Modernizing the hotel technology stack with cloud-based solutions to improve antiquated workflows

Canary Technologies is a leader in hospitality software solutions, and is trusted by thousands of hotels worldwide. Canary's suite of solutions secure guest data, and enhance the guest experience. Canary's solutions include industry-leading Contactless Mobile Check-in, Digital Authorizations, Digital Contracts, and Digital Amenity Ordering. Canary's solutions help ensure PCI compliance and reduce chargebacks/fraud by up to 90%. Our team includes former hoteliers who understand the nuances of what goes on behind the scenes at the property and truly recognize the needs of hoteliers. Canary's solutions are trusted by tens of thousands of hoteliers worldwide.

Canary Technologies

Introduction Kusama is a decentralized network of nodes that can be used to build and run decentralized applications (dApps). Kusama uses the same system architecture as Polkadot, which makes it a suitable testing ground for new features and functionality before they are integrated into the main network. Kusama is designed to be a platform for experimentation, allowing developers to try out new ideas in a live environment. Kusama is still in development, it is subject to change and may not be fully stable. What is Kusama? Kusama is a blockchain platform that enables developers to build and launch decentralized applications. The Kusama network uses the same technology as Polkadot, which makes it easy for developers to get started. Kusama also has its own native currency, called KSM, which can be used to purchase goods and services on the platform or traded on cryptocurrency exchanges. As a playground for developers, Kusama offers a safe environment in which they can experiment with new ideas and test their applications. Key features of Kusama Kusama is a decentralized network designed for canary releases, rapid development cycles, and experimentation for Polkadot. Kusama uses Substrate, an open-source framework for building blockchain applications, to create a platform that is censorship-resistant and scalable. Changes to the network must be approved by a two-thirds supermajority of the validators in order to be implemented. The native token of Kusama is called KSM and it is used for staking, paying transaction fees, and voting on governance proposals. Who are the developers behind Kusama? Kusama is a decentralized network designed to incentivize early adoption of Polkadot and its ecosystem. Kusama is owned by the Web3 Foundation, a Swiss non-profit foundation that promotes decentralized technologies. The Foundation provides grants and funding to support the development of the Kusama network and ecosystem projects. Kusama also has a governance model that allows for community involvement in decision-making processes. The native currency of the Kusama network is KSM, which is used for staking and transaction fees. Why Kusama is important for the innovation of the Polkadot ecosystem? Kusama is a Polkadot-based canary network that allows developers to experiment with new features and scalability solutions before they are implemented on Polkadot. This helps to ensure that the platform remains scalable and reliable and that the user experience is consistent across all devices. Kusama is also known as the because it's a place where development can happen more quickly and with fewer constraints than on mainnet. This makes Kusama ideal for testing new ideas and solutions before they are implemented on Polkadot's mainnet. "wilderness" or "parachain testnet" Anyone can join in on the fun by using Kusama parachains to access its unique features without having to be a developer or know about blockchain technology. Simply sign up, stake your tokens, and get started! To get started, check out where I explain how to create a Polkadot account. Making a Kusama account is the same as making a account. Once populated one can easily toggle any KSM or DOT parachain. Once set up a user can seamlessly, without leaving one central control board, utilize the whole ecosystem. my other article on Polkadot Polkadot Also published . here

What Is Kusama and Why Is It the Canary Network to Polkadot?

Heavy is the head that wears the crown during a Wall Street bull run, and Nvidia’s status as the king of the ongoing generative AI boom means that the stock can be especially vulnerable to the prospect of weakening investor appetite. Nvidia (NASDAQ:NVDA) became Wall Street’s most exciting story in 2023. The chipmaker rallied some 239% throughout the year, smashing through the $1 trillion market capitalization glass ceiling before peaking at a cap in excess of $2tn. The optimism, fuelled by the ongoing generative AI market rally, continued to make waves in the first-quarter of 2024. NVDA rallied almost 90% in the opening three months of the year to continue its strong spell of dominance. But finally, at the beginning of April, the stock took its biggest tumble in four years. The wider implications of the Nvidia sell-off are likely to shake up the entire generative AI market. Counting the Cost of NVDA Sell-Offs The first three weeks of April 2024 saw NVDA shed more than 15% of its value amid widespread investor sell-offs. Although greater degrees of volatility can be expected for stocks caught up in the hype phase of an emerging market, Nvidia’s April downturn has been the company’s biggest dip since the initial outbreak of the Covid-19 pandemic in March 2020. For Nvidia, the pandemic era must seem like a lifetime ago. Its March 2020 sell-offs saw the stock tumble to $51.44. The stock’s peak closing price in March 2024 was $950.02. But after four years of meteoric growth, Nvidia’s recent market downturn is particularly concerning–particularly now the stock’s gained the status as a tech pioneer that’s been driving the S&P 500 higher on a consistent basis. Nvidia’s position at the pinnacle of the generative AI boom could mean that the stock would always be the first to absorb the impact of a change in investor sentiment, and the fears are that NVDA would be the canary in the mine should we suddenly discover that generative AI has been in a hype bubble that’s ready to burst for some time. Although Nvidia’s recent difficulties have coincided with the news that US inflation rates have continued to defy expectations, placing the prospect of interest rate cuts on hold for the foreseeable future, analysts suggest that fears over a cooling generative AI boom remain the key cause of investor concern in the wake of the underperformance. “The stock pullback has very little to do with rates,” explained Parag Thatte, strategist at Deutsche Bank, in a recent Financial Times interview. “It’s more to do with investors pricing in slower earnings growth.” There’s also a growing concern that generative AI has begun to transform from its hype phase towards the question of implementation. While Large Language Models (LLMs) like ChatGPT have been excellent tools in showcasing the sheer power and potential of generative AI, they’ve also been largely error-prone in a manner that could cause more hesitancy among enterprises seeking to embrace the technology. It’s for this reason that Gil Luria, analyst at DA Davidson, warned that a 20% slide in Nvidia stock could take place by the end of 2024. Luria suggests that the implementation phase of Nvidia’s products, which are currently being bought en masse by the likes of Microsoft and Amazon, could run into trouble when key buyers reach capacity and demand begins to fall. However, other analysts suggest that the end of the generative AI hype phase will be met by a more strategic implementation phase which could keep the industry growing. “Concerns about GenAI failing to meet expectations in 2024 should be seen in the context of the natural cycle of hype and maturity of new technologies,” said Maxim Manturov, head of investment research at Freedom Finance Europe. “While some industry leaders have expressed frustration with the speed of progress in artificial intelligence and GenAI in their organisations, others remain optimistic about its potential.” “Companies heavily invested in GenAI are under pressure to prove profitability, and industry reports indicate a shift to more strategic AI initiatives in the coming years,” Manturov added. Is the Generative AI an Exceptional Case? Here, it’s worth reminding ourselves that we’re talking about the generative AI boom. This is an industry that Bloomberg Intelligence last year suggested would grow at a CAGR of 42% into a $1.3 trillion industry by 2032. Fresh demand for generative AI products was predicted to add around $280 million in new software revenue. Could we be talking about an emerging technology that’s market impact is without precedent? Morgan Stanley analysts have underlined the indecisive outlook for Nvidia and the wider generative AI market in Q2 2024 by proposing a more bullish outlook for NVDA as well as the generative AI-focused Amazon (AMZN). As Nvidia reported Q4 2023 revenue increases of 265% to $22 billion, CEO Jensen Huang claimed that "accelerated computing and generative AI have hit the tipping point," before adding that "demand is surging worldwide across companies, industries and nations." The company’s innovations in recent weeks have included the text-to-3D tool, LATTE3D, which Nvidia described as a ‘virtual 3D printer’, as well as a collaboration with Hipocratic AI to develop a network of generative AI nurses that could cost $9 an hour. Nvidia’s quick 5% rebound following its record sell-offs should serve as a wider vote of confidence in the innovation that the company is capable of. Regardless of whether its success has made it the GenAI canary, NVDA is certainly an innovative stock that’s capable of sustaining its impressive growth. Bubble or Boom? Ultimately, Nvidia’s performance over the past 12 months means that the stock has become a live litmus test for investor sentiment in generative AI. Whether that’s a good thing or not depends wholly on the capabilities of the industry itself. Nvidia’s rapid growth has seen the stock rub shoulders with the likes of Microsoft, Apple, and Alphabet, and the company will be keen to follow the lead of other industry giants to continue sustaining its growth through innovation. As a semiconductor leader, the possibilities are nearly endless for Nvidia. As long as the capabilities of generative AI continue to astound clients and deliver on its promises, NVDA remains a stock that can continue to grow. If investors get a whiff of saturation or inefficiency, the Nvidia litmus test could face some further challenges ahead. Heavy is the head that wears the crown during a Wall Street bull run, and Nvidia’s status as the king of the ongoing generative AI boom means that the stock can be especially vulnerable to the prospect of weakening investor appetite. Nvidia (NASDAQ:NVDA) became Wall Street’s most exciting story in 2023. The chipmaker rallied some 239% throughout the year, smashing through the $1 trillion market capitalization glass ceiling before peaking at a cap in excess of $2tn. Nvidia The optimism, fuelled by the ongoing generative AI market rally, continued to make waves in the first-quarter of 2024. NVDA rallied almost 90% in the opening three months of the year to continue its strong spell of dominance. But finally, at the beginning of April, the stock took its biggest tumble in four years. The wider implications of the Nvidia sell-off are likely to shake up the entire generative AI market. Counting the Cost of NVDA Sell-Offs The first three weeks of April 2024 saw NVDA shed more than 15% of its value amid widespread investor sell-offs. Although greater degrees of volatility can be expected for stocks caught up in the hype phase of an emerging market, Nvidia’s April downturn has been the company’s biggest dip since the initial outbreak of the Covid-19 pandemic in March 2020. For Nvidia, the pandemic era must seem like a lifetime ago. Its March 2020 sell-offs saw the stock tumble to $51.44. The stock’s peak closing price in March 2024 was $950.02. But after four years of meteoric growth, Nvidia’s recent market downturn is particularly concerning–particularly now the stock’s gained the status as a tech pioneer that’s been driving the S&P 500 higher on a consistent basis. Nvidia’s position at the pinnacle of the generative AI boom could mean that the stock would always be the first to absorb the impact of a change in investor sentiment, and the fears are that NVDA would be the canary in the mine should we suddenly discover that generative AI has been in a hype bubble that’s ready to burst for some time. Although Nvidia’s recent difficulties have coincided with the news that US inflation rates have continued to defy expectations, placing the prospect of interest rate cuts on hold for the foreseeable future, analysts suggest that fears over a cooling generative AI boom remain the key cause of investor concern in the wake of the underperformance. prospect of interest rate cuts on hold prospect of interest rate cuts on hold “The stock pullback has very little to do with rates,” explained Parag Thatte , strategist at Deutsche Bank, in a recent Financial Times interview. “It’s more to do with investors pricing in slower earnings growth.” explained Parag Thatte explained Parag Thatte There’s also a growing concern that generative AI has begun to transform from its hype phase towards the question of implementation. While Large Language Models (LLMs) like ChatGPT have been excellent tools in showcasing the sheer power and potential of generative AI, they’ve also been largely error-prone in a manner that could cause more hesitancy among enterprises seeking to embrace the technology. largely error-prone largely error-prone It’s for this reason that Gil Luria, analyst at DA Davidson, warned that a 20% slide in Nvidia stock could take place by the end of 2024. Luria suggests that the implementation phase of Nvidia’s products, which are currently being bought en masse by the likes of Microsoft and Amazon, could run into trouble when key buyers reach capacity and demand begins to fall. 20% slide in Nvidia stock 20% slide in Nvidia stock However, other analysts suggest that the end of the generative AI hype phase will be met by a more strategic implementation phase which could keep the industry growing. “Concerns about GenAI failing to meet expectations in 2024 should be seen in the context of the natural cycle of hype and maturity of new technologies,” said Maxim Manturov, head of investment research at Freedom Finance Europe . “While some industry leaders have expressed frustration with the speed of progress in artificial intelligence and GenAI in their organisations, others remain optimistic about its potential.” Freedom Finance Europe Freedom Finance Europe “Companies heavily invested in GenAI are under pressure to prove profitability, and industry reports indicate a shift to more strategic AI initiatives in the coming years,” Manturov added. Is the Generative AI an Exceptional Case? Here, it’s worth reminding ourselves that we’re talking about the generative AI boom. This is an industry that Bloomberg Intelligence last year suggested would grow at a CAGR of 42% into a $1.3 trillion industry by 2032 . $1.3 trillion industry by 2032 $1.3 trillion industry by 2032 Fresh demand for generative AI products was predicted to add around $280 million in new software revenue. Could we be talking about an emerging technology that’s market impact is without precedent? Morgan Stanley analysts have underlined the indecisive outlook for Nvidia and the wider generative AI market in Q2 2024 by proposing a more bullish outlook for NVDA as well as the generative AI-focused Amazon (AMZN). more bullish outlook more bullish outlook As Nvidia reported Q4 2023 revenue increases of 265% to $22 billion, CEO Jensen Huang claimed that "accelerated computing and generative AI have hit the tipping point," before adding that "demand is surging worldwide across companies, industries and nations." claimed that claimed that The company’s innovations in recent weeks have included the text-to-3D tool , LATTE3D, which Nvidia described as a ‘virtual 3D printer’, as well as a collaboration with Hipocratic AI to develop a network of generative AI nurses that could cost $9 an hour. text-to-3D tool text-to-3D tool collaboration with Hipocratic AI collaboration with Hipocratic AI Nvidia’s quick 5% rebound following its record sell-offs should serve as a wider vote of confidence in the innovation that the company is capable of. Regardless of whether its success has made it the GenAI canary, NVDA is certainly an innovative stock that’s capable of sustaining its impressive growth. Bubble or Boom? Ultimately, Nvidia’s performance over the past 12 months means that the stock has become a live litmus test for investor sentiment in generative AI. Whether that’s a good thing or not depends wholly on the capabilities of the industry itself. Nvidia’s rapid growth has seen the stock rub shoulders with the likes of Microsoft, Apple, and Alphabet, and the company will be keen to follow the lead of other industry giants to continue sustaining its growth through innovation. As a semiconductor leader, the possibilities are nearly endless for Nvidia. As long as the capabilities of generative AI continue to astound clients and deliver on its promises, NVDA remains a stock that can continue to grow. If investors get a whiff of saturation or inefficiency, the Nvidia litmus test could face some further challenges ahead.

Nvidia’s Shaky Q2 Underlines the Stock as Generative AI’s Sentiment Litmus Test

এনভিডিয়ার শ্যাকি Q2 জেনারেটিভ এআই এর সেন্টিমেন্ট লিটমাস টেস্ট হিসাবে স্টককে আন্ডারলাইন করেছে

Nvidias wackeliges zweites Quartal unterstreicht die Aktie als Lackmustest für die Stimmung von Generative AI

El inestable segundo trimestre de Nvidia subraya la acción como prueba de fuego del sentimiento de la IA generativa

Le Shaky Q2 de Nvidia souligne le titre en tant que test décisif du sentiment de Generative AI

एनवीडिया की अस्थिर Q2 स्टॉक को जेनरेटिव एआई के सेंटीमेंट लिटमस टेस्ट के रूप में रेखांकित करती है

エヌビディアの第2四半期の不安定さは、ジェネレーティブAIの感情リトマス試験として株価を浮き彫りにする

Nvidia의 Shaky Q2는 Generative AI의 감정 리트머스 테스트로서 주식을 강조합니다.

Shaky Q2 da Nvidia destaca o estoque como teste decisivo de sentimento da IA generativa

Нестабильный второй квартал Nvidia подчеркивает, что акции являются лакмусовой бумажкой настроений генеративного ИИ

Nvidia'nın Titrek 2. Çeyreği Hisse Senedinin Üretken Yapay Zekanın Duyarlılık Turnusol Testi Olduğunu Vurguluyor

Q2 run rẩy của Nvidia nhấn mạnh cổ phiếu là bài kiểm tra tình cảm của AI sáng tạo

Nvidia 第二季度业绩表现不佳，凸显其股票是生成式 AI 情绪的试金石

266 Stories To Learn About Product Development

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 2. OBSERVATIONS 2.1. A Candidate in Archival K2 Data EPIC 249631677 was observed by K2 in Campaign 15 from 2017-08-23 22:18:11 UTC to 2017-11-19 22:58:27 UTC continuously for about 90 days as part of program GO 15005 (PI: I. Crossfield). The pointing was maintained by using two functioning reaction wheels, while the telescope drifted slowly in the third axis due to radiation pressure from the Sun, which was corrected periodically by thruster firing (Howell et al. 2014). As a consequence of such a modus operandi, uncorrected K2 light curves can show saw-tooth structures. Many pipelines have been built to correct for such systematics. Two popular detrending algorithms for K2 light curves are K2SFF (Vanderburg & Johnson 2014) and everest (Luger et al. 2016). These pipelines have helped to achieve precision comparable to that of Kepler by correcting for systematics caused by intrapixel and inter-pixel variations. In the case of EPIC 249631677b, the standard deviation of the flattened light curve for K2SFF was observed to be 1230 ppm, compared to 685 ppm for everest. Considering this, we use the light curve from the everest pipeline, available from the Mikulski Archive for Space Telescopes, throughout this analysis. We use a biweight filter with a window of 0.75 days, as implemented in w¯otan (Hippke et al. 2019), to generate the flattened light curve for further analysis, and use only data with quality factor of 0. This light curve can be seen in Figure 1. The simple aperture photometric light curve has a scatter of 2527 ppm, which improves to 685 ppm after everest processing. We searched the flattened data for periodic transit signals using the transit least squares algorithm (TLS) (Hippke & Heller 2019), and found a prominent peak around 3.14 days as can be seen in Figure 2. We assessed the presence of additional candidate signals after modeling out the 3.14-d signal by re-running TLS, but did not find any with a significant signal detection efficiency (i.e., SDE>10). 2.2. Candidate Vetting with SPECULOOS Telescopes We followed up on the planetary candidate by observing with SPECULOOS Southern Observatory (SSO) two transit windows on UT 25 February 2020 by Ganymede and on UT 18 March 2020 by Io, and one transit window with SPECULOOS Northern Observa-. tory on UT 18 May 2020 by Artemis. SSO is composed of four telescopes: Io, Europa, Ganymede and Callisto, which are installed at ESO Paranal Observatory (Chile) and have been operational since January 2018. SNO is currently composed of one telescope (Artemis), which is located at the Teide Observatory (Canary Islands, Spain) and operational since June 2019. All SPECULOOS telescopes are identical robotic Ritchey-Chretien (F/8) telescopes with an aperture of 1-m. They are equipped with Andor iKon-L cameras with e2v 2K × 2K deep-depletion CCDs, which provide a Field of View (FoV) of 12 0 × 12 0 and the corresponding pixel scale is 0.35 00 pixel−1 (Delrez et al. 2018; Jehin et al. 2018). To schedule those windows we used the SPeculoos Observatory sChedule maKer (SPOCK), described in Sebastian et al. (in prep.). Observations were made with an exposure time of 40 s in an I+z filter, a custom filter (transmittance >90% from 750 nm to beyond 1000 nm) designed for the observation of faint red targets usually observed by the SPECULOOS survey (Delrez et al. 2018; Murray et al. 2020). SSO data were then processed using the SSO Pipeline, which accounts for the water vapor effects known to be significant for differential photometry of redder hosts with bluer comparison stars (Murray et al. 2020). SNO data were processed using prose, a Python-based data reduction package, which generates light curves from raw images (Garcia et al. in prep.). It creates a stacked image to extract the positions of the stars in the field, and uses the positions to perform aperture annulus photometry. A differential light curve is produced using a weighted light curve derived from the field stars, while the instrumental systematics, such as pointing shift and full width half maximum, are recorded to assist later in detrending. We show detrended light curves from SPECULOOS in Figure 3, where we recovered the transit events within 1σ of the calculated ephemeris from K2 data. Since these observations were obtained two years after K2 Campaign 15, they improve the precision of the transit ephemeris by an order of magnitude.

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Observation

Remember the good old days (read: pre-pandemic) when scaling your app meant adding more servers, like piling bricks on a Jenga tower? Yeah, good times. But just like that shaky tower's inevitable collapse, managing infrastructure the old way meant unavoidable chaos. Enter , the superhero swooping in to save the day, transforming infrastructure into code and bringing order to the wild kingdom of deployments. GitOps Managing servers, configurations, and deployments was a tangled mess. We juggled spreadsheets, memorized cryptic commands, and prayed every update wouldn't trigger a digital apocalypse. GitOps changes the game by treating infrastructure like any other code – living in source control, versioned, and managed through familiar workflows: boom, instant peace of mind. As per Gene Kim, Founder of DevOps Enterprise Summit: GitOps is the natural evolution of DevOps, bringing infrastructure into the same world of version control and automation that revolutionized software development. It's the missing piece for truly continuous delivery and a key enabler for faster, more reliable deployments. So, How Does This Magic Work? GitOps is all about declarative configuration. Instead of manually configuring servers one by one, we define our desired state in a Git repository. Think of it like a blueprint for your infrastructure – you describe what you want, not how to get it. Then, become the magic paintbrushes, reading your blueprint and automatically translating it into reality. Boom, the infrastructure deployed (and updated!) like clockwork. CI/CD pipelines Term Definition Impact Declarative Configuration Describing what you want your infrastructure to do, not how. Think blueprints, not instruction manuals. Simplifies deployments, reduces errors, and enables easy version control. CI/CD Pipelines Automated workflows that transform Git changes into real-world infrastructure updates. Think paintbrushes bringing blueprints to life. Streamlines deployments, ensures consistency, and facilitates rapid iterations. Single Source of Truth One central repository (Git) containing all infrastructure states. Think the master blueprint, never scattered scraps. Eliminates configuration drift, enhances visibility, and simplifies troubleshooting. Reconciliation The process of comparing the desired state (Git) with the actual state (deployed infrastructure) and making adjustments. Think of it as the construction foreman ensuring everything matches the blueprint. Maintains infrastructure stability, catches configuration drifts, and automates corrective actions. Rollbacks The ability to easily revert to previous versions of your infrastructure. Think of it as an undo button for your digital world. Reduces risk, facilitates experimentation, and provides safety nets for deployments. But Wait, There's More! GitOps tools like ArgoCD and Flux act as the construction foremen, ensuring your infrastructure aligns with your blueprint. They continuously compare the desired state in your Git repository with the actual state of your resources, automatically making any necessary adjustments. It's like having a robot army constantly checking and double-checking, ensuring your infrastructure never strays from your vision. GitOps vs. Other Infrastructure Management Approaches Approach Pros Cons Manual Scripting Offers granular control and familiar tools. Error-prone, inconsistent, and difficult to scale and collaborate. Configuration Management Tools Automates deployments and centralizes configurations. Can be complex, vendor lock-in, limited flexibility, etc. Infrastructure as Code (IaC) Declarative approach, version control, reusable modules, etc. Requires coding expertise; can be challenging to debug complex configurations. GitOps Combines benefits of IaC with CI/CD pipelines, unified view, and a single source of truth. Higher learning curve and requires a deeper understanding of Git and tooling. And the Benefits? Oh, Where Do We Even Begin? No more surprise server meltdowns. GitOps provides a single source of truth for your infrastructure, minimizing manual errors and fragile configurations. Stability: Need to scale up? Down? Deploy a new feature? Just update your Git repository, and boom, your infrastructure magically adapts. Agility: Gone are the days of crossed fingers and nervous deployments. GitOps automates and standardizes the process, making consistent, reliable deployments a reality. Reliability: Developers and operations teams can finally work together in harmony, using the same tools and speaking the same language (code!). Collaboration: Need to know what's happening in your infrastructure? Just peek into your Git repository. Version history, rollbacks, everything's right there, transparent and organized. Visibility: Let's Be Honest! The world of software development is already complex enough. Wrestling with unruly infrastructure shouldn't be part of the package. GitOps is the key to unlocking a new level of control, agility, and peace of mind. It's about taking the chaos out of your deployments and letting your code do the talking (or rather, the building). So the opinion of Kelsey Hightower, DevOps Advocate at Microsoft, elaborates it more clearly: Flux's flexibility and multi-cloud support make it a compelling choice for organizations running hybrid or multi-cloud deployments. Its focus on security and compliance further strengthens its appeal for enterprise environments. Use Case Benefits Complexity Blue-Green Deployments Deploy updates to a new environment first, then seamlessly switch over, minimizing downtime. Higher initial setup and requires additional resources. Canary Deployments Gradually roll out updates to a small subset of users to detect and fix issues before broader release. Requires advanced monitoring and rollback strategy. Multi-Cloud and Hybrid Deployments Manage infrastructure across different cloud providers and on-premises environments. Can be complex depending on chosen tools and cloud platforms. Automated Security Compliance Integrate security scans and compliance checks into the CI/CD pipeline, ensuring a secure infrastructure. Requires specialized security tools and expertise. Infrastructure Testing Test infrastructure changes before deployment within the CI/CD pipeline, enhancing stability and reliability. Adds additional testing overhead, and requires specific testing frameworks. So, whether you're a seasoned DevOps warrior or a newbie developer still figuring out the difference between a server and a hamster wheel, consider embracing GitOps. It's not just a trend; it's a paradigm shift, a portal to a more efficient, reliable, and, dare I say, enjoyable development world. Go forth, explore the possibilities, and remember, with GitOps, your infrastructure is no longer a beast to tame but a powerful orchestra conducting your digital symphony. Don't Forget: Experiment with GitOps on a single application or environment before diving headfirst into your entire infrastructure. Start small: ArgoCD, Flux, Terraform – the GitOps ecosystem is vast. Pick tools that align with your existing workflows and needs. Choose the right tools: GitOps bridges the gap between developers and operations. Work together to define your infrastructure as code and reap the benefits together. Embrace collaboration: Wrapping It Up! By following these tips and embracing the power of GitOps, you can conquer the chaos, orchestrate your infrastructure with precision, and build a digital empire that stands the test of time. Now, go forth, and code with confidence!

GitOps: From Chaos to Control - Managing Infrastructure Like Code

PARIS, France, December 20th, 2023/Chainwire/--Layer 3 Foundation Announces Testnet of Its P2P Yellow Clearing Network for Counterparty Risk Mitigation. Layer 3 Foundation, a pioneer in blockchain-based financial technology, has announced the launch of a testnet program for its Yellow Clearing Network, the first-ever decentralized clearing system. This initiative marks a significant step towards realising the foundation's mission of decentralizing clearing and settlement processes to scale trading using blockchain technologies. Founded by a team of experts in fintech, software architecture, cyber-security, and market making, Layer 3 Foundation has been at the forefront of leveraging blockchain to create innovative financial solutions. The $YELLOW token, central to Yellow Clearing Network, is a testament to the foundation's commitment to cutting-edge technology and a decentralized future. What To Expect In The Testnet The program will begin with Yellow’s closed beta testnet which will go live on Duckies, Yellow’s Canary Network, which utilizes $DUCKIES as the native token. The closed testnet will open on January 22nd, 2024, and continue till April of the same year and focus on testing key features of the trading terminal, including but not limited to: Opening and closing of clearing channels with counterparts; Off-chain market trading; Settlement of trades using blockchain escrow swaps; Deployment of broker nodes and mining liquidity; Testnet users will play a crucial role in evaluating the trading terminal's user experience, interface usability, Web3 signing flow, and overall system robustness. Layer 3 Foundation aims to gather valuable metrics, uncover vulnerabilities, and refine the platform for a wider audience. Applications for the closed beta can be submitted at yellow.org/canarynet. The company seeks blockchain-savvy individuals passionate about blockchain technology, trading, and finance. Successful candidates are expected to have a basic understanding of financial markets, experience in participating in testnets, and influence within small communities. Preference will be given to seasoned traders and technology enthusiasts who support the company’s mission. Yellow Clearing Network uses ClearSync, a protocol that frees traders from counterparty risk. It achieves this by verifying that all parties have sufficient collateral funding to fulfill trades. This ensures that traders do not exceed their capital limits, giving counterparties confidence that trades will be fully honored. Future Developments and Executive Insights Feedback from the closed testnet will be instrumental in preparing the platform for an open testnet phase and eventual general availability. says Louis Bellet, a core contributor at Layer 3 Foundation. "At Layer 3 Foundation, the imminent market introduction of our technology fills me with immense excitement! It's a game-changer in blockchain's evolution." Upcoming features include cross-chain settlements and enhanced security measures. The closed beta represents a critical phase in Layer 3 Foundation's roadmap to broader adoption and token listing. About Layer 3 Foundation The mission of Layer-3 Foundation is to develop a state channel-powered ecosystem as a Fintech scaling standard to support the evolution of the Internet towards Web 3.0. The company stands behind Yellow Clearing Network, a peer-to-peer solution for trading, clearing, and settlement. Operating as a Layer3 that utilizes state channels, Yellow Network provides a safer way for individuals and institutions to trade. With no requirement to lock assets, thanks to safety collateral, counterparties can transact safely within a liquid and fully compliant environment. Learn more: https://www.layer3.foundation/,  https://www.yellow.org/ About ClearSync Ltd. ClearSync Ltd. is a UK-based technology research center and a developer of the ClearSyncTM protocol, a set of automated smart contracts that allow participants to lock and unlock collateral through state channels to protect the clearing and settlement of assets. This press release is for informational purposes only and does not constitute an offer or solicitation to sell shares or securities. Contact Head of Biz Dev & Marketing Anastasiia Bobeshko ana@yellow.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Layer 3 Foundation Announces Testnet Of P2P Yellow Clearing Network For Counterparty Risk Mitigation

Let's learn about via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Interoperability /Learn Repo 1. What Is True Blockchain Interoperability? If you are in the crypto world, then the word "blockchain interoperability" may familiar to you because there are so many projects are gaining publicity and funds from investors by using this term, and many projects like  Chainlink, Cosmos, Polkadot, and Wanchain are claiming to give true blockchain interoperability. So in this article, we will discuss about these   projects and try to understand what kind of blockchain interoperability they are giving and what benefits we can get because of blockchain interoperability? 2. The Spatial Web is Coming... Web 3.0 is About to Take a Giant Leap - Part 4 Anything that wants to be recognized will have to be included in this Web 3.0 network. Every protocol, platform, interaction, messaging, data distribution, 1st 3. Blockchain Interoperability: What Do Cross-Chain Messages Do Anyway Cross-chain messages, the bread and butter of interoperability are an important blockchain technology that be used for transactions, information exchange & more 4. DAOs are a Catalyst for Decentralization - Unhashed #15 Harmony.one’s founder, Stephen Tse, discusses the importance of interoperability for blockchain. 5. Understanding Ethermint Ethermint is a portmanteau word blending Ethereum and Tendermint together. But for most people, their grasp on the project, the what, the why stops there. 6. Startup Interview with Antonio Peláez, Dapp CEO & Cofounder Dapp was nominated as one of the best startups in Queretaro in Startups of the Year hosted by HackerNoon. 7. Interoperability - the Key Blockchain Challenge in the Fragmented Web3 World A major blocker of Web3 is that blockchain are often isolated with no communication between them. The expansion of Web3 requires interoperability. 8. Meet Lisk Lisk is an open-source blockchain application platform that aims to improve Web3 accessibility for both users and developers. 9. Building for Inclusivity: Deconstructing Metaverse Barriers Joining the Metaverse isn't so easy, as this striped red fox recently discovered. So how do we embed inclusivity into its foundation for an equitable future? 10. Bullieverse: Building a Metaverse Beyond Gaming During the Non-fungible Conference, Hackernoon got an exclusive interview with Arunkumar Krishnakumar, who shared insights on his new project, Bullieverse. 11. A New Era of Interoperability With Metropolis World and Flare Network Metropolis World wants to go one step further and bridge the gap between different blockchains and metaverses, partnering with the Flare Network 12. The Difference Between WWW and the Spatial Web The World Wide Web is a library of pages. The Spatial Web is a library of spaces.
using a new protocol linking spaces and all objects over multiple dimensions. 13. Univers Partners With VR Connections to Make Metaverse Interoperability a Reality As the Metaverse narrative grows we will see multiple and different iterations of the Metaverse, each one based on the vision laid out by its creators. 14. True Decentralization is the Key to Cross-chain Bridge Security That said, even though today’s bridging solutions are not without their shortcomings, cross-chain bridging is a mandatory feature for blockchains. 15. Interoperability and Why It's Important for the Blockchain Space Interoperability in the context of blockchains refers to a blockchain's capacity to freely exchange information with other blockchains which will be important. 16. How Is Inter-blockchain Communication and Interoperability Achieved? This story explains inter-blockchain communication (IBC) and interoperability. 17. The Spatial Web Is at Hand...Is Web3 Prepared? - Part 1 Most Web3 companies are building for "enhanced" Web2 experiences. They are not yet considering what is truly required for the implementation of a decentralized 18. The Spatial Web is at Hand... What are Smart Technologies? Part 2 Undeniably, there are specific Smart Technologies that will be crucial to the architecture of this universal network of interconnected people, places & things, 19. Problems With Cross-Chain Bridging and Direct Integrations With Web2 Blockchain bridges are hard to do technically and susceptible to hacks. That's why other solutions like direction integrations are needed. 20. Providing Proof in an Untrusting World Dragonchain's hybrid platform provides blockchain-based measurable proof  for selective transparency, accountability, and regulatory compliance. 21. Exciting Developments in the Blockchain Industry 2022 A look at exciting developments in the blockchain industry for 2022. 22. Lisk Events at Berlin Blockchain Week Do you want to learn more about blockchain? Would you like to network with other Web3 enthusiasts and learn more about the most important industry trends? Then 23. The Role of Multichain Token Systems in Achieving Blockchain Interoperability In this interview we'll speak with Marius Ciortan on the role that multichain token systems play in achieving interoperability. 24. A Beginners Guide On Blockchain Bridges Blockchain Bridges address Blockchain's inherent inability for interoperability. By allowing different chains to communicate, the ecosystem is streamlined. 25. Cosmos Signal: Our Quarterly Keynote, Q3 2021 Our all-new high-impact quarterly keynote in which we reveal the latest exciting developments and showcase stunning new products in the Cosmos ecosystem is here 26. Omnichain dApps: a new crypto primitive Why the world needs a decentralized blockchain with built-in omnichain interoperability 27. Avatars and The Metaverse Here is what you need to know about avatars in the metaverse 28. Cross-chain Communication and the Role of CCU This is the next edition of the blog post series covering the details of the Lisk interoperability solution. 29. A Guide to Cross-Chain Bridges With the rapid development of DeFi market, the need for a covenient solution to transfer the assets between chains became apparent. 30. The Lingering Problem With Cross-Chain Bridges Without interoperability protocols, decentralization would be a myth. Let's explore the problem of cross-chain bridges and its solution. 31. Interoperability and Its Role in the adoption of Blockchain Technology A look at interoperability and its role in the adoption of blockchain technology 32. Why Multichain Collaboration Is Critical To The Future Of Web3 Web3 is going to be built by a network of engineers working towards common goals using multichain collaboration to make it as interconnected as possible. 33. Why the Spatial Web Demands a New Protocol As technology evolves, the way in which we interact with it and utilize it demands more expansion and sophistication over time. 34. Compact Blockchain Protocols can Disrupt IoT Devices If designed in a compact manner, blockchain applications can disrupt the IoT industry. 35. Layer2 Interoperability: StarkEx Vs.  Loopring Vs. Hermez Vs. Connext StarkEx, Loopring, Hermez and Connext use different methods to solve the funding efficiency problem of liquidity providers. Will any of them be a perfect one? 36. "Interoperability Would Mean Real Freedom of Money" - Binance CEO CZ [Original Interview] Despite going through a very rough period between early 2018 and early 2020, the so-called “crypto winter” has seemingly come to an end. Bitcoin and alternative markets are showing signs of life in terms of price discovery. Combined with the rise in attention for DeFi projects, the second half of this year - as well as the years to come - offer a lot of potential. More and more companies are paying attention to Bitcoin. As Binance CEO Changpeng Zhao states, it is a sign of stimulus money flowing from Wall Street into Bitcoin. 37. Peeling Off the Layers of the Metaverse The metaverse has layers, just like ogres and onions. 38. Cryptocurrency 2020: Heading Towards Privacy, Adoption, and Ease of Use? If cryptocurrency payments are to ever compete with credit cards and mobile apps for day-to-day use, sending and receiving cryptocurrency needs to be just as easy. 39. DeFiChain's U-Zyn Chua Thinks Multichain Web3 Compatibility is Where Blockchain Industry is Headed Discussion on the future of multichain technology within the Web3 space. 40. Ethereum High Transaction Fees Discourages New Participants From Entering the Ecosystem This article talks about blockchain, FATF and NFTs. 41. "India May Regulate Crypto But I don’t Anticipate A Ban" - Sandeep Nailwal This interview discusses the internet of blockchain, Polygon/matic and Defi and how developers can build decentralised applications. 42. The State of Blockchain Interoperability in 2020 [An Overview] This is my first major study (source), about Blockchain Interoperability, which started in December 2019 and was last updated today. We analysed 330+ documents and obtained feedback from 30 people, to categorize the most relevant blockchain interoperability projects. We came up with three categories: Cryptocurrency-directed interoperability approaches, Blockchain Engines, and Blockchain Connectors. 43. "Rollups Incentivize Operators to Verify and Execute Transactions Correctly." - Kevin Liu This interview talks about blockchain interoperability and scaling blockchain solutions with layer two solutions. 44. "The Need for Interoperability Is Becoming Increasingly Clear" - Jack Lu This article talks about blockchain interoperability and the early days of ethereum and smart contract development. 45. Catalysing DeFi: Introduction to Fusion and Fantom The DeFi space is growing tremendously since its inception, and platforms like Fusion and Fantom are proving to be the catalyst in this ever-growing niche. 46. The State of Blockchain Interoperability in 2021 After nearly 9 months of work, version 3 of “A Survey on Blockchain Interoperability: Past, Present, and Future Trends” is out. This survey depicts the state of blockchain interoperability as of March 2021, by collecting and analyzing 404 documents. 47. Blockchain Interoperability Interoperability is a characteristic of a product or system, whose interfaces are completely understood, to work with other products or systems, at present or in the future, in either implementation or access, without any restrictions. - Wikipedia 48. From Bianjie Point of View: On Helping Develop Cosmos Ecosystem We discuss how Bianjie and IRISnet have been instrumental in developing Cosmos so far, what the recent grant from the Interchain Foundation (ICF) means and more 49. Meet the New Lisk Mainnet Lisk Mainnet v2 successfully completed the migration to Lisk Mainnet v3. This comprises the biggest protocol changes on the Lisk blockchain ever. See details. 50. Token Transfers Using IBC Passing of Proposal 41 marks a momentous milestone in the evolution of the Cosmos ecosystem & the vision to realize the IBC Protocol as foundation. 51. Crypto Projects Are Racing to Crack the Interoperability Problem Interoperability is a big word with even bigger connotations for the cryptosphere. In Fact, apart from the burgeoning DeFi movement, no other industry vertical is attracting so much investment and interest. 52. Using Web3 to Detangle the World’s Supply Chain The global supply chain is in a gridlock. Let's fix that. 53. Purchase NFTs on Flare Using the Token of a Different Blockchain - Trustlessly! Dubai, Feb 23: The layer-1 for data, Flare, just completed a live demonstration of new interoperability functionality now available in beta on the network. Using two core interoperability protocols, State Connector and Flare Time Series Oracle (FTSO), an NFT was trustlessly purchased with the transaction taking place on a different chain using a different token. The demo was performed on Flare’s canary network, Songbird, with the purchase made using DOGE and XRP tokens. 54. Why does Blockchain Interoperability exist? Around ten months ago we wrote in this blog post that all the research up to blockchain interoperability was completed. 55. Blockchain Interoperability: The Lifecycle of a Sidechain From its inception to its potential termination, a sidechain may go through three stages, corresponding to three states, in the context of the interoperability. 56. Novel Solutions for Inclusive Digital Currencies An efficient, interoperable, scalable, and quantum encrypted CBDC solution for sovereign monetary systems of the future, possible today. 57. Lisk's Interoperability Solution: An Overview One of the central topics at our recent Lisk.js 2021 event, and the one we are most proud of in the Research team, was the publication of the Lisk interoperabil 58. Sparse Merkle Trees: The New State Model for Lisk's Interoperability This blog post is part of the interoperability blog post series taking a closer look at various aspects of the Lisk interoperability solution. 59. Tips to Achieve Data Interoperability in Healthcare Health data is notoriously difficult to share. Due to its sensitive nature, it requires more privacy and security than any other data. However, the inability to access it on time can cause significant harm. In a world with high healthcare interoperability, doctors can aggregate patient data from various care facilities with a few mouse clicks and view it on their computer screen, including the complex medical imaging files. This will supply clinicians with a comprehensive overview of a patient’s medical history, allow them to access any recent test results, instead of having to repeat the same tests, and will eliminate redundant entries. In their pursuit of interoperability, medical facilities turn to healthcare software development vendors. But they are met with various challenges, including technicalities, such as which standard to choose, privacy concerns, and reluctance towards data sharing. So, how to improve interoperability in healthcare? And is it worth the investment? 60. Co-Chains: Bridging Public and Private chains as a way to INTEROPERABILITY? Blockchain industry is developing at a very high pace of innovation, adjusting and tuning its parameters, trying to find its market fit. There are thousands of projects available on the CMC, but just a few really worth a bigger attention. Lets try to briefly review the industry, its trends, where we are heading, and how Hybrid platforms (priv+pub) fit into a bigger picture. 61. Blockchain Bridges And Interoperability: Overview, Roles, and Integrations To resolve the issue of non-interoperability, a cornerstone technology of blockchain has been developed — the bridge. 62. Polkadot Deep Dive 2021 - Connecting The $DOTs A detailed 2021 guide on Polkadot and how it is geared towards solving some of the most fundamental yet complex problems faced by the blockchain ecosystem today 63. Interoperability in NFT Games with Adrian Krion In this slogging AMA, we hosted Adrian Krion, the CEO of Spielworks. Adrian explained the purpose of Spielworks, and we discussed tokens, gaming and NFTs. 64. On Multichain and Interoperability with Maciej Baj and Jacobi Kowalewski In this Slogging AMA, we welcome Maciej Baj and Jacob Kowalewski from t3rn, a smart contracts platform. Thank you for checking out the 64 most read stories about Interoperability on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

64 Stories To Learn About Interoperability

Automated deployment strategies have emerged as a game-changing method to simplify , empowering organizations to achieve release cycles and improve their development flexibility. This article delves into the details of automated deployment strategies examining their importance, advantages, and how they effectively tackle challenges. software delivery What Are Automated Deployment Strategies? Automated deployment strategies consist of various tools and techniques that streamline the process of deploying software applications to production environments. These strategies remove the requirements for intervention greatly reducing the potential for mistakes and minimizing downtime during deployment. By automating the deployment process organizations can guarantee consistency, reliability, and repetition, in their software delivery cycles. Significance of Automated Deployment Strategies The significance of automated deployment strategies lies in their ability to address several critical challenges faced by software development teams: Manual deployment processes are often time-consuming, error-prone, and require significant effort from developers and operations personnel. Automated deployment strategies eliminate these inefficiencies, allowing teams to focus on higher-value tasks. Manual Deployment Inefficiencies: Manual deployments often introduce bottlenecks in the release cycle, delaying the delivery of new features and updates to users. Automated deployment strategies accelerate release cycles, enabling organizations to respond rapidly to market demands and user feedback. Release Cycle Bottlenecks: Manual deployments often involve downtime, disrupting user access to applications. Automated deployment strategies minimize downtime, ensuring seamless and uninterrupted service delivery. Reduced Deployment Downtime: Automated deployment strategies ensure consistent and repeatable deployment processes, eliminating the risk of variations and inconsistencies that can lead to errors and failures. Improved Deployment Consistency: Challenges of Manual Deployment Processes Manual deployment processes pose several challenges that hinder software development agility and reliability: Manual deployments are susceptible to human error, which can lead to configuration mistakes, compatibility issues, and application downtime. Human Error: Manual deployments lack repeatability, as the process is often dependent on individual knowledge and experience. This can lead to inconsistencies and variations in deployment outcomes. Lack of Repeatability: Manual deployments require significant time and effort from developers and operations personnel, diverting resources away from more strategic initiatives. Time-Consuming Efforts: Manual deployments become increasingly challenging as the scale and complexity of software applications increase. Limited Scalability: Automated deployment strategies effectively address these challenges, paving the way for streamlined, reliable, and scalable software delivery. Types Of Automated Deployment Strategies Automated deployment strategies encompass a spectrum of approaches tailored to specific deployment scenarios and application requirements. Each strategy offers unique advantages and considerations, making the choice of deployment method crucial for ensuring optimal performance and user experience. Rolling deployment , also known as incremental deployment, involves releasing a new version of the application in a gradual manner, rolling out the update to a small subset of users first. This approach minimizes disruption and allows for continuous monitoring of the deployment's impact. If any issues arise, the deployment can be rolled back or halted, preventing widespread disruption. Rolling deployment Blue-green deployment involves maintaining two identical production environments, designated as "blue" and "green." When a new version is ready, it is deployed to the "green" environment, while the "blue" environment continues to serve live traffic. Once the new version is thoroughly tested and validated, the traffic is switched to the "green" environment, while the "blue" environment is decommissioned. This approach ensures seamless transitions and minimal downtime. Blue-green deployment Canary deployment Canary deployment is a technique that involves releasing a new version of the application to a small, controlled group of users, often referred to as "canaries." This group is selected to represent a diverse range of user demographics and usage patterns. By observing the canaries, organizations can detect potential issues early on before rolling out the update to a broader audience. A/B testing deployment deployment involves releasing two different versions of an application to separate groups of users. This approach enables organizations to compare the performance and user acceptance of the two versions. By analyzing user behavior and feedback, organizations can determine which version is more effective and make informed decisions about the wider rollout. A/B testing The choice of automated deployment strategy depends on various factors, including application architecture, deployment frequency, and risk tolerance. Rolling deployment is particularly suitable for applications that can tolerate minor disruptions, while blue-green deployment is ideal for mission-critical applications where downtime is unacceptable. Canary deployment is valuable for identifying potential issues early on, while A/B testing deployment is crucial for data-driven decision-making. Advantages and Disadvantages of Automated Deployment Strategies Each automated deployment strategy offers unique benefits and considerations, making the choice of deployment method crucial for ensuring optimal performance and user experience. Rolling Deployment Advantages: Minimal downtime and disruption Continuous monitoring and rollback capability Suitable for applications with high availability requirements Disadvantages: Requires careful configuration and monitoring May introduce performance degradation during deployment Not suitable for applications with strict consistency requirements Real-world example: Netflix utilizes rolling deployment to release new features and updates to its streaming platform. By gradually rolling out updates to a small subset of users, Netflix can monitor the impact of the update and quickly roll back if necessary, minimizing disruption to its global user base. Blue-Green Deployment Advantages: Zero downtime during deployments Seamless traffic switching Suitable for mission-critical applications Disadvantages: Requires maintaining two identical production environments May increase infrastructure costs Complexity of setup and maintenance Real-world example: 's e-commerce platform, Amazon.com, employs blue-green deployment to ensure seamless transitions during major software releases. By maintaining two identical production environments, Amazon can thoroughly test new versions without impacting live traffic, ensuring a smooth and reliable user experience. Amazon Canary Deployment Advantages: Early detection of potential issues Controlled rollout to a limited audience Suitable for identifying performance bottlenecks Disadvantages: May introduce performance variations Requires careful selection of canary users Not suitable for applications with strict consistency requirements Real-world example: Google utilizes canary deployment to release new features to its search engine. By deploying new features to a small group of canary users, Google can gather feedback and identify potential issues before rolling out the update to a broader audience, ensuring a high-quality user experience. A/B Testing Deployment Advantages: Data-driven decision-making Comparison of different feature implementations Suitable for optimizing user engagement and conversion Disadvantages: Requires careful experiment design and analysis May introduce performance variations Not suitable for applications with strict consistency requirements Real-world example: E-commerce platforms like Shopify and Etsy use A/B testing deployment to optimize their checkout processes and landing pages. By comparing different versions of these pages, e-commerce platforms can identify the most effective design and maximize conversion rates. Implementing Automated Deployment Pipelines Automated deployment pipelines are the backbone of modern software delivery, enabling organizations to streamline the process of releasing new software to production environments. These pipelines encompass a series of automated steps that transform code changes into deployable artifacts, test the artifacts, and deploy them to the target environment. Key Components of Automated Deployment Pipelines Effective automated deployment pipelines typically consist of the following key components: CI involves automatically building and testing code changes as they are committed to a version control system. This ensures that code is always in a buildable and testable state. Continuous Integration (CI): CD extends CI by automatically deploying tested code changes to production environments. This minimizes the time between code changes and deployment, enabling rapid delivery of new features and updates. Continuous Delivery (CD): IaC treats infrastructure as software, enabling automated provisioning and management of infrastructure resources. This ensures that infrastructure is consistently configured and aligned with the application's requirements. Infrastructure as Code (IaC): Continuous monitoring and logging are crucial for tracking the health and performance of deployments. This enables early detection of issues and prompt corrective actions. Monitoring and Logging: Tools and Technologies for Automated Deployment A rich ecosystem of tools and technologies supports automated deployment pipelines. Some popular examples include: Jenkins, GitLab CI/CD, Azure DevOps Pipelines, CircleCI CI/CD tools: Terraform, Ansible, Chef, Puppet Infrastructure as Code tools: Ansible, Chef, Puppet, SaltStack Configuration management tools: Prometheus, Grafana, Splunk, ELK Stack Monitoring and logging tools: Building and Implementing Automated Deployment Pipelines The process of building and implementing automated deployment pipelines typically involves the following steps: Determine the stages of the deployment pipeline, such as development, testing, staging, and production. Define deployment stages: Integrate CI tools to automatically build and test code changes as they are committed. Automate code building and testing: Configure CD tools to automatically deploy tested code changes to a staging environment for final validation. Automate deployment to staging: Utilize IaC tools to automate the provisioning and configuration of infrastructure resources. Automate infrastructure provisioning: Integrate monitoring and logging tools to provide continuous visibility into deployment health and performance. Implement continuous monitoring: Establish automated rollback and recovery procedures to handle deployment failures. Automate rollback and recovery: Continuously refine and improve the deployment pipeline based on experience and feedback. Continuous improvement: By following these steps and leveraging appropriate tools and technologies, organizations can effectively implement automated deployment pipelines, enabling rapid, reliable, and consistent software delivery. Conclusion Automated deployment strategies have completely transformed the landscape of software development empowering organizations to achieve progress and bring value to users at an unprecedented pace and effectiveness. By automating the process of deploying software organizations can eliminate the potential for mistakes, reduce downtime and guarantee an experience, across different environments. The key takeaways from this article are as follows: Automated deployment strategies encompass a range of techniques that automate the process of releasing new software versions to production environments. Different deployment strategies, such as rolling deployment, blue-green deployment, canary deployment, and A/B testing deployment, offer unique advantages and considerations tailored to specific deployment scenarios and application requirements. Automated deployment pipelines are the backbone of modern software delivery, enabling organizations to streamline the process of releasing new features and updates. Implementing automated deployment pipelines requires careful planning, selection of appropriate tools and technologies, and continuous improvement based on experience and feedback. Automated deployment strategies are not just a trend; they are an essential component of modern software development practices. By embracing automation, organizations can unlock the full potential of agile methodologies and deliver software that meets the ever-changing demands of today's digital world. For further learning and exploration, consider the following resources: Continuous Delivery: Reliable Software Releases in the Age of DevOps: Here The Phoenix Project: A Novel About IT Operations: Here

CANARY TECHNOLOGIES

canarytechnologies.com

51-200 employees

Since 2017

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CANARY TECHNOLOGIES

canarytechnologies.com

51-200 employees

Since 2017

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CANARY TECHNOLOGIES

EVERGREEN INDEX #2505

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