workplace automation, intelligent platform development, and DevOps enablement. Neudesic mostly focuses on healthcare, food and beverages, energy, and hospitality industries. The company has its own products, for example, apps for knowledge management and business collaboration. Neudesic is a Microsoft and Nintex partner. 27. Dev IQ Dev IQ is a consulting and custom software development firm that is driven by the principles of Agile, design thinking, and empathic design. It specializes in UI/UX design, web and mobile app development, IoT ecosystems, and cloud-native solutions. It has hands-on experience with such cloud providers as AWS, ClearDATA, Google Cloud Platform, and Microsoft Azure. The company’s key customers are Visa, DaVita, Patagonia, Kiehl’s, Denver Health, Collective Goods, and more. 28. Denovo Denovo is a full-service software development firm that has specialized in product design, engineering, and maintenance since 2003. It mostly focuses on cloud hosting and infrastructure, managed services, and disaster recovery. Denovo is an AWS Partner and Oracle Platinum Partner. It successfully completed 2,400+ implementation projects based on Oracle ERP and Oracle Cloud for commercial and public-sector customers, such as Thule, Qualtek, Sunrise Medical, Phoenix, and more. 29. Precision Solutions Precision Solutions is a software development firm that has partnered with dozens of companies since 2012, from the largest Fortune 100 defense contractors to small businesses. Its key customers are Griffin, Singer, MDC, Talley, Rare Coin, and more. The company’s range of services covers custom software development, cloud migration, and integration. Precision Solutions also released its own products, such as Odoo apps. 30. Paralect Paralect is a custom software engineering agency founded in 2009. With the main focus on mobile app development and integration, it has launched 25 products with 12 million end users. Its products include an intelligent messaging platform, automotive workshop software, a freelance platform, and real estate lead management software. The company also specializes in MVPs for startups and innovative businesses, delivered in 12 weeks. Time to pick your Denver software developer We hope this list will become a good place to start for your further research. Match it with your project requirements, collect references, listen to your gut feeling, and choose your future software development partner.

Top 30 Software Development Companies in Denver

30 Most Trusted Java Development Companies on the Market in 2020

[Photo by on ] freestocks.org Unsplash CVS is offering to pay $66 billion for Aetna — a blending that could turn local drugstores into health insurers. Assessing the impacts of the recent rash of mergers and takeovers in the healthcare field. The mega-marketplace revolution is in full swing across all industries right now. Amazon shelled out $13.7 billion for Whole Foods in an effort to gain a stronger foothold in the grocery industry, AT&T wants to buy Time Warner for $85 billion, UnitedHealth Group is acquiring DaVita Medical Group and the latter’s 300 doctors’ clinics for $4.9 billion, and CVS is offering to pay $66 billion for Aetna — a blending that could turn local drugstores into health insurers. These deals collectively paint a picture of a business environment that’s ripe for consolidation and ready to merge. Of particular interest to the healthcare industry — already grappling with changes and turmoil on many different fronts — the proposed CVS-Aetna deal could find the acquirer creating a unified, seamless benefits offering to customers (such as large employers). “The combined behemoth would also have a large and diversified store of health data, an important asset in today’s competitive environment,” the reports, noting that Amazon’s intention to “move into the pharmacy business helped spur CVS’ move on Aetna.” Wall Street Journal From this recent spate of mega-deals, the entities involved expect benefits like improved economies of scale (the “power of one” versus various, scattered entities), better profits (which, in turn, can fund R&D and other initiatives); and new faces on the newly-combined firm’s management and leadership teams. The disadvantages include, but aren’t limited to, difficulties coordinating and communicating across the combined entity and the length of time it takes to physically “merge” the two companies into a single, efficient organization. The question is, how will these combined superpowers impact their customers and their respective industries as a whole? Here are four ways things could play out: It could spawn a groundswell of mega-mergers and, subsequently, less competition. If the merger goes through, it could trigger another wave of takeovers. In fact, analysts feel it could cause a wave of industry consolidation, with Walgreens Boots Alliance (WBA) and Rite-Aid (RAD) currently seeking mergers or acquisitions with other major health insurers. This could give a smaller concentration of companies a bigger stronghold on the nation’s healthcare industry. Consumer options could be reduced and customers could be subjected to higher co-pays (i.e., if they select a non-CVS drugstore to fill an Aeta plan prescription). Healthcare is a fundamental service that’s comparable to cable, electricity, and telephones, all of which have been consolidated under mega brands and via mega-mergers. And while healthcare is a highly-regulated industry, the idea of only having one drugstore or one insurance provider to choose from could push prices up while whittling down choices. Better economies of scale could be in sight for healthcare insurers and other providers. Under pressure to address losses suffered from former President Barack Obama’s Affordable Care Act, and then navigate the murky waters of Obamacare eradication, major health insurers have been grappling with reductions in program offerings. Health care mergers and acquisitions have been rampant over the last few years, making the savings associated with economies of scale that much more critical as margins tighten. In the CVS-Aetna deal, for example, the combined entity would also have more firepower against a giant e-tailer like Amazon — an advantage that companies across many industries are trying to figure out right now. In fact, the “Amazon Effect” is one of the drivers of healthcare mergers and the quest to provide a single point of service and information for customers. This, in turn, helps to reduce the cost of running the healthcare supply chain, the cost of customer acquisition, and the cost of healthcare delivery. Amazon is already leveraging these advantages in numerous marketplaces, and healthcare firms want to be able to replicate that in their own industry. Premiums may not get any lower. Seeking relief from rising insurance and healthcare bills, consumers are waiting for a miracle to come and lower their bills. The CVS-Aetna deal probably isn’t their knight in shining armor. In fact, there’s been no real impact on lowering premiums among beneficiaries due to health insurance mergers, according to a study. In fact, statistics show that less competition in the health payer market can result in higher monthly premiums for beneficiaries, and particularly for those in employer-sponsored, commercial health plans. But just because you merge an insurance company with the drugstore doesn’t mean consumers will be privy to lower prices. At a basic level, premiums really only go down if fewer people visit the doctor or hospital, and a mega-merger isn’t going to make that happen. Harvard Business Review Organizations may stop innovating. When was the last time your cable company introduced a groundbreaking, innovative product? Or your electric company rolled out a pioneering new service? The answer is probably “never,” and that’s because competition spawns innovation. In the absence of a competitor knocking on your customers’ doors, trying to sell them a newer, better, and more efficient product or service, the drive to innovate dwindles to zero. This could happen if we start to see more healthcare companies merging and creating less competition for themselves. Anytime you have a captive consumer, companies don’t think too hard about innovation. As more companies look to merge with one another in an effort to gain operational savings, reduce overhead, eliminate redundancies, and gain a foothold in new verticals, the underlying tenets of healthcare probably won’t change much — namely because transformation can’t happen unless we take a holistic view of the problem and fix all of the pain points within that larger picture. Because all of the systems that make up the bigger healthcare picture are interdependent, you can’t just fix one part and expect transformation to occur. In the end, preventative medicine is what’s going to reduce the cost of healthcare, and that’s both an individual and a social responsibility. Copyright © 2017 by Faisal Hoque. All rights reserved. I am an . Founder of and other companies. Shadoka enables aspirations to lead, innovate, and transform. Shadoka’s accelerators and solutions bring together the management frameworks, digital platforms, and thought leadership to enable innovation, transformation, entrepreneurship, growth and social impact. entrepreneur and author SHADOKA Author of “ ” (McGraw Hill) and “ ” (Motivational Press). Follow me on Twitter . Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability Survive to Thrive: 27 Practices of Resilient Entrepreneurs, Innovators, and Leaders Faisal Hoque Use the Everything Connects leadership app and Suvvive to Thrive resiliancy app for free.

The Rise of the Healthcare Mega-Marketplaces: What Does It Mean For You?

Hey Hackers! I’m , and I’m the Mechanical Design Engineer @ and Industrial Design Lead @ Davit Ayvazyan Alpha Tek Automation, Inc. Robomart, Inc. First of all, a huge thank you to the HackerNoon community and staff for nominating me for a 2021 Noonies award! I’ve been nominated in the following category, please do check out this award page and vote: HackerNoon Contributor of the Year - WORK As someone in the mechanical design industry, I believe that the most exciting technology of the present is generative design because it takes the design process to the next level. You just enter requirements, and AI generates the design. Learn more about my thoughts and opinions on mechanical design and my journey in the mechanical engineering industry via the interview below. 1. What do you do and why do you do it? (tell us your story) I am doing mechanical design, and I love it. I graduated from the Faculty of Mechanical Engineering Technology in 2007. But it seems to me that from childhood, I began to engage in mechanical engineering since I used to open everything to see what is inside and how it works. So my engineering is innate :) 2. Tell us more about the things you create / write / manage / build! My profession is an amazing journey. During my career, I have worked on various projects that are very different from each other. Since I am also in industrial design, this expands the range of my projects even more. I designed a unique wristwatch, miniature devices with circuits inside for a smart packaging company, plasma cutting machines, airport radar mechanics, and much more. 3. How did you end up on your current career path? Do you like it? After university, I worked for two local companies in Armenia, which gave me excellent practical knowledge. Then I decided to switch to remote work in order to work in foreign companies, and I fell in love with it. I loved working on different types of projects, meeting and working with great, smart people from different countries and cultures. And it was amazing. 4. What tech are you most excited or passionate about right now and why? It's definitely a Generative Design. This is a new way of designing using the power of artificial intelligence. The engineer just needs to communicate correctly with the machine in terms of conditions and requirements. After the AI generates the design faster, efficient, and unlike anything people could think of. 5. What tech are you most worried about right now and why? I couldn't find any tech that I'm worried about. It depends on who is using this technology and for what purpose. I am more concerned about people who blame technology for their problems rather than looking for the root cause. 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? Most likely, I would invest in several startups in the health and fitness industry. Because they have great ideas and due to lack of money, they will remain on paper. Also, in some projects aimed at raising the level of education of children in Armenia. 7. What are you currently learning? I’m interested in Biomechanical Engineering. This is a new field for me, and I have a lot to learn. 8. What’s the best advice you’ve ever given someone? Difficult to judge, but in my opinion, this: “Do not rush. Be careful and do it right. Since no one will remember that you finished it first, but everyone will remember that you made mistakes.” 9. What’s the best advice you’ve ever received? I got a lot of great advice, but right now, I'm thinking about the following: “If you can't beat the fear, just do it scared” I first heard this from Will Smith. About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

‘I’m Concerned About People Who Blame Technology for Their Problems’: Davit Ayvazyan 

The MERN stack has emerged as a powerful framework for modern web development, combining MongoDB, Express.js, React.js, and Node.js. This guide will walk you through setting up a MERN stack application efficiently, leveraging a pre-configured repository and streamlined development environment management. Overview of MERN Stack The MERN stack comprises four key technologies: MongoDB: A flexible, document-based NoSQL database
Express.js: A minimal web application framework for Node.js
React.js: A JavaScript library for building dynamic user interfaces
Node.js: A JavaScript runtime for scalable server-side applications Each component plays a crucial role in creating dynamic and interactive web applications. Setting Up the Development Environment Prerequisites Basic JavaScript knowledge
An Integrated Development Environment (IDE) like Visual Studio Code
Docker installed for containerized development
Daytona for managing your dev environments Getting Started with the MERN App We'll use a Creator Relationship Management (CRM) App as our example. This full-stack application is designed for influencers, podcasters, and creators to manage client relationships efficiently. Setting up MongoDB Cluster Follow the MongoDB Atlas Getting Started guide to create a cluster and obtain your MongoDB URI. Note: If you encounter connection issues, allow access from anywhere in your cluster's Network Access settings. Configuring the Development Workspace We'll use Daytona to automate the setup of a Node.js dev environment with necessary tools and port forwarding based on our starter project. Cloning and Setting Up Your MERN Stack Environment Create a workspace:
daytona create https://github.com/daytonaio-experiments/starter-mern-saas.git



Set your preferred IDE:
daytona ide



Open the workspace:
daytona code



Start the backend server:


Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I:
MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"



Run the server:
cd backend && npm run dev





Start the frontend:


Create a .env file in the frontend directory:
VITE_BACKEND_URL="http://localhost:8000/api/customers/"



Run the frontend:
cd frontend && npm run dev Your MERN app should now be running. Repository Structure The repository is organized into: Backend: Server-side code (Node.js and Express.js)
Frontend: Client-side application (React.js and Tailwind CSS)
.devcontainer: Configuration files for the development environment Streamlined Setup with .devcontainer The .devcontainer/devcontainer.json file provides a predefined development environment. Here's the configuration: {
    "name": "Node.js, Express, React, MongoDB & Tailwind",
    "image": "ubuntu:22.04",
    "features": {
        "ghcr.io/devcontainers/features/common-utils:2.4.7": {
            "username": "daytona",
            "userUid": 1000,
            "userGid": 1000,
            "configureZshAsDefaultShell": true
        },
        "ghcr.io/devcontainers/features/node:1": {
            "nodeGypDependencies": true,
            "version": "lts",
            "nvmVersion": "0.40.0"
        },
        "ghcr.io/devcontainers/features/git:1": {}
    },
    "overrideFeatureInstallOrder": [
        "ghcr.io/devcontainers/features/common-utils",
        "ghcr.io/devcontainers/features/git",
        "ghcr.io/devcontainers/features/node"
    ],
    "portsAttributes": {
        "5174": {
            "label": "Frontend",
            "onAutoForward": "notify"
        },
        "8000": {
            "label": "Backend",
            "onAutoForward": "ignore"
        },
        "27017": {
            "label": "MongoDB",
            "onAutoForward": "ignore"
        }
    },
    "customizations": {
        "vscode": {
            "extensions": [
                "mongodb.mongodb-vscode",
                "dbaeumer.vscode-eslint",
                "esbenp.prettier-vscode",
                "bradlc.vscode-tailwindcss",
                "davidanson.vscode-markdownlint"
            ]
        }
    },
    "workspaceFolder": "/workspaces/starter-mern-saas",
    "onCreateCommand": "npm install -g nodemon",
    "postCreateCommand": "cd backend && npm install && cd ../frontend && npm install",
    "remoteUser": "daytona"
} This configuration ensures a consistent development environment across different machines. Customizing the MERN Application Adding New Routes Create a new route file (e.g., productRoutes.js): const express = require('express');
const router = express.Router();
const productService = require('../service/productService');

router.get('/products', async (req, res) => {
    try {
        const products = await productService.getAllProducts();
        res.status(200).json(products);
    } catch (error) {
        res.status(500).json({ message: error.message });
    }
});

module.exports = router; Implement controller functions in a service file (productService.js): class ProductService {
    async getAllProducts() {
        // Logic to fetch all products
    }
}

module.exports = ProductService; Register the new route in app.js: const productRoutes = require('./routes/productRoutes');

app.use('/api', productRoutes); Adding New React Components Create a new component file (e.g., ProductList.jsx): import React, { useEffect, useState } from 'react';
import axios from 'axios';

const ProductList = () => {
    const [products, setProducts] = useState([]);

    useEffect(() => {
        const fetchProducts = async () => {
            const response = await axios.get('/api/products');
            setProducts(response.data);
        };
        fetchProducts();
    }, []);

    return (
        <div>
            <h1>Products</h1>
            <ul>
                {products.map(product => (
                    <li key={product.id}>{product.name}</li>
                ))}
            </ul>
        </div>
    );
};

export default ProductList; Integrate the component in App.jsx: import React from 'react';
import ProductList from './components/ProductList';

function App() {
    return (
        <div className="App">
            <ProductList />
        </div>
    );
}

export default App; Conclusion You've now set up a MERN stack application, cloned an example repository, and learned how to customize both the backend and frontend. This streamlined setup process allows you to focus on developing and enhancing your application without worrying about environmental inconsistencies. Happy coding, and enjoy your development journey with the MERN stack! The MERN stack has emerged as a powerful framework for modern web development, combining MongoDB, Express.js, React.js, and Node.js. This guide will walk you through setting up a MERN stack application efficiently, leveraging a pre-configured repository and streamlined development environment management. Overview of MERN Stack The MERN stack comprises four key technologies: MongoDB: A flexible, document-based NoSQL database Express.js: A minimal web application framework for Node.js React.js: A JavaScript library for building dynamic user interfaces Node.js: A JavaScript runtime for scalable server-side applications MongoDB : A flexible, document-based NoSQL database MongoDB Express.js : A minimal web application framework for Node.js Express.js React.js : A JavaScript library for building dynamic user interfaces React.js Node.js : A JavaScript runtime for scalable server-side applications Node.js Each component plays a crucial role in creating dynamic and interactive web applications. Setting Up the Development Environment Prerequisites Basic JavaScript knowledge An Integrated Development Environment (IDE) like Visual Studio Code Docker installed for containerized development Daytona for managing your dev environments Basic JavaScript knowledge An Integrated Development Environment (IDE) like Visual Studio Code Docker installed for containerized development Docker Daytona for managing your dev environments Daytona Getting Started with the MERN App We'll use a Creator Relationship Management (CRM) App as our example. This full-stack application is designed for influencers, podcasters, and creators to manage client relationships efficiently. Setting up MongoDB Cluster Follow the MongoDB Atlas Getting Started guide to create a cluster and obtain your MongoDB URI. MongoDB Atlas Getting Started guide Note : If you encounter connection issues, allow access from anywhere in your cluster's Network Access settings. Note Configuring the Development Workspace We'll use Daytona to automate the setup of a Node.js dev environment with necessary tools and port forwarding based on our starter project. Cloning and Setting Up Your MERN Stack Environment Create a workspace:
daytona create https://github.com/daytonaio-experiments/starter-mern-saas.git Set your preferred IDE:
daytona ide Open the workspace:
daytona code Start the backend server:


Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I:
MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"



Run the server:
cd backend && npm run dev Start the frontend:


Create a .env file in the frontend directory:
VITE_BACKEND_URL="http://localhost:8000/api/customers/"



Run the frontend:
cd frontend && npm run dev Create a workspace: daytona create https://github.com/daytonaio-experiments/starter-mern-saas.git Create a workspace: daytona create https://github.com/daytonaio-experiments/starter-mern-saas.git daytona create https://github.com/daytonaio-experiments/starter-mern-saas.git Set your preferred IDE: daytona ide Set your preferred IDE: daytona ide daytona ide Open the workspace: daytona code Open the workspace: daytona code daytona code Start the backend server: Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I:
MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"



Run the server:
cd backend && npm run dev Start the backend server: Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I:
MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" Run the server:
cd backend && npm run dev Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I: MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" Create a .env file in the backend directory with your MongoDB UR (you can find it in Atlas dashboard)I: .env MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" MONGO_URI="mongodb+srv://<username>:<password>@SOMETHING.SOMETHING.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" Run the server: cd backend && npm run dev Run the server: cd backend && npm run dev cd backend && npm run dev Start the frontend: Create a .env file in the frontend directory:
VITE_BACKEND_URL="http://localhost:8000/api/customers/"



Run the frontend:
cd frontend && npm run dev Start the frontend: Create a .env file in the frontend directory:
VITE_BACKEND_URL="http://localhost:8000/api/customers/" Run the frontend:
cd frontend && npm run dev Create a .env file in the frontend directory: VITE_BACKEND_URL="http://localhost:8000/api/customers/" Create a .env file in the frontend directory: .env VITE_BACKEND_URL="http://localhost:8000/api/customers/" VITE_BACKEND_URL="http://localhost:8000/api/customers/" Run the frontend: cd frontend && npm run dev Run the frontend: cd frontend && npm run dev cd frontend && npm run dev Your MERN app should now be running. Repository Structure The repository is organized into: Backend: Server-side code (Node.js and Express.js) Frontend: Client-side application (React.js and Tailwind CSS) .devcontainer: Configuration files for the development environment Backend : Server-side code (Node.js and Express.js) Backend Frontend : Client-side application (React.js and Tailwind CSS) Frontend .devcontainer : Configuration files for the development environment .devcontainer Streamlined Setup with .devcontainer The .devcontainer/devcontainer.json file provides a predefined development environment. Here's the configuration: .devcontainer/devcontainer.json {
    "name": "Node.js, Express, React, MongoDB & Tailwind",
    "image": "ubuntu:22.04",
    "features": {
        "ghcr.io/devcontainers/features/common-utils:2.4.7": {
            "username": "daytona",
            "userUid": 1000,
            "userGid": 1000,
            "configureZshAsDefaultShell": true
        },
        "ghcr.io/devcontainers/features/node:1": {
            "nodeGypDependencies": true,
            "version": "lts",
            "nvmVersion": "0.40.0"
        },
        "ghcr.io/devcontainers/features/git:1": {}
    },
    "overrideFeatureInstallOrder": [
        "ghcr.io/devcontainers/features/common-utils",
        "ghcr.io/devcontainers/features/git",
        "ghcr.io/devcontainers/features/node"
    ],
    "portsAttributes": {
        "5174": {
            "label": "Frontend",
            "onAutoForward": "notify"
        },
        "8000": {
            "label": "Backend",
            "onAutoForward": "ignore"
        },
        "27017": {
            "label": "MongoDB",
            "onAutoForward": "ignore"
        }
    },
    "customizations": {
        "vscode": {
            "extensions": [
                "mongodb.mongodb-vscode",
                "dbaeumer.vscode-eslint",
                "esbenp.prettier-vscode",
                "bradlc.vscode-tailwindcss",
                "davidanson.vscode-markdownlint"
            ]
        }
    },
    "workspaceFolder": "/workspaces/starter-mern-saas",
    "onCreateCommand": "npm install -g nodemon",
    "postCreateCommand": "cd backend && npm install && cd ../frontend && npm install",
    "remoteUser": "daytona"
} {
    "name": "Node.js, Express, React, MongoDB & Tailwind",
    "image": "ubuntu:22.04",
    "features": {
        "ghcr.io/devcontainers/features/common-utils:2.4.7": {
            "username": "daytona",
            "userUid": 1000,
            "userGid": 1000,
            "configureZshAsDefaultShell": true
        },
        "ghcr.io/devcontainers/features/node:1": {
            "nodeGypDependencies": true,
            "version": "lts",
            "nvmVersion": "0.40.0"
        },
        "ghcr.io/devcontainers/features/git:1": {}
    },
    "overrideFeatureInstallOrder": [
        "ghcr.io/devcontainers/features/common-utils",
        "ghcr.io/devcontainers/features/git",
        "ghcr.io/devcontainers/features/node"
    ],
    "portsAttributes": {
        "5174": {
            "label": "Frontend",
            "onAutoForward": "notify"
        },
        "8000": {
            "label": "Backend",
            "onAutoForward": "ignore"
        },
        "27017": {
            "label": "MongoDB",
            "onAutoForward": "ignore"
        }
    },
    "customizations": {
        "vscode": {
            "extensions": [
                "mongodb.mongodb-vscode",
                "dbaeumer.vscode-eslint",
                "esbenp.prettier-vscode",
                "bradlc.vscode-tailwindcss",
                "davidanson.vscode-markdownlint"
            ]
        }
    },
    "workspaceFolder": "/workspaces/starter-mern-saas",
    "onCreateCommand": "npm install -g nodemon",
    "postCreateCommand": "cd backend && npm install && cd ../frontend && npm install",
    "remoteUser": "daytona"
} This configuration ensures a consistent development environment across different machines. Customizing the MERN Application Adding New Routes Create a new route file (e.g., productRoutes.js): Create a new route file (e.g., productRoutes.js ): productRoutes.js const express = require('express');
const router = express.Router();
const productService = require('../service/productService');

router.get('/products', async (req, res) => {
    try {
        const products = await productService.getAllProducts();
        res.status(200).json(products);
    } catch (error) {
        res.status(500).json({ message: error.message });
    }
});

module.exports = router; const express = require('express');
const router = express.Router();
const productService = require('../service/productService');

router.get('/products', async (req, res) => {
    try {
        const products = await productService.getAllProducts();
        res.status(200).json(products);
    } catch (error) {
        res.status(500).json({ message: error.message });
    }
});

module.exports = router; Implement controller functions in a service file (productService.js): Implement controller functions in a service file ( productService.js ): productService.js class ProductService {
    async getAllProducts() {
        // Logic to fetch all products
    }
}

module.exports = ProductService; class ProductService {
    async getAllProducts() {
        // Logic to fetch all products
    }
}

module.exports = ProductService; Register the new route in app.js: Register the new route in app.js : app.js const productRoutes = require('./routes/productRoutes');

app.use('/api', productRoutes); const productRoutes = require('./routes/productRoutes');

app.use('/api', productRoutes); Adding New React Components Create a new component file (e.g., ProductList.jsx): Create a new component file (e.g., ProductList.jsx ): ProductList.jsx import React, { useEffect, useState } from 'react';
import axios from 'axios';

const ProductList = () => {
    const [products, setProducts] = useState([]);

    useEffect(() => {
        const fetchProducts = async () => {
            const response = await axios.get('/api/products');
            setProducts(response.data);
        };
        fetchProducts();
    }, []);

    return (
        <div>
            <h1>Products</h1>
            <ul>
                {products.map(product => (
                    <li key={product.id}>{product.name}</li>
                ))}
            </ul>
        </div>
    );
};

export default ProductList; import React, { useEffect, useState } from 'react';
import axios from 'axios';

const ProductList = () => {
    const [products, setProducts] = useState([]);

    useEffect(() => {
        const fetchProducts = async () => {
            const response = await axios.get('/api/products');
            setProducts(response.data);
        };
        fetchProducts();
    }, []);

    return (
        <div>
            <h1>Products</h1>
            <ul>
                {products.map(product => (
                    <li key={product.id}>{product.name}</li>
                ))}
            </ul>
        </div>
    );
};

export default ProductList; Integrate the component in App.jsx: Integrate the component in App.jsx : App.jsx import React from 'react';
import ProductList from './components/ProductList';

function App() {
    return (
        <div className="App">
            <ProductList />
        </div>
    );
}

export default App; import React from 'react';
import ProductList from './components/ProductList';

function App() {
    return (
        <div className="App">
            <ProductList />
        </div>
    );
}

export default App; Conclusion You've now set up a MERN stack application, cloned an example repository, and learned how to customize both the backend and frontend. This streamlined setup process allows you to focus on developing and enhancing your application without worrying about environmental inconsistencies. Happy coding, and enjoy your development journey with the MERN stack!

MERN Stack: A Guide for New Developers

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 7. DISCUSSION AND LIMITATIONS Deep Lake’s primary use cases include (a) Deep Learning Model Training, (b) Data Lineage and Version Control, (c) Data Querying, and Analytics, (d) Data Inspection and Quality Control. We took NumPy [55] arrays as a fundamental block and implemented version control, streaming dataloaders, visualization engine from scratch. 7.1 Format Design Space The Tensor Storage Format (TSF) is a binary file format designed specifically for storing tensors, which are multi-dimensional arrays of numerical values used in many machine learning and deep learning algorithms. The TSF format is designed to be efficient and compact, allowing for fast and efficient storage and access of tensor data. One key advantage of the TSF format is that it supports a wide range of tensor data types, including dynamically shaped tensors. In comparison, the Parquet [79] and Arrow [13] formats are columnar file formats that are designed for storing and processing large analytical datasets. Unlike TSF, which is specifically designed for tensor data, Parquet and Arrow are optimized for efficient storage and querying of analytical workloads on tabular and time-series data. They use columnar storage and compression techniques to minimize storage space and improve performance, making them suitable for big data applications. However, TSF has some advantages over Parquet and Arrow when it comes to tensor data. TSF can support tensor operations and efficient streaming to deep learning frameworks. Other tensor formats [18, 52, 23, 57] are efficient for massively massively parallelizable workloads as they don’t require coordination across chunks. Tensor Storage Format key trade-off is enabling to store dynamically shape arrays inside a tensor without padding memory footprint. For example, in computer vision it is very common to store multiple images with different shapes or videos have dynamic length. To support the flexibility, minor overhead is introduced in the form of previously discussed chunk encoder that in practice we haven’t observed impact on production workloads. 7.2 Dataloader Deep Lake achieves state-of-the-art results in local and remote settings, as seen in benchmarks for iterating on large images Fig. 7. Primarily, it has been faster than FFCV [39], which claimed a reduction of ImageNet model training up to 98 cents per model training. Furthermore, Deep Lake achieves similar ingestion performance to WebDataset [19]. Deep Lake significantly outperforms on larger images. Parquet is optimized for small cells and analytical workloads, while Deep Lake is optimized for large, dynamically shaped tensorial data. Compared to other data lake solutions, its minimal python package design enables Deep Lake to be easily integrated into large-scale distributed training or inference workloads. 7.3 Future work The current implementation of Deep Lake has opportunities for further improvement. Firstly, the storage format does not support custom ordering for an even more efficient storage layout required for vector search or key-value indexing. Secondly, Deep Lake implements branch-based locks for concurrent access. Similar to Delta ACID transaction model [27], Deep Lake can be extended to highlyperformant parallel workloads. Thirdly, the current implementation of TQL only supports a subset of SQL operations (i.e., does not support operations such as join). Further work will focus on making it SQL-complete, extending to more numeric operations, running federated queries in external data sources and benchmarking against SQL engines. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 7. DISCUSSION AND LIMITATIONS Deep Lake’s primary use cases include (a) Deep Learning Model Training, (b) Data Lineage and Version Control, (c) Data Querying, and Analytics, (d) Data Inspection and Quality Control. We took NumPy [55] arrays as a fundamental block and implemented version control, streaming dataloaders, visualization engine from scratch. 7.1 Format Design Space The Tensor Storage Format (TSF) is a binary file format designed specifically for storing tensors, which are multi-dimensional arrays of numerical values used in many machine learning and deep learning algorithms. The TSF format is designed to be efficient and compact, allowing for fast and efficient storage and access of tensor data. One key advantage of the TSF format is that it supports a wide range of tensor data types, including dynamically shaped tensors. In comparison, the Parquet [79] and Arrow [13] formats are columnar file formats that are designed for storing and processing large analytical datasets. Unlike TSF, which is specifically designed for tensor data, Parquet and Arrow are optimized for efficient storage and querying of analytical workloads on tabular and time-series data. They use columnar storage and compression techniques to minimize storage space and improve performance, making them suitable for big data applications. However, TSF has some advantages over Parquet and Arrow when it comes to tensor data. TSF can support tensor operations and efficient streaming to deep learning frameworks. Other tensor formats [18, 52, 23, 57] are efficient for massively massively parallelizable workloads as they don’t require coordination across chunks. Tensor Storage Format key trade-off is enabling to store dynamically shape arrays inside a tensor without padding memory footprint. For example, in computer vision it is very common to store multiple images with different shapes or videos have dynamic length. To support the flexibility, minor overhead is introduced in the form of previously discussed chunk encoder that in practice we haven’t observed impact on production workloads. 7.2 Dataloader Deep Lake achieves state-of-the-art results in local and remote settings, as seen in benchmarks for iterating on large images Fig. 7. Primarily, it has been faster than FFCV [39], which claimed a reduction of ImageNet model training up to 98 cents per model training. Furthermore, Deep Lake achieves similar ingestion performance to WebDataset [19]. Deep Lake significantly outperforms on larger images. Parquet is optimized for small cells and analytical workloads, while Deep Lake is optimized for large, dynamically shaped tensorial data. Compared to other data lake solutions, its minimal python package design enables Deep Lake to be easily integrated into large-scale distributed training or inference workloads. 7.3 Future work The current implementation of Deep Lake has opportunities for further improvement. Firstly, the storage format does not support custom ordering for an even more efficient storage layout required for vector search or key-value indexing. Secondly, Deep Lake implements branch-based locks for concurrent access. Similar to Delta ACID transaction model [27], Deep Lake can be extended to highlyperformant parallel workloads. Thirdly, the current implementation of TQL only supports a subset of SQL operations (i.e., does not support operations such as join). Further work will focus on making it SQL-complete, extending to more numeric operations, running federated queries in external data sources and benchmarking against SQL engines. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Discussion and Limitations

গভীর লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: আলোচনা এবং সীমাবদ্ধতা

Deep Lake, ein Lakehouse für Deep Learning: Diskussion und Einschränkungen

Deep Lake, una casa de lago para el aprendizaje profundo: discusión y limitaciones

Deep Lake, un Lakehouse pour le Deep Learning : discussion et limites

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: चर्चा और सीमाएँ

ディープラーニングのためのレイクハウス「Deep Lake」：議論と限界

딥 러닝을 위한 레이크하우스인 Deep Lake: 논의와 한계

Deep Lake, um Lakehouse para Deep Learning: Discussão e Limitações

Deep Lake, домик у озера для глубокого обучения: обсуждение и ограничения

Deep Lake, Derin Öğrenme için Bir Göl Evi: Tartışma ve Sınırlamalar

Deep Lake, Lakehouse cho Deep Learning: Thảo luận và hạn chế

Deep Lake，深度学习的 Lakehouse：讨论和局限性

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 2. CURRENT CHALLENGES In this section, we discuss the current and historical challenges of unstructured or complex data management. 2.1 Complex Data Types in a Databases It is generally not recommended to store binary data, such as images, directly in a database. This is because databases are not optimized for storing and serving large files and can cause performance issues. In addition, binary data does not fit well into a database’s structured format, making it difficult to query and manipulate. This can lead to slow load times for users. Databases are typically more expensive to operate and maintain than other types of storage, such as file systems or cloud storage services. Therefore, storing large amounts of binary data in a database can be more costly than other storage solutions. 2.2 Complex Data Along with Tabular Formats Increases in large-scale analytical and BI workloads motivated the development of compressed structured formats like Parquet, ORC, Avro, or transient in-memory formats like Arrow [79, 6, 20, 13]. As tabular formats gained adoption, attempts to extend those formats, such as Petastorm [18] or Feather [7] for deep learning, have emerged. To the best of our knowledge, these formats have yet to gain wide adoption. This approach primarily benefits from native integrations with Modern Data Stack (MDS). However, as discussed previously, upstream tools require fundamental modifications to adapt to deep learning applications. 2.3 Object Storage for Deep Learning The current cloud-native choice for storing large unstructured datasets is object storage such as AWS S3 [1], Google Cloud Storage (GCS) [3], or MinIO [17]. Object storage does offer three main benefits over distributed network file systems. They are (a) cost-efficient, (b) scalable, and (c) serve as a format-agnostic repository. However, cloud storages are not without drawbacks. Firstly, they introduce significant latency overhead, especially when iterating over many small files such as text or JSON. Next, unstructured data ingestion without metadata control can produce "data swamps". Furthermore, object storage has built-in version control; it is rarely used in data science workflows. Lastly, data on object storage gets copied to a virtual machine before training, thus resulting in storage overhead and additional costs. 2.4 Second Generation of Data Lakes The second-generation data lakes led by Delta, Iceberg, Hudi [27, 15, 10] extend object storage by managing tabular format files with the following primary properties. (1) Update operations: inserting or deleting a row on top of a tabular format file. (2) Streaming: downstream data ingestion with ACID properties and upstream integration with query engine exposing SQL interface. (3) Schema evolution: evolving columnar structure while preserving backward compatibility. (4) Time travel and audit log trailing: preserving historical state with rollback property where queries can be reproducible. Also, support for row-level control on data lineage. (5) Layout optimization: Built-in feature to optimize file sizes and data compaction with custom ordering support. Significantly speeds up querying. However, second-generation data lakes are still bound by the limitations of the inherent data formats to be used in deep learning, as previously discussed in section 2.2. Hence in this paper, we extend the second generation of data lake capabilities for deep learning use cases by rethinking the format and upstream features, including querying, visualization, and native integration to deep learning frameworks to complete the ML lifecycle as shown in Fig. 2. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 2. CURRENT CHALLENGES In this section, we discuss the current and historical challenges of unstructured or complex data management. 2.1 Complex Data Types in a Databases It is generally not recommended to store binary data, such as images, directly in a database. This is because databases are not optimized for storing and serving large files and can cause performance issues. In addition, binary data does not fit well into a database’s structured format, making it difficult to query and manipulate. This can lead to slow load times for users. Databases are typically more expensive to operate and maintain than other types of storage, such as file systems or cloud storage services. Therefore, storing large amounts of binary data in a database can be more costly than other storage solutions. 2.2 Complex Data Along with Tabular Formats Increases in large-scale analytical and BI workloads motivated the development of compressed structured formats like Parquet, ORC, Avro, or transient in-memory formats like Arrow [79, 6, 20, 13]. As tabular formats gained adoption, attempts to extend those formats, such as Petastorm [18] or Feather [7] for deep learning, have emerged. To the best of our knowledge, these formats have yet to gain wide adoption. This approach primarily benefits from native integrations with Modern Data Stack (MDS). However, as discussed previously, upstream tools require fundamental modifications to adapt to deep learning applications. 2.3 Object Storage for Deep Learning The current cloud-native choice for storing large unstructured datasets is object storage such as AWS S3 [1], Google Cloud Storage (GCS) [3], or MinIO [17]. Object storage does offer three main benefits over distributed network file systems. They are (a) cost-efficient, (b) scalable, and (c) serve as a format-agnostic repository. However, cloud storages are not without drawbacks. Firstly, they introduce significant latency overhead, especially when iterating over many small files such as text or JSON. Next, unstructured data ingestion without metadata control can produce "data swamps". Furthermore, object storage has built-in version control; it is rarely used in data science workflows. Lastly, data on object storage gets copied to a virtual machine before training, thus resulting in storage overhead and additional costs. 2.4 Second Generation of Data Lakes The second-generation data lakes led by Delta, Iceberg, Hudi [27, 15, 10] extend object storage by managing tabular format files with the following primary properties. (1) Update operations: inserting or deleting a row on top of a tabular format file. Update operations: (2) Streaming : downstream data ingestion with ACID properties and upstream integration with query engine exposing SQL interface. Streaming (3) Schema evolution: evolving columnar structure while preserving backward compatibility. Schema evolution: (4) Time travel and audit log trailing: preserving historical state with rollback property where queries can be reproducible. Also, support for row-level control on data lineage. Time travel and audit log trailing: (5) Layout optimization: Built-in feature to optimize file sizes and data compaction with custom ordering support. Significantly speeds up querying. Layout optimization: However, second-generation data lakes are still bound by the limitations of the inherent data formats to be used in deep learning, as previously discussed in section 2.2. Hence in this paper, we extend the second generation of data lake capabilities for deep learning use cases by rethinking the format and upstream features, including querying, visualization, and native integration to deep learning frameworks to complete the ML lifecycle as shown in Fig. 2. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Current Challenges

গভীর লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: বর্তমান চ্যালেঞ্জ

Deep Lake, ein Lakehouse für Deep Learning: Aktuelle Herausforderungen

Deep Lake, un lago para el aprendizaje profundo: desafíos actuales

Deep Lake, un Lakehouse pour le Deep Learning : les défis actuels

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: वर्तमान चुनौतियाँ

ディープラーニングのためのレイクハウス「Deep Lake」：現在の課題

딥 러닝을 위한 레이크하우스인 Deep Lake: 현재의 과제

Deep Lake, um Lakehouse para Deep Learning: desafios atuais

Deep Lake, домик у озера для глубокого обучения: текущие проблемы

Deep Lake, Derin Öğrenme için Bir Göl Evi: Güncel Zorluklar

Deep Lake, Lakehouse cho Deep Learning: Những thách thức hiện tại

Deep Lake，深度学习的 Lakehouse：当前的挑战

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 3. TENSOR STORAGE FORMAT Deep Lake datasets follow columnar storage architecture, with tensors as columns, as shown in Fig. 3. Each tensor is a collection of chunks - binary blobs that contain the data samples. An index map associated with each tensor helps find the right chunk and index of the sample within that chunk for a given sample index. 3.1 Dataset A sample in a dataset represents a single row indexed across parallel tensors. As opposed to a document storage format, sample elements are logically independent, which enables partial access to samples for running performant queries or streaming selected tensors over the network to the GPU training instances. Multiple tensors can be grouped. Groups implement syntactic nesting and define how tensors are related to each other. Syntactic nesting avoids the format complication for hierarchical memory layout. Changes to the dataset’s schema are also tracked over time with version control, similar to dataset content changes. 3.2 Tensors Tensors are typed and can be appended or modified in-place. Default access to an index or a set of indices returns the data as NumPy arrays [55]. Instead of storing 1-D data as seen in Parquet [79] or series in Arrow [13], tensors can accommodate n-dimensional data, where typically the first dimension corresponds to the index or batch dimension. Tensors can contain dynamically shaped arrays, also called ragged tensors, as opposed to other statically chunked array formats such as Zarr [52]. 3.3 Types Htype defines the expectations on samples in a tensor such as data type (dtype as seen in NumPy [55]), shape, number of dimensions, or compression. Typed tensors make interacting with deep learning frameworks straightforward and enable sanity checks and efficient memory layout. By inheriting from a generic tensor htype, we can construct types such as image, video, audio, bbox, dicom, and others. For example, a tensor with image htype would expect samples being appended to it to have dtype as uint8 and shape length 3 (i.e. width, height and number of channels). We further expand on the notion of htypes allowing for meta types that support storing image sequences in tensors (sequence[image]), referencing to remotely stored images, while maintaining the regular behavior of a image tensor (link[image]), or even possible cross-format support. 3.4 Memory Layout A Deep Lake dataset contains a provenance file in JSON format and folders per tensor. A tensor contains chunks, chunk encoder, tile encoder, and tensor metadata. Tensors can be optionally hidden. For instance, hidden tensors can be used to maintain down-sampled versions of images or preserve shape information for fast queries. Tensors are stored in chunks at the storage level. While statically (inferred) shaped chunking avoids maintaining a chunk map table, it introduces significant user overhead during the specification of the tensor, custom compression usage limitations, underutilized storage for dynamically shaped tensors, and post-processing inefficiencies. Deep Lake chunks are constructed based on the lower and upper bound of the chunk size to fit a limited number of samples. This comes with a trade-off of having a compressed index map that preserves the sample index to chunk id mapping per tensor while enabling chunk sizes in the range optimal for streaming while accommodating mixed-shape samples. One could consider the approach taken in this paper as an optimized trade-off between file system page map and compute-defined map-less array storage system. For practical reasons, a single chunk encoder can be scaled to billions of images while maintaining a 150MB chunk encoder per 1PB tensor data. Further scaling can be introduced by sharding the chunk encoder. Chunks contain header information such as byte ranges, shapes of the samples, and the sample data itself. If a sample is larger than the upper bound chunk size, which is the case for large aerial or microscopy images, the sample is tiled into chunks across spatial dimensions. The only exception to tiling is videos. Videos are preserved due to efficient frame mapping to indices, key-frame-only decompression, and range-based requests while streaming. 3.5 Access Patterns The tensor storage format is optimized for deep learning training and inference, including sequential and random access. Sequential access is used for running scan queries, transforming tensors into other tensors, or running inference. Random access use cases include multiple annotators writing labels to the same image or models storing back predictions along with the dataset. While the strict mode is disabled, out-of-the-bounds indices of a tensor can be assigned, thus accommodating sparse tensors. However, random assignment over time will produce inefficiently stored data chunks. To fix the data layout, we implement an on-the-fly re-chunking algorithm to optimize the data layout. One of the key access patterns of Deep Lake is shuffled stream access for training machine learning models. It requires random or custom order access while streaming chunks into the training process. This is achieved by involving range-based requests to access sub-elements inside chunks, running complex queries before training to determine the order, and maintaining a buffer cache of fetched and unutilized data. This avoids having a separate compute cluster for running shuffling algorithm [50]. Each tensor has its own chunks, and the default chunk size is 8MB. A single chunk consists of data from multiple indices when the individual data points (image, label, annotation, etc.) are smaller than the chunk size. Conversely, when individual data points are larger than the chunk size, the data is split among multiple chunks (tiling). Exceptions to chunking logic are video data. Deep Lake format is optimized for maximizing throughput to GPU processing. It includes CPU pre-fetching, decompression or decoding, transformations, and GPU memory transfer in a deep learning framework’s expected layout. 3.6 Storage Providers Deep Lake can be plugged into any storage provider, including object storages such as AWS S3 [1], Google Cloud Storage (GCS) [3], POSIX compatible file systems, or local in-memory storage. Moreover, it constructs memory caching by chaining various storage providers together, for instance - the Least Recently Used (LRU) cache of remote S3 storage with local in-memory data. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 3. TENSOR STORAGE FORMAT Deep Lake datasets follow columnar storage architecture, with tensors as columns, as shown in Fig. 3. Each tensor is a collection of chunks - binary blobs that contain the data samples. An index map associated with each tensor helps find the right chunk and index of the sample within that chunk for a given sample index. 3.1 Dataset A sample in a dataset represents a single row indexed across parallel tensors. As opposed to a document storage format, sample elements are logically independent, which enables partial access to samples for running performant queries or streaming selected tensors over the network to the GPU training instances. Multiple tensors can be grouped. Groups implement syntactic nesting and define how tensors are related to each other. Syntactic nesting avoids the format complication for hierarchical memory layout. Changes to the dataset’s schema are also tracked over time with version control, similar to dataset content changes. 3.2 Tensors Tensors are typed and can be appended or modified in-place. Default access to an index or a set of indices returns the data as NumPy arrays [55]. Instead of storing 1-D data as seen in Parquet [79] or series in Arrow [13], tensors can accommodate n-dimensional data, where typically the first dimension corresponds to the index or batch dimension. Tensors can contain dynamically shaped arrays, also called ragged tensors, as opposed to other statically chunked array formats such as Zarr [52]. 3.3 Types Htype defines the expectations on samples in a tensor such as data type (dtype as seen in NumPy [55]), shape, number of dimensions, or compression. Typed tensors make interacting with deep learning frameworks straightforward and enable sanity checks and efficient memory layout. By inheriting from a generic tensor htype, we can construct types such as image, video, audio, bbox, dicom, and others. For example, a tensor with image htype would expect samples being appended to it to have dtype as uint8 and shape length 3 (i.e. width, height and number of channels). We further expand on the notion of htypes allowing for meta types that support storing image sequences in tensors (sequence[image]), referencing to remotely stored images, while maintaining the regular behavior of a image tensor (link[image]), or even possible cross-format support. 3.4 Memory Layout A Deep Lake dataset contains a provenance file in JSON format and folders per tensor. A tensor contains chunks, chunk encoder, tile encoder, and tensor metadata. Tensors can be optionally hidden. For instance, hidden tensors can be used to maintain down-sampled versions of images or preserve shape information for fast queries. Tensors are stored in chunks at the storage level. While statically (inferred) shaped chunking avoids maintaining a chunk map table, it introduces significant user overhead during the specification of the tensor, custom compression usage limitations, underutilized storage for dynamically shaped tensors, and post-processing inefficiencies. Deep Lake chunks are constructed based on the lower and upper bound of the chunk size to fit a limited number of samples. This comes with a trade-off of having a compressed index map that preserves the sample index to chunk id mapping per tensor while enabling chunk sizes in the range optimal for streaming while accommodating mixed-shape samples. One could consider the approach taken in this paper as an optimized trade-off between file system page map and compute-defined map-less array storage system. For practical reasons, a single chunk encoder can be scaled to billions of images while maintaining a 150MB chunk encoder per 1PB tensor data. Further scaling can be introduced by sharding the chunk encoder. Chunks contain header information such as byte ranges, shapes of the samples, and the sample data itself. If a sample is larger than the upper bound chunk size, which is the case for large aerial or microscopy images, the sample is tiled into chunks across spatial dimensions. The only exception to tiling is videos. Videos are preserved due to efficient frame mapping to indices, key-frame-only decompression, and range-based requests while streaming. 3.5 Access Patterns The tensor storage format is optimized for deep learning training and inference, including sequential and random access. Sequential access is used for running scan queries, transforming tensors into other tensors, or running inference. Random access use cases include multiple annotators writing labels to the same image or models storing back predictions along with the dataset. While the strict mode is disabled, out-of-the-bounds indices of a tensor can be assigned, thus accommodating sparse tensors. However, random assignment over time will produce inefficiently stored data chunks. To fix the data layout, we implement an on-the-fly re-chunking algorithm to optimize the data layout. One of the key access patterns of Deep Lake is shuffled stream access for training machine learning models. It requires random or custom order access while streaming chunks into the training process. This is achieved by involving range-based requests to access sub-elements inside chunks, running complex queries before training to determine the order, and maintaining a buffer cache of fetched and unutilized data. This avoids having a separate compute cluster for running shuffling algorithm [50]. Each tensor has its own chunks, and the default chunk size is 8MB. A single chunk consists of data from multiple indices when the individual data points (image, label, annotation, etc.) are smaller than the chunk size. Conversely, when individual data points are larger than the chunk size, the data is split among multiple chunks (tiling). Exceptions to chunking logic are video data. Deep Lake format is optimized for maximizing throughput to GPU processing. It includes CPU pre-fetching, decompression or decoding, transformations, and GPU memory transfer in a deep learning framework’s expected layout. 3.6 Storage Providers Deep Lake can be plugged into any storage provider, including object storages such as AWS S3 [1], Google Cloud Storage (GCS) [3], POSIX compatible file systems, or local in-memory storage. Moreover, it constructs memory caching by chaining various storage providers together, for instance - the Least Recently Used (LRU) cache of remote S3 storage with local in-memory data. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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