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Dentistry Dentistry Dr. Alesia Apana is a dentist in Washington, District of Columbia. She provides advice on proper brushing, flossing, cleaning, healthy gums, and other dental care. It's ideal ...

Dr. Alesia Apana

U.S. News & World Report

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Mera Apana Aasma Song | Romy | Akelli (Original Motion Picture Soundtrack)

gaana

At one end of the table, Ricky Apana is toasting crushed eggshells in a giant wok while Seth Raabe pours charred animal bone bits into a jar. Vincent Mina is sprinkling brown sugar over smashed ...

Korean natural farming uses organic items to replenish soil

The Maui News

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Chandra gupt Maury ko kyu tu apana bap banata hai Song | Vinod kushwah

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Last year, Malama Kakanilua and Apana wrote to the SHPD administrator seeking a contested case on Archaeological Services Hawaii’s 2018 permit and any permit applications for subsequent years ...

Judge reverses permit approval for archaeological firm

Get the most recent info and news about APANA on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Apana, Inc. provides automated water management solutions. The Company offers cloud-based analytics platform to scan, pinpoint, and guide resolution of water waste by instantly alerting frontline staff. Apana serves commercial and industrial applications in the State of Washington.

APANA

Meet the Winners of Startups in North and Central America

I recently sat down with the CEO of BeInCrypto to discuss the rise of crypto journalism and the recent spate of anti-crypto narratives circulating in mainstream media. You can also read Forbes’ story on . Alena Afanaseva How BeInCrypto Took On The Sector’s Macho Image - And Won Does BeInCrypto Have a Physical Office? Thanks to the culture of the Web3 industry, BE[IN]CRYPTO proudly adopts a remote-first and asynchronous work culture. As a result, we have successfully built one of the world's leading crypto media companies, covering 65 markets in 14 languages without needing a physical office or headquarters. Our team, comprising over 260 members across 65 markets, operates entirely remotely. I am based in Argentina, there are opportunities for in-person meetings, and we do occasionally gather with team members at BE[IN]CRYPTO. While I still believe in the importance of face-to-face interactions, I recognize that they are not as crucial as once. Moreover, as a leader committed to sustainability and mindful of our carbon footprint, we embrace a remote-first culture to achieve a net-zero impact. What's your route to blockchain and Web 3? Is crypto journalism a promising industry? I take great pride that BE[IN]CRYPTO's editorial arm has achieved self-sustainability, making us one of the rare news media organizations within the crypto industry and globally capable of building an editorial model that generates sufficient cash flow to maintain profitability. This accomplishment is due primarily to our remote-first approach, which has significantly reduced our financial requirements. However, we are not resting on our laurels. We recognize the strength of our community and readership base and are actively working towards diversifying our portfolio of services. With the emergence of technologies like AI and the changing landscape of content consumption, we are committed to reimagining the future and meeting the evolving demands of our readers. Regardless of any future changes, I firmly believe that the need for a trustworthy news organization with an unbiased narrative will remain as crucial in the future as it is today. Thanks to this strategy, we operate in 14 languages and cover 65 markets while keeping our operational costs lower than our competitors through our remote-first culture. Being financially independent allows us to remain fully transparent and unbiased, enabling us to explore different growth directions, such as developing our education portal, dedicated HR product for talent management, and other services that build our financial strength. As we look towards the future of BE[IN]CRYPTO, our focus remains on diversifying our revenue streams by developing new products that address the needs of industry participants. By continually innovating and providing value for our readers and partners, we are confident that we will maintain our position as a leading media organization in the crypto industry. One such new offering we are developing is an advanced learning portal, providing users with a comprehensive learning experience to develop new skills and gain a competitive edge in the job market through upskilling and reskilling. We aim to bridge the talent gap and fuel adoption by offering a learning and development platform that benefits job seekers and employers. Additionally, we offer managed HR services to top businesses in the industry, facilitating team building, hiring, and talent upskilling. We are confident that our platform will soon become the go-to resource for traditional companies seeking to upskill their existing talent to meet the demands of web3 adoption and development. In addition to vertical diversification, our goal is to become the reference point for everything web3 in all languages. We operate in 14 languages and plan to expand our linguistic capacities to provide even more coverage. BE[IN]CRYPTO offers services in more languages than any other direct competitor, giving us an edge in the industry. How did you set up Beincrypto? In 2018, the idea for BE[IN]CRYPTO was born during the ICO boom. With a decade of experience as a fundamental analyst, I saw the vast potential of blockchain projects and recognized a dire need for reliable information in the industry. Many inexperienced entrepreneurs were attempting to cash in on the burgeoning industry, leading to many scams, misinformation, and shilling around crypto. I had the right background and experience to develop a crypto news media project that could fill this gap. Our mission to simplify the first steps for non-experienced users set us apart from competitors, making the web3 space accessible for everyone. We focused on providing trustworthy and accurate information for all things crypto, aimed specifically at newbies. This approach proved successful, and within six months of launch, we attracted 500K monthly users. We soon realized that while crypto market information was accessible in English, it was scarce in other languages. This prompted us to change our focus from growing within the English-speaking audience to expanding worldwide, offering more opportunities to people from emerging markets. After a year of operation, we formed local teams to provide original content in their language, starting with Brazilian Portuguese, Spanish, Russian, and Turkish. Two years later, we added four more languages, becoming the world's leading international crypto new media. In 2021, we became the first crypto news media to adopt globally recognized trust indicators through the 'Trust Project.' The project honors news sites that comply with its 8 Trust Indicators aiming for news transparency and accuracy. This recognition helped us establish ourselves as a reliable source of information and cemented our commitment to providing only fact-checked information. Today, we publish unique content in 14 languages written by locals who understand the audience's pains. At BE[IN]CRYPTO, we strive to empower your web3 journey, regardless of your background and skills. Our mission is to make the web3 space accessible to everyone, and we will do our best to provide you with all the needed information without misleading or complicating things. While the costs were on us for the first six months after we launched BE[IN]CRYPTO, we continue to expand and provide a platform for all things crypto. What were the early challenges you faced setting up your media company? How did you overcome them? At BE[IN]CRYPTO, we lead by example when it comes to finding top talent. We believe in hiring individuals who share our values of responsibility and self-drive, are passionate about new technologies, and are willing to work hard to build something from scratch. However, finding high-quality talent can be challenging, especially when working with a limited budget and in a new industry like web3. When we started, we faced the same hurdles, but through perseverance, we built the right team of 250+ employees spread across 50+ countries. Managing a 100% international and remote team from different cultures, backgrounds, and time zones in a decentralized setting is an ongoing improvement process. Local teams produce our content, and we have partners in over 160 cities worldwide. These regional teams take timely global information and fine-tune it to their target audience, speaking their language - literally. Building on this success, we are now developing our HR product offering to help similar businesses in the industry build the right capacities in a decentralized and asynchronous manner. Through our expertise in managing a remote team, we aim to help others navigate this new way of working and build their successful decentralized teams. Does crypto journalism have a common philosophy? Bloomberg, Financial Times, and CNBC are increasingly entering the crypto sphere by establishing dedicated news branches. While several successful crypto information portals cater to various needs, we closely follow the work of our colleagues at Decrypt, Cointelegraph, and Coindesk. Despite the intense competition, we take pride in that BE[IN]CRYPTO, the youngest among them, has established itself as one of the top three players in less than four years. At BE[IN]CRYPTO, we differentiate ourselves by simplifying the initial steps for non-experienced users and helping a less mature audience understand the web3 and crypto industry, explore its potential, and embark on their journeys. This sets us apart from competitors who primarily target the needs of experienced "crypto geeks," making the space exclusive and inaccessible to a broader audience. Our mission is to empower individuals on their web3 journey, regardless of their background and skills. We firmly believe that anyone can enter the space and are committed to providing all the necessary information without any misleading or complicated jargon. Our goal is to make web3 accessible to everyone by offering learning opportunities and employment possibilities within the industry, allowing individuals to develop the skills and knowledge needed to thrive in the crypto world. Today, we can confidently assert that BE[IN]CRYPTO and most of our direct competitors in the industry take journalism very seriously and adhere to the highest journalistic ethics when collecting and publishing news. However, as the sector is forward-thinking, we must also be agile and creative. It has become common for media outlets to reference tweets from insiders, access community information, and build stories around bite-sized knowledge. This agile approach is driven by the need for financial market players to have firsthand information to make informed decisions. As a result, I believe that crypto media is challenging the status quo and traditional media while maintaining trustworthiness and a solid reputation. Is it reactive to mainstream media pushing a wrong narrative about crypto? Mainstream media has long been influenced by bias shaped by sponsors and external funding. As I mentioned earlier, at BE[IN]CRYPTO, we firmly believe that only financially self-sustainable media organizations can maintain unbiased reporting in today's complex entrepreneurial environment. Traditional media has exhibited bias not only in their coverage of crypto and web3-related news and stories but also in the handling of incidents like the FTX scandal, where media outlets tended to approach the story with a victim-blaming perspective, failing to hold criminals accountable and using language that softens the severity of the situation. The New York Times, for instance, was among those guilty of this approach. In other words, it is exhibits bias against crypto. I firmly believe that any media organization should recognize its power and act with goodwill, serving as an open platform for education and enlightenment without bias. It may sound overly optimistic, and perhaps it is, but this is how we operate within our organization. Where do you see it heading? As I mentioned earlier, the trustworthiness of a news media organization cannot be achieved when it heavily depends on external factors. At BE[IN]CRYPTO, we take pride in being an independent news media organization with a positive cash flow. We have chosen a different path in an industry where many players receive heavy subsidies, such as Coindesk, owned by Digital Currency Group, and Binance, investing in news media organizations. Binance, for example, made a $200 million investment in Forbes shortly after suing the publisher for defamation and even secured seats on the media organization's board. Instead of relying on external financial support, we believe in sustainable and steady growth, reinvesting our profits into developing markets. The question of how a news media organization can remain unbiased when it heavily relies on external investments for survival is not unique to crypto media but applicable to the entire news business and media industry. While financial support and funding may offer a competitive advantage to some businesses, our focus on sustainable growth allows us to maintain independence and make decisions that prioritize the needs of our readers and the markets we serve. This strategy also enables us to attract top talent, enhance our editorial capabilities, and expand our user base in alignment with our values and mission. We have established several revenue streams at BE[IN]CRYPTO contributing to our positive cash flow. Our primary source of revenue comes from advertisers and affiliate partners, reflecting the value of our platform as a leading source of information and insights for the crypto industry. In addition to advertising, we have experienced a surge in demand for our expert HR product, which assists emerging Web3 businesses in building their teams, streamlining hiring processes, and establishing remote work foundations. Our job portal, featuring opportunities from top employers in the industry, has also seen substantial growth, attracting over 20,000 job seekers every week. Our newly developed education portal and consulting business have started generating revenue after years of steady investment and product development. These areas hold significant potential for further growth and expansion in the coming years. Looking ahead to 2023, our goal is to diversify our revenue streams through new product development that caters to the needs of industry participants. We are confident in maintaining our position as a leading media organization in the crypto industry by continuously innovating and introducing offerings that add value for our readers and partners.

"Traditional Media Exhibit Bias Against Crypto", Alena Afanaseva CEO BeInCypto News Media 

Abhor online project management tools? They are an unrelenting aspect of remote work. Meet Slack, Asana, and WhatsApp, the three horsemen of the WFHpocalypse. This Slogging thread by Justin Roberti, Arielle Shnaidman, Linh Smooke, Akasha Rose and David Smooke occurred in Hacker Noon's Slogging community #unpopular-opinion channel, and has been edited for readability. Justin Roberti Jun 10, 2021, 5:59 PM Unpopular Opinion: 10 Ways Asana Lowers your Productivity Arielle Shnaidman Jun 10, 2021, 6:17 PM 👍 1 You can’t toggle between two different emails/logins. Justin Roberti Jun 10, 2021, 6:36 PM 🔥 3 In the amount of time that it takes me to find the project and add a task, I can write 10 tasks on a notepad AND draw a decent cartoon cat. Linh Smooke Jun 10, 2021, 6:58 PM 👍 2 The emphasis on deadline and date at the expense of expansion of ideas (card style) like Notion or trello. Justin Roberti Jun 10, 2021, 8:23 PM Subtasks are an excellent way to outline key steps in a task, so everyone knows who needs to do what and by what deadline. And like the one true ring, subtasks are invisible to all but the most powerful observer, meaning your subtasks protected in solitude where no assignee will ever find them. Linh Smooke Jun 10, 2021, 8:24 PM The price point. Linh Smooke Jun 10, 2021, 8:25 PM 👍 1 (I tried to use asana as a team for a year, nobody got much done with the system, then at the end of it got charged a hefty sum. Good thing they were able to refund!) Justin Roberti Jun 10, 2021, 8:26 PM True - remember basecamp in the early days? I never made that one work right either, but it was free. Linh Smooke Jun 10, 2021, 8:27 PM OMG basecamp. lol. we did discuss at length their no politics policy in this podcast . https://hackernoon.com/this-week-on-planet-internet-big-dictator-energy-nn943437 Linh Smooke Jun 10, 2021, 8:30 PM In general I'm not opposed to freemium though if I see that they do provide value. Unfortunately, asana ain’t it. Justin Roberti Jun 10, 2021, 8:31 PM :rolling_on_the_floor_laughing: 4 hahaha i have to say, this episode has 65% more dunking on Limarc Ambalina than other leading podcasts. Linh Smooke Jun 10, 2021, 8:31 PM 😀 2 He did sympathize with dictators…. Justin Roberti Jun 10, 2021, 8:35 PM 👀 1 💚 1 Slack, Asana, WhatsApp (and/or Telegram) - the three horsemen of the WFHpocalypse. Justin Roberti Jun 10, 2021, 8:38 PM 😂 2 Freemium... where ASPs go to get friendzoned. Linh Smooke Jun 10, 2021, 8:40 PM We LOVE (maybe too much) notion here at HN. Natasha Nel is basically their sales rep (not really… 😃 ) but like I’m so glad she intro-ed us to the awesomeness that’s notion. Justin Roberti Jun 10, 2021, 8:41 PM I've not used it. Is it free(mium)? Linh Smooke Jun 10, 2021, 8:44 PM ha yeah. It’s like Trello meets Asana meets google doc (ok i’m not a notion sales rep though). Linh Smooke Jun 10, 2021, 8:44 PM Wondering if we just have to temper the notion love down if this thread gets published on HN or get a cut from them first. :rolling_on_the_floor_laughing: Justin Roberti Jun 10, 2021, 8:46 PM :rolling_on_the_floor_laughing: 2 "Linh Dao Smooke shills for Notion" Linh Smooke Jun 10, 2021, 8:46 PM For real. Justin Roberti Jun 10, 2021, 8:47 PM I do not work like any PM software I have found. that doesn't mean I'm right - it means I'm ADHD but it also means the notepad is my best bet. Linh Smooke Jun 10, 2021, 8:49 PM Speaking of ADHD, I learned this thing (maybe I’m just ignorant) very recently. That ADHD doesn’t mean the ability to not focus on things. On the contrary, it means you either focus exclusively on 1 thing, or a million things at once. it blew my effing mind. Linh Smooke Jun 10, 2021, 8:50 PM David Smooke is a total ADHD btw lol Justin Roberti Jun 10, 2021, 8:52 PM 💚 2 That's why I immediately liked David. I don't know if this should ever be an article because now it's slightly confessional but yes, one nice thing about writing, blockchain, and writing about blockchain is that I can intentionally work with other people i relate to... and I think that often means the hyperfocus/wide focus thing. Linh Smooke Jun 10, 2021, 8:55 PM Also that means you prob can’t work “for” anybody but yourself, and crave that independence + freedom…not a bad thing at all in my opinion. Justin Roberti Jun 10, 2021, 8:59 PM 💚 1 True. I think its part of the neuro-divergent picture and it was just seen as "Bad" in the 1980-90s. Akasha Rose Jun 10, 2021, 11:30 PM What happens if you plug Asana into Notion.... would people still hate it? https://www.youtube.com/watch?v=rOUxfH7RRrA David Smooke Jun 11, 2021, 5:20 AM 😂 1 😀 1 ADHD is not binary, it is granular. My attention moves fast, and that can be a deficit to my proclaimed activity and the activity of those around me, which I’d argue is not a disorder. My therapist describes it differently. Honestly my therapist is disordered, and the evidence for this is Dark Air with Terry Carnation: https://www.terrycarnation.com Linh Smooke Jun 11, 2021, 1:33 PM #classic

Unpopular Opinion: 10 Ways Asana Lowers your Productivity

29 Asana Alternatives: The Ultimate Breakdown

If you’ve worked at an early-stage , you’ve seen a system break down before. There’s a process in place. It works (or the team thinks it works). It breaks. The team keeps using it because they don’t have time to fix it. Eventually, the team accepts that it has outgrown the system. They build and adopt a new one. startup Requirements Gathering & Understanding the Problem I knew that the management process wasn’t working, but I wasn’t sure why. I needed to fully understand the problem before I could even consider solutions. I spent time looking over the system and trying to figure out exactly where it was breaking. project I discovered four major flaws that were causing the existing system to behave ineffectively: It was designed for one team working on one project. Our team frequently worked on a few projects at a time. It did not have a centralized location where I could track tasks easily and quickly. I was using the Kanban board while the engineers were using the issues tracker. It was hard to see what people were working on, so I’d often need to spend upward of 30 minutes to figure out what people were doing, I’d often have to spend a lot of time explaining what I meant when I wrote feature descriptions. We had a lot of structure and steps to follow, but it wasn’t making us more effective. We were mired in process, but it was only slowing us down. Once I understood the problems with the process, I was able to set four concrete goals for the new system. The system needed to make it easy for a team member to see all of their tickets across multiple projects. The system needed to be straightforward, so I could quickly see what each team member was working on. The system needed to reduce the friction caused by back-and-forth conversations about the contents of tickets. The system needed to fit in with our core value of minimal process. In other words, we needed just enough process that it made us more effective. I decided to treat these goals as KPIs. With my goals in hand, I began to research existing solutions. Market Research I sat down to research how other teams were approaching project management and what technologies they were leveraging to do it. I paid close attention to the tradeoffs those teams were making when they chose a platform. I had used Pivotal Tracker in the past and found that while it offered in-depth analytics, it paid the price in usability and flexibility. Github Projects linked with Github but the engineers never wanted to use the Kanban board feature and often forget to close their issues. Keeping track of who was working on what began to take up 80% of my day. Jira, a favorite of big engineering teams, felt too heavy for our small engineering team. Google Sheets would have made sense to our stakeholders but required too much upkeep for our needs. Finally, we settled on Asana. The UI is pleasing, I could create both lists and Kanban boards, and it felt flexible. Prototyping Once I felt I had done enough research, I picked the smallest and most well-defined piece of the process that I could think of: the engineering workflow. I designed an MVP that we could test almost immediately. I started out with two “features:” a Backlog and a Sprint Board. The Backlog was a prioritized list of tasks. The first iteration Backlog The Sprint Board, on the other hand, was a Kanban board that had 4 columns. Ready, In Progress, In Review, and Done. The first iteration Sprint Board And that was it! Once I confirmed that everyone on the team had bought into the adjustment, we went ahead and started to use the process. The key was that I immediately began asking for feedback, especially around the goals I was measuring the process against. User Feedback & Iteration My team was happy to give feedback. One of the most important suggestions came from one of the engineers on the team. He asked if we could add more columns to the Sprint board. In my attempt to keep process as minimal as possible, I had allowed for too little complexity. After some back-and-forth, we added two more columns to the board–– and . This change made it easier to see what people were working on at a glance and it reduced in-person conversations about tickets. Ready for Release Live As the number of projects we were taking on multiplied, my team also brought up that it became difficult to understand which tickets belonged to which project. We began to add the name of the project to the beginning of the ticket’s name. For example, if the ticket was named we would change it to: That meant that the ticket was associated with our CMS, rather than our Alexa skill. refactor scheduler feature [CMS] refactor scheduler feature. This small adjustment allowed us to remain flexible while still fitting in with our core value of minimal process. We could still use one board to work on multiple projects, instead of creating a different board for each project and having to check every single one of them. Lessons Learned Building a system is building a system, and it’s possible to borrow from other disciplines. Project Management at an early-stage startup is not as different from Product Management or Engineering as I had initially thought. Solving a problem tends to reveal flaws in satellite processes. For example, once the engineering workflow was ironed out, I realized I needed to iterate on the design and the product processes. It’s crucial to test your assumptions. Ask your team what they think. There’s nothing better than user feedback! Something that works for you might not work for your users. 🌟 🌟 If you liked this post, make sure to follow me on medium , follow me on twitter , and clap for this post!

How I Use Asana for Project Management

110 Stories To Learn About Time Management

When new learnings are discovered locally, there must also be some mechanism to enable the rest of the organization to use and benefit from that knowledge into explicit, codified knowledge, which becomes someone else's expertise through practice.” Gene Kim, The DevOps Handbook It's hard to believe that two Flickr employees could have such a profound impact on the world of IT. Their , " ," delivered at the O'Reilly Velocity conference in 2009, has led to the widespread adoption of DevOps. and are the masterminds behind this innovative approach to work. legendary talk 10 Deploys per day Dev & Ops at Flickr John Allspaw Paul Hammond In their presentation, they emphasized the importance of integrating development and operations teams to efficiently build, test, and deploy software. The blame game The development team and Operations used to work in separate silos, each with its own metrics. Developers built and tested software before pushing it to IT Operations for deployment. Developers often refuse to accept fault when things go wrong. They claim everything went well in their environment before pushing the work to the other side (Operations teams), maybe due to broken infrastructures. The operations team complained about receiving buggy and poorly written software, potentially due to unstructured coding. This led to a never-ending cycle of blaming. 2. DevOps a new highway for engineering and Collaboration DevOps is a combination of tools, a cultural change, a philosophy, and a new mindset that has revolutionized the IT world. By bringing the development and operations teams together, it has changed the way we work. It is a culture of sharing that unites previously separated silos into one efficient unit to deliver software effectively. The result is a significant shift in the way IT organizations operate. Keep C.A.L.M.S What makes DevOps a cultural change is its core precious principles, the ability to deliver high-quality software, Developers and the IT operations team can work efficiently, improve their way of learning and collaborate. let’s be CALMS to discover the principles : : DevOps is not a technology, as a cultural change requires different stakeholders to bring the change they want to see. It’s a culture of sharing. Culture : DevOps goes beyond a set of tools. Automation techniques play a crucial role in speeding up the deployment of processes, detecting defects, and fixing them earlier. Automation : Improving efficiency and reducing waste are the main goals of lean. Continuous improvement and learning are behind this management philosophy. Lean : You can’t improve what you do not measure! success in DevOps is required to measure performance, people metrics, and processes, a great way for tremendous success. Measure : Sharing is caring, DevOps requires a culture of sharing and feedback, no separated silos anymore. The ultimate goal is to create an inclusive and shared fate environment. Share The importance of communication and collaboration Communication and collaboration are key to a successful DevOps culture. When the development and operations teams work effectively, they can: Identify problems early in the development process, which is a great way to save time and money Deploy new features and update swiftly Improve the software quality Reduce the risk of downtime foster a greater innovative culture Gone are the days of isolated departments and information silos. Now, everyone in the organization is kept up-to-date about the latest changes and developments. Transparency is highly valued, and teams communicate openly, sharing their ideas and knowledge with one another. This collaborative approach fosters an environment where amazing ideas can come to fruition at any time, and experimentation is encouraged. Team members can freely share their work with the world and receive constructive feedback, which can lead to innovation and progress. A plethora of tools DevOps is a culture of sharing where communication is key. There are many tools available to help teams collaborate efficiently and learn from each other. Some of the well-known tools include: Trello Microsoft teams Asana Base camp How to adopt a DevOps culture Change is a natural part of life. Innovation and progress are ingrained in human nature, and we constantly strive to better ourselves and the world around us. However, adopting new ideas and ways of thinking can be challenging for some individuals, such as embracing the concept of DevOps. It takes time and effort to properly train teams and ensure everyone is on board with the new mindset. There are various methods to create a positive impact: Create a work environment where people can learn, fail, grow and innovate Accept failure and don’t play the victim Create a work environment where people accept their faults and grow Being open and honest with the teams Create a work environment where people can learn and collaborate all the time; nobody knows everything Create a shared responsibility between the development and Operations teams The DevOps culture in numbers The new mindset has a global impact, and each company has a unique experience and story to share. If we were to list every company and tell their story, we'd need a book! Atlassian is a well-known company. According to a survey in 2020 with CITE research, of respondents said DevOps had a positive impact on their work, a fast time to market, and high-quality software. 99% 49% 61% Culture is paramount before innovating on a global scale! Conclusion Every company has a unique vision and mission aimed at making a positive impact on the planet. However, adopting DevOps requires time and a shift in mindset. Those who successfully adopt this cultural change are now seeing their dreams turn into reality. Here are some examples of companies who have achieved this: Netflix Facebook Linkedin Gitlab Amazon web services Microsoft Culture doesn't originate from nothing. Success requires everyone to participate in learning, failing, sharing, and growing environment, which leads to incredible innovations.

DevOps: From Blame Game to Collaboration

In a previous post, I provided an introduction to Apache Iceberg and showed how it uses MinIO for storage. I also showed how to set up a development machine. To do this, I used Docker Compose to install an Apache Spark container as the processing engine, a REST catalog, and MinIO for storage. I concluded with a very simple example that used Apache Spark to ingest data and PyIceberg to query the data. If you are new to Apache Iceberg or if you need to get Apache Iceberg set up on your development machine, then give this a read. introductory post In this post, I’ll pick up where my previous post left off and investigate a common big data problem – the need for a single solution to provide storage for raw data, unstructured data, and structured data (data that has been curated from raw data). Additionally, the same solution should provide a processing engine that allows for efficient reporting against the curated data. This is the promise of Data Lakehouses - the capabilities of Data Warehouses for structured data and the capabilities of Data Lakes for unstructured data - all in a centralized solution. Let’s look at our big data scenario in more detail. A Common Problem The diagram below depicts a common problem and a hypothetical solution. Data is coming into a data center from multiple locations and in multiple formats. What is needed is a centralized solution that allows raw data to be transformed such that a processing engine can efficiently support business intelligence, data analytics, and machine learning. At the same time, this solution must also be capable of storing unstructured data (text, images, audio, and video) for data exploration and machine learning. It should also keep any data that was transformed in its original format in case a transformation needs to be replayed or a data integrity issue needs to be investigated. As a concrete example, imagine a global custodial bank that is managing mutual funds for its clients. Data representing the accounting book of records and the investment book of records for each fund for each client is streaming into the Data Lakehouse constantly from geographies around the world. From there, safe passage checks need to occur (was everything sent received), and data quality checks need to run. Finally, the data can be partitioned and loaded into another store that will support start-of-day and end-of-day reporting. Alternately, maybe this diagram represents an IOT scenario where weather stations are sending temperature and other weather-related data. Regardless of the scenario, what is needed is a way to store the data safely in its original format and then transform and process any data that needs to be stored in a more structured fashion - all in one centralized solution. This is the promise of a Data Lakehouse - the best of a Data Warehouse and a Data Lake combined into one centralized solution. Let’s make the hypothetical solution described above real. This is depicted in the diagram below. There are two logical components to our Data Lakehouse. The first is an implementation of Apache Iceberg for structured data - the equivalent of a Data Warehouse. (This is what I built out in my - so I will not go into detail here.) The second logical component is MinIO for unstructured data - the Data Lake side of our Data Lakehouse. All data coming into the Lakehouse is delivered to this logical instance of MinIO. In reality, the two logical instances of MinIO shown above could be the same instance of MinIO in your data center. If the cluster you are running MinIO in can handle the ingestion of all incoming data and the processing requirements of Apache Iceberg, then such a deployment will save money. This is, in fact, what I will do in this post. I’ll use a bucket within Apache Iceberg’s instance of MinIO to hold all unstructured and raw data. previous post Let’s start playing with data by introducing the dataset I will use for this exercise and ingesting it into MinIO. The Global Summary of the Day Dataset The dataset we will experiment with in this post is a public dataset known as the Global Surface Summary of the Day (GSOD), which is managed by the National Oceanic and Atmospheric Administration (NOAA). NOAA currently maintains data from over 9000 stations around the world and the GSOD dataset contains summary information per day from these stations. You can download the data . There is one gzip file per year. It starts in 1929 and ends in 2022 (at the time of this writing). To build our Data Lakehouse, I downloaded every year’s file and put it in the MinIO instance being used for our Data Lakehouse. I put all files in a bucket named `lake.` The two buckets within our instance of MinIO are shown below. The `warehouse` bucket was created when we installed Apache Iceberg. here I used the MinIO console to ingest the raw data manually. In a professional pipeline, you will want to do this in an automated fashion. Check out to see how to use Kafka and Kubernetes to get data into MinIO. How to Set up Kafka and Stream Data to MinIO in Kubernetes These files are packaged for downloading convenience –  if you try to use them directly to create a report or a graph, then that would be a very IO-intensive operation (and potentially CPU-intensive). Imagine that you want to chart the average temperature per year from a specified station. To do this, you have to open every file and search through every row, looking for the entries that match your station on the day of interest. A better option is to use our Data Lakehouses capabilities for curating the data and reporting on the curated data. The first step is to set up a new Jupyter notebook. Set up a Jupyter Notebook First, navigate to the Jupyter Notebook server that is installed in the Apache Spark processing engine. It can be found at . Create a new notebook and in the first cell, add the imports shown below. (All the completed notebooks created in this post can be found .) http://localhost:8888 here from collections import namedtuple
import csv
import json
import logging
import tarfile
from time import time
from typing import List

from minio import Minio
from minio.error import S3Error
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

pd.options.mode.chained_assignment = None
bucket_name = 'lake' Notice that we are importing the MinIO library. The notebook we are building is an ETL pipeline from unstructured storage (MinIO Data Lake) to structured storage (Apache Iceberg, which uses MinIO under the hood.) The start of your notebook should look like this. Now, we can create an Iceberg database and table for our data. Creating an Iceberg Database and Table Creating the database and table for the GSOD dataset is straightforward. The script below will create the database which we will name `noaa`. Add this in a cell after the imports. %%sql
CREATE DATABASE IF NOT EXISTS noaa; The script below will create the table. gsod %%sql
CREATE TABLE IF NOT EXISTS noaa.gsod (
  station string,
  date timestamp,
  latitude double,
  longitude double,
  name string,
  temp double
)
USING iceberg
PARTITIONED BY (station) As you play around with Apache Iceberg, you will often want to drop a table so you can start an experiment over. The script below will drop the table should you wish to change anything about its setup. gsod %%sql
DROP TABLE IF EXISTS noaa.gsod; Ingesting Data from MinIO to Iceberg Now that we have the raw year-based zip files in our Lakehouse, we can extract, transform and load them into our Data Lakehouse. Let’s introduce some helper functions first. The function below will return a list of MinIO objects in a specified bucket that matches a prefix. def get_object_list(bucket_name: str, prefix: str) -> List[str]:
'''
Gets a list of objects from a bucket.
'''
  logger = logging.getLogger('gsod_logger')
  logger.setLevel(logging.INFO)

  # Load the credentials and connection information.
  with open('credentials.json') as f:
    credentials = json.load(f)

  # Get data of an object.
  try:
    # Create client with access and secret key
    client = Minio(credentials['url'], # host.docker.internal
      credentials['accessKey'],
      credentials['secretKey'],
      secure=False)

    object_list = []
    objects = client.list_objects(bucket_name, prefix=prefix, recursive=True)
    for obj in objects:
      object_list.append(obj.object_name)

    except S3Error as s3_err:
      logger.error(f'S3 Error occurred: {s3_err}.')
      raise s3_err
    except Exception as err:
      logger.error(f'Error occurred: {err}.')
      raise err

  return object_list Note that in the code above, a MinIO credentials file is needed. This can be obtained from the MinIO console. If you do not know how to get MinIO credentials, then there is a section of this that shows how to generate and download them. post Next, we need a function to get an object from MinIO. Since the objects are tar files we also need this function to extract data out of the tar archive and transform it into a Pandas DataFrame. This is done using the function below. def tar_to_df(bucket_name: str, object_name: str) -> pd.DataFrame:
  '''
  This function will take a tarfile reference in MinIO and do the following:
  - unzip the tarfile
  - turn the data into a single DataFrame object
  '''
  logger = logging.getLogger('gsod_logger')
  logger.setLevel(logging.INFO)

  # Temp file to use for processing the tar files.
  temp_file_name = 'temp.tar.gz'
  # Load the credentials and connection information.
  with open('credentials.json') as f:
    credentials = json.load(f)

  # Get data of an object.
  try:
    # Create client with access and secret key
    client = Minio(credentials['url'], # host.docker.internal
      credentials['accessKey'],
      credentials['secretKey'],
      secure=False)


    object_info = client.fget_object(bucket_name, object_name, temp_file_name)

    Row = namedtuple('Row', ('station', 'date', 'latitude', 'longitude', 'elevation',     

                     'name', 'temp', 'temp_attributes', 'dewp',
                     'dewp_attributes', 'slp', 'SLP_attributes', 'stp', 'stp_attributes', 

                     'visib', 'visib_attributes',
                     'wdsp', 'wdsp_attributes', 'mxspd', 'gust', 'max', 'max_attributes', 

                     'min', 'min_attributes', 'prcp',
                     'prcp_attributes', 'sndp', 'frshtt'))
    # Columns of interest and their data types.
    dtypes={
      'station': 'string',
      'date': 'datetime64[ns]',
      'latitude': 'float64',
      'longitude': 'float64',
      'name': 'string',
      'temp': 'float64'
    }

    tar = tarfile.open(temp_file_name, 'r:gz')
    all_rows = []
    for member in tar.getmembers():
      member_handle = tar.extractfile(member)
      byte_data = member_handle.read()
      decoded_string = byte_data.decode()
      lines = decoded_string.splitlines()
      reader = csv.reader(lines, delimiter=',')

    # Get all the rows in the member. Skip the header.
    _ = next(reader)
    file_rows = [Row(*l) for l in reader]
    all_rows += file_rows

    df = pd.DataFrame.from_records(all_rows, columns=Row._fields)
    df = df[list(dtypes.keys())]
    for c in df.columns:
      if dtypes[c] == 'float64': df[c] = pd.to_numeric(df[c], errors='coerce')
    df = df.astype(dtype=dtypes)

  except S3Error as s3_err:
    logger.error(f'S3 Error occurred: {s3_err}.')
    raise s3_err
  except Exception as err:
    logger.error(f'Error occurred: {err}.')
    raise err

  return df Both of these functions are generic utilities that can be reused regardless of what you are doing with MinIO. Consider putting them in your personal collection of code snippets or your organization's Github Gist. Now, we are ready to send data to the warehouse side of our Lakehouse. This can be done with the code below, which starts a Spark session, loops through all the GSOD tar files, extracts, transforms and sends it to our Iceberg table. from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Jupyter').getOrCreate()

objects = get_object_list(bucket_name, 'noaa/gsod')

for obj in reversed(objects):
  print(obj)
  df = tar_to_df(bucket_name, obj)
  table = pa.Table.from_pandas(df)
  pq.write_table(table, 'temp.parquet')
  df = spark.read.parquet('temp.parquet')
  df.write.mode('append').saveAsTable('noaa.gsod') The code in this section manually loaded data from a MinIO bucket. In a production environment, you will want to deploy this code in a service and use for automated ingestion. MinIO Bucket Events Querying the Iceberg Data Lakehouse using PyIceberg Let’s start a new notebook for reporting. The cell below imports the utilities we will need. Specifically, we will be using PyIceberg for data retrieval, Pandas for data wrangling, and Seaborn for visualizing data. from pyiceberg.catalog import load_catalog
from pyiceberg.expressions import GreaterThanOrEqual, EqualTo
import pandas as pd
import seaborn as sns

pd.options.mode.chained_assignment = None
catalog = load_catalog('default') What we want to do is calculate the average temperature per year for a given weather station. This gives us one number per year and takes into account all the seasons of the year. The first step is to query Iceberg for all data for a given station. This is done below using PyIceberg. tbl = catalog.load_table('noaa.gsod')

sc = tbl.scan(row_filter="station == '72502014734'")
df = sc.to_arrow().to_pandas()
df.head(10) The station id used in the code above is for a station located at Newark Liberty International Airport, NJ, US. It has been operational since 1973 (almost 50 years of data). When the code runs, you will get the output below. (I am using the DataFrame head() function to get a sample.) Next, we need to group by year and calculate the mean. Using Pandas, this is a few lines of code. No looping is needed. df['year'] = df['date'].dt.year
df = df[['year','temp']]
grouped_by_year = df.groupby('year')
average_by_year = grouped_by_year.mean()
average_by_year Once this cell runs, you will see a single value for each year. The top few years are shown below. Finally, we can visualize our yearly averages. We will use Seaborn to create a line plot. This takes just one line of code. sns.lineplot(data=df, x="year", y="temp", errorbar=None) The line plot is shown below. Another command you should always run after running a report for the first time is below. [task.file.file_path for task in sc.plan_files()] This is a list comprehension that will give you a list of all the data files in Apache Iceberg that have data matching your query. There will be a lot, even though Iceberg’s metadata can filter out many. Seeing all the files involved drives home the fact that high-speed object storage is an important part of a Lakehouse. Summary In this post, we built a Data Lakehouse using MinIO and Apache Iceberg. We did this using the GSOD dataset. First, raw data was uploaded to the Lake side of our Data Lakehouse (MinIO). From there, we created a database and a table in Apache Iceberg (the Data Warehouse side of our Data Lakehouse). Then we built a simple ETL pipeline to move data from the Lake to the Warehouse within the Data Lakehouse. Once we had Apache Iceberg fully populated with data, we were able to create an average yearly temperature report and visualize it. Keep in mind that if you want to build a Data Lakehouse in production, then you will need MinIO’s enterprise features. Consider looking at , , and . Object Lifecycle Management Security Best Practices Kafka Streaming Bucket Events Also published . here

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