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Let's learn about via these 46 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Infrastructure As Code /Learn Repo 1. How To Configure CloudFront Using CloudFormation Template I recently worked on implementing CloudFront for s3 bucket files. Most of the tutorials were doing that using console management(UI) but we at Blue Sky Analytics prefer “code as infrastructure”. So I had to do this using CloudFormation. With the help of a few StackOverflow links and tutorials, I was able to write CloudFormation Template. 2. How to Test Terraform the Right Way In this blog post, you'll learn about why you should be testing IaC and the best platforms to get started with. 3. The Relevance of Infrastructure-as-Code Concepts in Security So what is Infrastructure-as-Code (IaC) anyway? IaC is a process that automates the provisioning and management of cloud resources. 4. Change the Name of your AWS S3 Bucket in Terraform Without Breaking Things Learn how to rename an AWS S3 bucket in Terraform. See renaming process of Amazon s3 bucket in a few simple steps. 5. Using a Product-Led Growth Mindset to Improve User Experience How changing the development mindset to a product-led-growth one can improve the user experience by focusing the thought process for the team's members. 6. What Really Propelled The Cloud Revolution: A Developer's Perspective An overview of the main trends that have pushed the Cloud industry — and the world — forward, in the past decade and a half. 7. Static Code Analysis  for Infrastructure as Code Using Azure DevOps Pipelines This article explains how to set up static code analysis for infrastructure as code using Azure DevOps pipelines. 8. 6 Free Terraform Tools to Facilitate a Multi-Cloud Infrastructure Have a multi-cloud infrastructure? These free Terraform tools will help you to reduce costs and make your multi-cloud efficient. 9. How to Create an Azure API Management Instance using Bicep Lang via Azure DevOps The more I use Bicep, the more I love it. This is what ARM Templates should have been. 10. Deploy Docker Using Ansible 11. Smart E-bike Kits: A Tour Inside Our Servers The Idea 12. Learn About Infrastructure as Code in 5 Minutes and Why You Should Use It Infrastructure as Code (IaC) is the process of managing and configuring an infrastructure using configuration files, rather than manually editing configurations 13. Improve Your IaC With a Specialized Management Platform Most often, there is no point in implementing an in-house solution as the cost and effort of building and maintaining it may exceed its potential benefits. 14. Managing a GitHub Organization With Infrastructure as Code This post will share our experience in adopting infrastructure as code (IaC) to manage GitHub organization resources. 15. Why Learn Any Other Programming Language When You Can Learn Typescript? Use TypeScript in all your projects and all use cases without any limitation. You can now do frontend, backend, infrastructure and machine learning in one progr 16. Code as Documentation and Assessment Documenting IT networking lab procedures in code rather than word-processor documents enables more authentic, equitable and consistent assessment. 17. AWS CloudFormation Template Basics Get started creating IaC using CloudFormation templates. 18. Why Understanding Infrastructure-as-code is Critical to Scaling Your Technology Infrastructure Infrastructure as code (IaC) is one of the most important trends in modern IT development. Read about the benefits of IaC and how to implement it. 19. How Infrastructure as Code Is Forcing a Revolution in Cost Management You might have read our beta launch blog post, which describes how Infrastructure as Code (IaC) is driving the third datacenter revolution (the first two being virtualization and the move to public clouds). In this post, I want to go deeper into looking at how IaC is driving a new need and approach to cost visibility and management. 20. How To Manage Infrastructure With Terraform What is Terraform? 21. Codify your SaaS Apps: The Answer to the Unmanaged SaaS Jungle A phenomenon we have encountered often, when helping companies overcome drift, is a common neglect of the entire SaaS toolchain. Learn why this is troubling. 22. Three Ways to Scale Your Apps with Render When your app is experiencing high traffic or utilization, you need to scale your service to handle that load. We’ll explore three ways to scale your apps. 23. Building Your Infrastructure With Just a Diagram This hands-on tutorial will teach you how to create infrastructure via the AWS Application Composer Console. 24. Check Out the Principles and Best Practices of Infrastructure as Code for 2023 Infrastructure-as-code is a very important concept to understand in the DevOps world today. 25. How To Migrate An Existing Infrastructure into Terraform Terraform is a powerful tool to have in your toolset. 26. Hashicorp's Terraform: A Introduction Join us in the journey of learning Terraform. This blog series aims to provides a guidance for essentials to be known while working with Terraform. 27. 2023 Will Be the Year of Kubernetes (and Other Predictions in the Cloud and Infrastructure Industry) Dive into the new year’s top cloud and infra-tech trends, straight from a cloud and infrastructure technology fanatic. 28. Introducing Driftctl: Your IaC Security Belt We recently released the first versions of driftctl, a new open-source project for infrastructure developers, DevOps, SRE, and cloud practitioners, with the goal of helping manage all kinds of drifts. 29. Cloud Application Infrastructure from Code (IfC) Presents a relatively new concept of Infrastructure from Code (IfC) as the next logical step in cloud automation 30. Project-as-Code: The Catalyst DevOps Needs If you are familiar with Docker, Terraform, and the CI platforms (eg: Jenkins, CircleCI, Codeship), you already know the power of Declarative DevOps. It can make development easier by being repeatable, predictable, and fast. Supporting technologies both hide complexities and offer important reuse by supporting simple structured syntax in an easy to create and read file. Each technology has codified much of its domain allowing developers to author and instrument with nothing more than a text editor. 31. I Got a New EKS Cluster Running And Terraform Code in 2 minutes AWS EKS + Terraform + Cloudskiff do the job 32. B2B Fintech Trends for 2021: Storage, Security, Cloud Migration, IaC As CTO at Altar.io, a large part of my work revolves around observing the forward-looking industry trends (including fintech trends) that affect how we create innovative products. 33. Why you should take care of Infrastructure Drift When talking about infrastructure drift, you often get knowing glances and heated answers. Recording gaps in your infra between what you expected to be and the reality of what is, is a well known and widespread issue bothering hundreds of DevOps teams around the globe. 34. The State of Cloud Automation and the Promise of Infrastructure Automation The Cloud has become a familiar feature of the tech stacks at most organizations and is becoming much more than a place for server resources. At present, almost everything in our digital landscape is connected to a Cloud in one way or another. 35. From “Feature branches” to “Feature environments” with Terraform “The advantage of feature branching is that each developer can work on their own feature and be isolated from changes going on elsewhere.” (FeatureBranch) 36. How To Setup Continuous Integration Pipeline By Using Terraform And GitLab CI Terraform is a fantastic tool for managing your cloud infrastructure, especially if your assets are hosted on multiple cloud providers. 37. Creating and Provisioning Azure Container Apps with Bicep Using Bicep, we can deploy Azure Container Apps quickly and easily! 38. Building a “Maintenance Mode” with Terraform and Github Pages As you may have seen, we just launched our public beta of env0 last month. As part of the run-up to the launch, our dev team had to go through and make sure everything about our infrastructure was ready for ongoing public use: one element being creating a maintenance mode for both our Application and our public API. 39. Application Deployment: Recipe for Great Serverless Strategy This is true for the most part, but Serverless apps also have a certain property that can make their deployment and maintenance time consuming. 40. Exporting Your GKE Cluster to Terraform Cloud: A Guide with Challenges and Solutions In this article, I'll share our journey at ANNA Money from using our Kubernetes installation in GCP to the managed Kubernetes service by Google and GKE. 41. What is Everything-as-Code? Examining  the Explosion of "as Code" Buzzwords If you’re confused when you read about “[some software term] as code” or “everything as code,” all you really need to know is that we’re talking about automation: The thing we use to do tedious tasks for us, or orchestrate tasks when they become too large and complex for manual methods. 42. Creating a Instance Scheduler using AWS CDK The AWS CDK is a software development framework to define cloud infrastructure as code and provision it through CloudFormation. The CDK integrates fully with AWS services and allows developers to use high-level construct to define cloud infrastructure in code. 43. Cloud-Native Yay or Nay: Reasons to Use Cloud-Native Technologies Cloud-native has been the talk of the town for quite some time now. Some developers think it’s just hyped way too much and will soon diminish from the limelight. Others think cloud-native will revolutionize software development and is here to stay! 44. Newsflash - "People with AWS Console Access Tend to Change Things" This blog post is a written transcript of the FOSDEM Talk: “Infrastructure drifts aren’t like Pokemon, you can’t catch ’em all”, by Stephane Jourdan – CTO and founder 45. How Do You Manage Helm Charts? Whether you love it or hate it, Helm is a ubiquitous tool for managing Kubernetes applications. You can use it in many different ways which is great, but can also be overwhelming. 46. Terraform Configuration Syntax Overview All you need to know to get started about Terraform Syntax. Thank you for checking out the 46 most read stories about Infrastructure As Code on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

46 Stories To Learn About Infrastructure As Code

Not all heroes wear capes. Some heroes are somewhere out there in our vast universe behind a computer screen, contributing to the of our tech community. greenness Regardless of the global difficulties that we faced in 2021, our internet heroes have been advocating for changes and innovations and reaching out to the world, from every nation, background, gender, and predisposition. In our #internetheroes category, we are celebrating small victories by creators, thinkers, makers who have dedicated their talent to innovations of the internet that significantly impact our lives for the better. Who are these heroes and how were they chosen? Discover them in the Annual Noonies Awards list! Here are the stats: . 4,961 nominees for 2021 586 Hacker Noon awards Across : 6 award categories , , , , , and . Decentralization Gaming Internet Heroes Internet Software Development Technology And a monumental over at 105,122 votes cast noonies.tech To see the winners simply visit , pick an award, and discover which name appears at the top. That’s the winner! noonies.tech The alone is announcing in different categories this year. 78 of the Internet! Internet Heroes 78 winners superhumans Who are they? Shall we reveal their identities behind their invisible capes? Best Use of Tech for Democracy has developed an app to track police brutality across the US, creating a new tool to keep the police accountable. Samuel Sinyangwe Best Use of Tech for Good is saving the planet, one dataset at a time. Blue Sky Analytics Green Tech Advocate of the Year The greenest tech awards goes to the most sustainable use of tech for the environment 💚🌱 Congrats to , CEO TreeApp, empowering people to plant trees via NGOs across the world. Jules Buker HackerNoon Contributor of the Year - AI Humans with irrational brains writing about machines with rational brains. A round of applause for with 448 votes, an euntrepreneur and machine learning enthusiast. @ilyshka HackerNoon Contributor of the Year - ALGORITHMS The Noonies celebrates the amazing people who power the technology industry, especially those who write. dedicated his time to make tech further our curiosity and creativity. Charlie G. HackerNoon Contributor of the Year - AMAZON Hey Alexa, send a cheer to writing on Business|Technology|FinTech! Sahana HackerNoon Contributor of the Year - ARTIFICIAL-INTELLIGENCE is rocking this with the passion to code, design, and create. Praneeth Guduguntla HackerNoon Contributor of the Year - AUGMENTED-REALITY The Noonies celebrates the amazing people who power the technology industry. Especially those who write! is leading this category on HackerNoon! Shannon HackerNoon Contributor of the Year - AUTOMATION "Automation may be a good thing, but don't forget that it began with Frankenstein." -- said an anonymous machine. Check out ’s stories! Ajay Kumar HackerNoon Contributor of the Year - BIG-DATA Gather and organize and process insights from large datasets with new computer strategies and technologies. Congratulations to of the uniqueness in big rata. Markle HackerNoon Contributor of the Year - BOTS won us over with his creativity! Tomas Sirio HackerNoon Contributor of the Year - CHINA writes about cyberspace, digital currencies, economics, foreign affairs, and technology Hugh Harsono HackerNoon Contributor of the Year - CLOUD It's raining tech stories and took the lead! Congratulations on this achievement 🌟 Vivian HackerNoon Contributor of the Year - CLOUD-COMPUTING Now it's raining cloud stories. is the proven platform to assess, develop, and validate hands-on technical skills. Cloud Academy HackerNoon Contributor of the Year - COMPUTER-SCIENCE won our hearts with 370 votes! Sergei Golitsyn " HackerNoon Contributor of the Year - CORONAVIRUS received 119 votes for amazing contributions! Alcor HackerNoon Contributor of the Year - CORONAVIRUS IMPACT ON BUSINESS received 119 votes for amazing contributions! Alcor HackerNoon Contributor of the Year - COVID 19 An astounding number of 1115 votes for our hero . 🥳 Brooks is an award-winning entrepreneur, speaker, and author. Michael Brooks HackerNoon Contributor of the Year - CROWDFUNDING is rocking this category! Nataraj HackerNoon Contributor of the Year - CYBER-SECURITY , a Cybersecurity expert and Ethical Hacker, won with tutorials on Cybersecurity. Kali Linux HackerNoon Contributor of the Year - CYBER-THREATS finds secrets in your code — before the bad guys do. Paul Price @ shhgit HackerNoon Contributor of the Year - CYBERSECURITY builds world-class Code Quality & Security tools: SonarQube, SonarLint and SonarCloud. SonarSource HackerNoon Contributor of the Year - DATA writes about data, how to manage it, how to do AI with it, how to make data-driven products! Ryan Dawson HackerNoon Contributor of the Year - DATA-ANALYSIS is a client-focused custom software development company. Imenso Software HackerNoon Contributor of the Year - DATA-ANALYTICS is an enterprise-grade solutions and platforms provider by using AI, ML, and NLP to solve business challenges. Xtract.io HackerNoon Contributor of the Year - DATA-PRIVACY is a Cybersecurity expert and Ethical Hacker who writes tutorials on cybersecurity. Kali Linux HackerNoon Contributor of the Year - DATA-SCIENCE is an enterprise-grade solutions and platforms provider by using AI, ML, and NLP to solve business challenges. Xtract.io HackerNoon Contributor of the Year - DATA-SECURITY Congratulations our contributor and tech director ! Mathias writes about Privacy, Productivity, Infosec, Death Metal, Photography, and good UX. Mathias Hellquist HackerNoon Contributor of the Year - DATA-VISUALIZATION , our Data Designer and Content Creator is leading the votes! Shachee Swadia HackerNoon Contributor of the Year - DATABASE Congratulations - Marvelous Senior Backend Software Developer! Oleg Stadnichenko HackerNoon Contributor of the Year - DEEP-LEARNING Votes go to , who described himself as in love with neural nets. He is VP of Product Delivery at Reface – AI-app to swap faces on videos, GIFs, and images. Alexey Chaplygin HackerNoon Contributor of the Year - DIGITAL-TRANSFORMATION 111 votes to - Alcor offers your own R&D center fully backed by our operational services. Alcor HackerNoon Contributor of the Year - DIVERSITY Congratulations to !!!!! .TECH Domains HackerNoon Contributor of the Year - DOTNET , Lead Software Engineer and Cyber Security Expert, won this category! Oleksii Holubiev HackerNoon Contributor of the Year - ELON-MUSK had the best stories 📚 congrats! Maria Stankevich HackerNoon Contributor of the Year - ENCRYPTION is a cybersecurity consultant, tech writer, and regular columnist for Savvy Security. Sam Patel HackerNoon Contributor of the Year - ETHICS surely inspired us again! Alexey is VP of Product Delivery at Reface – AI-app to swap faces on videos, GIFs, and images. Alexey Chaplygin HackerNoon Contributor of the Year - FINTECH grossed over an astounding number of 378 votes! A true internet hero 🦸‍♂️ Sergei Golitsyn HackerNoon Contributor of the Year - FUTURE studied Entrepreneurship and Strategic Communication at High Point University and is now Founder of BlueSkyAI. Mark McKinney HackerNoon Contributor of the Year - FUTURE-OF-WORK - congratulations on leading this category! kunal rajput HackerNoon Contributor of the Year - FUTURISM led this category, and is currently Coder and Author of Cryptocurrency Investing in the Age of Dollar Chaos. Anthony Watson HackerNoon Contributor of the Year - GROWTH An impressive number of 462 votes for Product Lead & Venture Founder ! Timoté Geimer HackerNoon Contributor of the Year - HACKING , congratulations on winning this category! SmartScanner HackerNoon Contributor of the Year - HARDWARE is a Co-founder and CTO at Integra Sources PhD in Physics and Mathematics, and also the winner of this category 🌟 Andrey Solovev HackerNoon Contributor of the Year - INFORMATION SECURITY is CEO & Lead trainer EH Academy (online cybersecurity training company by ). Irfan Shakeel ehacking.net HackerNoon Contributor of the Year - INNOVATION Impressive 251 votes! writes amazing articles about Domain. Sergei Golitsyn HackerNoon Contributor of the Year - INTERNET-OF-THINGS is a Market Research Analyst @Invozone. Sadia Mehmood HackerNoon Contributor of the Year - IOT - kudos for your amazing contribution! Lawrence Martin HackerNoon Contributor of the Year - LEAN-STARTUP 247 votes! is the Bestselling author of "Lean AI." Lomit Patel HackerNoon Contributor of the Year - MACHINE-LEARNING Congratulations to for leading this category! Neal Taylor HackerNoon Contributor of the Year - MATHEMATICS Sending our cheers to Technopreneur ! Sharad Sundararajan HackerNoon Contributor of the Year - MENTAL-HEALTH Psychiatrist and writer, , highlights topics of mental health in her contributions to Hackernoon. Her articles have featured ideas from caffeine overuse to accessibility. She is writing to help others with their mental health. Amy Shah HackerNoon Contributor of the Year - ML , Advisor| Author | Speaker | Teacher| Blogueur, congratulations for leading this category! Bertrand Jouvenot HackerNoon Contributor of the Year - MONEY An impressive number of 183 votes go to 🌼 Christian Kameir HackerNoon Contributor of the Year - OPEN-SOURCE has contributed to a variety of open-source projects and tools for the community and advanced them significantly this year. Stoplight From Elements for documentation to Spectral for automated API Style Guides and Prism for mocking, there are a variety of tools given to the developer community to be successful API design and developers. With their open-source October initiative, anyone at Stoplight or in the API community could contribute to pull requests on their open-source tools in exchange for 400 trees planted per pull request. Because of all the contributions to their open-source software, they were able to plant 10,000 trees. Go open source!! HackerNoon Contributor of the Year - PAYMENTS is the winner of this category 🔥 NOWPayments HackerNoon Contributor of the Year - PERSONAL-DATA is interested in Infosec & Biohacking, Security Architect by profession, and enjoys reading and running. Zen Chan HackerNoon Contributor of the Year - POLITICS is a Full-time Blockchain and Cryptocurrency nerd, Certified Blockchain Practitioner, and Certified Bitcoin Professional. Derek Lim HackerNoon Contributor of the Year - PRIVACY Congratulations to for changing the world one blog at a time. Haris Shahid HackerNoon Contributor of the Year - PSYCHOLOGY amassed a total of 400 votes! Congratulations for winning this category. Konstantin Sokolov HackerNoon Contributor of the Year - RASPBERRY-PI Congratulations, , Machine Learning Engineer for leading this section! Will Ho HackerNoon Contributor of the Year - REMOTE-TEAMS An impressive round of 1373 votes for , Co-principal at Response CRM, a fully remote business built and managed by a remote team since day one. Behzad Sharifi HackerNoon Contributor of the Year - ROBOTICS , Founder of NoteToServices, is the internet hero for this category! Matthew Gates HackerNoon Contributor of the Year - SCIENCE , congratulations for the deserving victory! Kahlil Corazo HackerNoon Contributor of the Year - SECURITY S : We build world-class Code Quality & Security tools: SonarQube, SonarLint and SonarCloud. onarSource HackerNoon Contributor of the Year - SMALL-BUSINESS delivers end-to-end design, web development, digital marketing, and email production services to businesses. Uplers HackerNoon Contributor of the Year - STARTUP The startup world is reading from our winner . Samiran Mondal HackerNoon Contributor of the Year - STARTUP-ADVICE Hey startup world, go to for stories on startup advice! Ritika Mehta HackerNoon Contributor of the Year - STARTUP-LESSONS There are more lessons in failure stories than success stories - ** covers the best ones this year! Ritika Mehta HackerNoon Contributor of the Year - STARTUPS R , congratulations on winning this category! itika Mehta HackerNoon Contributor of the Year - STARTUPS OF THE YEAR , CEO @Parabeac, thank you for your amazing stories! Ivan Huerta HackerNoon Contributor of the Year - STARTUPS TOP STORY Congratulations , Co-founder at , the new standard for open-source data integration. John Lafleur Airbyte.io HackerNoon Contributor of the Year - VIRTUAL-REALITY Our winner is ✨ metapunk HackerNoon Contributor of the Year - VR is rocking VR stories this year! Congratulations on the victory. Jack Boreham Health & Wellness Content Creator of The Year Congratulations , med student and health blogger who deserves this win of the year!! Juxtathinka HackerNoon Contributor of the Year - WEB MONETIZATION , congratulations on writing best web monetization stories! Sukhpinder Singh Loudest Tech Naysayer of the Year This one goes out to the rule-breakers - the "crazy skeptics", "cynical critics", and "persistent pessimists". And this year, our rule-breaker is ! Ashlee Marie Preston Rick Sanchez of the Year This prestigious award is intended to acknowledge the seemingly fine line between madness and brilliance. Sometimes having a perspective or drive that is outside the norm can be taunted and questioned, as seen in Rick and Morty and the Meaning of Life. But surpassing these challenges and creating something useful, brilliant, awesome, or just genuinely hilarious is a winning way to be. Congratulations , may you always be bold and quirky! Sergey Golitsyn TikTok's Crypto Czar The leader of the next generation's wealth discussion via the next generation's preferred medium - and the leads our category! TheBlockchainBoy Another big congratulation to all of our heroes 💚 Continue to shine with your energy and make the internet a better place to be in 2022! More Information: If you’re a winner, please answer ! If you’re a reader, participant, or just a curious individual, consider answering one of our other interviews . this interview here Read about the Noonies . To see all of the winners you may visit , pick any award, and view the name that appears at the top. here this site Or follow for posts with the list of names released over the course of the week of February 14, 2022. this account The will announce some of the top founders, hackers, and makers in all 6 categories. Noonies Winners Annoucement series Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Noonies Awards are sponsored by: , , and hosted by: Bybit Avast TECH Domains HackerNoon This post was written by and edited by An Le Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email . ellen@hackernoon.com

#Noonies2021 Awards: The List of Winners in the Internet Heroes Category

Data is at the centre of Blue Sky Analytics. After all, we ARE building a catalogue of environmental datasets. While working at Blue Sky, sometimes things can get quite hectic. From an outsider's perspective, it may seem like a team member is juggling various tasks at any given time. Even my colleagues and I, who make up the team of data scientists at Blue Sky, are rarely tied down to a single kind of assignment. Some days, I am working on building PoCs (Proof of Concepts) on the Jupyter notebook, while on other days, I am deploying these POCs into production. Some days are amazingly productive, where I end up devoting my entire time on the backend work of our , while on other days, I am left scratching my head over non-working code resulting from a single comma use! But one thing is for sure - geospatial refinery there is never a dull day at Blue Sky. Being a data scientist at Blue Sky has enabled me to embrace flexibility, have an open mind, and solve complex problems by using simple solutions. At the end of the day, I have learned that if you can achieve the desired result with just a Python or SQL query, do it! As Occam's Razor states, "The simplest explanation is usually the right one". If solving complex problems is not enough of an incentive, working at Blue Sky allows me to solve climate change - a clear and present threat to humanity's collective well-being. I believe Earth observation & geospatial analysis will play a crucial role in the coming decade. Geospatial data not only provides visual proof of what's happening around the globe but also links all kinds of physical, social, and economic indicators that help us understand what the past, present, and future would look like. Now that you know what I do at Blue Sky let me share how we do this. ⚽️ The Kick-off At Blue Sky, individuals from diverse backgrounds actively collaborate on various projects, sharing their sector expertise and varied experiences. This level of diversity also demands a lot of coordination in the workflow of a project. To achieve this, we follow a 5 step workflow that runs through the entire project timeline: Scoping, Research & Development, Data Hunt, Coding & Deployment and Generating Insight. On any given project, a data scientist might be involved in any one of these five steps. Below is a preview of what they look like. 🧠 Scoping We start with discussions and brainstorming sessions to broadly define the product and the possible datasets that can be a part of the product. These collaborations happen asynchronously on Notion or synchronously on a Tandem call. 🔬Research & Development Next, we run the possible datasets through several feasibility tests, including preliminary R&D. This process is focused on developing and testing the methodology to develop the product defined in the first step. It involves a thorough literature review and other forms of research to understand the existing body of work around the product we are developing. This is an important step in the grand scheme of things as it helps us evaluate the viability of developing a product. We are able to answer some of the critical questions/decisions regarding feasibility, data availability, ground-truthing & validation. All this requires extensive documentation, for which we again turn to Notion. 🔫 Data Hunt After understanding the science behind the problem, we start hunting for different types of input data that we would need to develop an MVP(Minimum Viable Product). Being a geospatial data company, while satellite data occupies a large part of our input data, it is not limited to just that. We also work with other types of data such as IoT data, weather data etc. At times, researching and aggregating the right kind of input data at desired spatial & temporal resolution can be a tedious task. While hunting for your data sources, one important thing to remember is that raw geospatial image files are notoriously large, making them hard to store and visualise (unless you like extremely slow loading dashboards). That's why we store them in Cloud Optimized GeoTIFFs (COGs). To learn more about COGs, you can watch this . video 👩‍💻 Coding and Deployment The next step is building a workflow that automates the data processing pipeline. Generally, the workflow contains standard GIS functions that turn the data into a standardised format. For e.g. it could involve , , etc. Our entire data workflow runs on AWS, which means that a data scientist at Blue Sky also needs to be familiar with AWS. projecting merging clipping In terms of our data stack, the complete data backend is written in Python because of its robustness to handle different kinds of datasets (geospatial or otherwise). Besides Python, we use Docker to test code in different environments and YAML to write configuration files for AWS. We also use for collaboration, tracking all our source code, and providing tools for task management. Github Post a complex process involving various tools, once everything is set up - which means the data has been processed and cleaned for exploratory data analysis and modelling - it's time to put that data to use! ⛏ Extracting Actionable Insight This is where we perform data exploration, apply statistical and machine learning models to derive valuable insights. We experimented extensively with our methods before coming up with the best solution for each particular use case. Through machine learning, we not only have the power to monitor ongoing events but also have the ability to anticipate future trends. We also maintain the highest standards when it comes to maintaining the accuracy of the model. The model before deployment goes through a number of cross-validation & refinement stages, helping to ensure high accuracy and flagging any anomalies early on. ⭐ Finally, we have a product! And finally, we reach the end of the tunnel with a product that can help change the world for the better. We currently have two data families - BreeZo and Zuri - that provide real-time insights on air quality and fires emissions data respectively. At Blue Sky, the plan is to fight climate change one product at a time.

A Day in the Life of a Data Scientist at a Climate Change Startup

I recently worked on implementing CloudFront for s3 bucket files. Most of the tutorials were doing that using console management(UI) but we at prefer “code as infrastructure”. So I had to do this using CloudFormation. With the help of a few StackOverflow links and tutorials, I was able to write CloudFormation Template. Blue Sky Analytics So What exactly CloudFront and CloudFromation are? These definitions will help you out. CloudFront: Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency, high transfer speeds, all within a developer-friendly environment. CloudFromation: On AWS, the CloudFormation service provides Infrastructure as Code capabilities. CloudFormation uses , configuration files defined in JSON or YAML syntax, that are human readable and can be easily edited, which you can use to define the resources you want to set up. CloudFormation reads a template and generates a , a set of resources ready to use on AWS. templates stack How is this gonna work? We are gonna use a private bucket, a public bucket can also be used but we don't use it to avoid direct access to bucket and leakage. CloudFront can access private bucket data using OAI(Origin Access Identity). An OAI is like a virtual user through which CloudFront can access private bucket. After creating OAI and using it in CloudFront, we need to update bucket policy, So that CloudFront with an OAI can access it. Resources we are going to build using CloudFormation: S3 Bucket OAI(Origin Access Identity). Update Bucket Policy CloudFront Distribution AWSTemplateFormatVersion: "2010-09-09" Resources: First, we need to define AWS template version and a "Resources" attribute to start writing resource templates. AWS template version defines the capabilities of a template. S3 Bucket Bucker resource template Bucket:
        Type: AWS::S3::Bucket
        Properties:
            AccessControl: Private
            BucketName: private-bucket
            Tags:
                - Key: description
                  Value: "Private files" "Bucket" is the name of resource template. We can use it any suitable name here. "Type" defines the type of resource, after this, we can define properties of resource. Each resource will have its own properties. OAI(Origin Access Identity) OAI resource template CloudFrontOriginIdentity:
    Type: AWS::CloudFront::CloudFrontOriginAccessIdentity
    Properties:
      CloudFrontOriginAccessIdentityConfig:
        Comment: 'origin identity' Update Bucket Policy Template resource to update bucket policy. BucketPolicy: Type: AWS::S3::BucketPolicy Properties: Bucket: private-bucket PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Principal: AWS: !Sub 'arn:aws:iam::cloudfront:user/CloudFront Origin Access Identity ${CloudFrontOriginIdentity}' Action: 's3:GetObject' Resource: arn:aws:s3:::private-bucket/* Two of the most important fields, here are AWS field under principle and Resource fields. Recently created OAI can be used directly by using its template resource name like this "${CloudFrontOriginIdentity}" CloudFront Distribution Now the final CloudFront Distribution resource template. One property you need to be careful here is "Origins", that basically defines your bucket origin and "DomainName"  will be like this "${bucket name}.s3.${region}.amazonaws.com". publicDistribution: Type: AWS::CloudFront::Distribution Properties: DistributionConfig: Origins: - DomainName: private-bucket.s3.us-east-2.amazonaws.com Id: S3-private-bucket S3OriginConfig: OriginAccessIdentity: !Sub 'origin-access-identity/cloudfront/${CloudFrontOriginIdentity}' Enabled: 'true' Comment: Some comment DefaultCacheBehavior: AllowedMethods: - GET - HEAD TargetOriginId: S3-private-bucket ForwardedValues: QueryString: 'false' Cookies: Forward: none ViewerProtocolPolicy: redirect-to-https ViewerCertificate: CloudFrontDefaultCertificate: 'true' After completing CloudFromation template, you can use sam command to deploy and check status of its CloudFromation stack. Sam command : sam deploy --template-file [sample-template] --stack-name [Stack name] --s3-bucket sam-test-bucket --capabilities CAPABILITY_IAM Few links to learn more: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-cloudfront-distribution.html https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/quickref-cloudfront.html Thanks for reading 💓️

How To Configure CloudFront Using CloudFormation Template

Cómo configurar CloudFront usando la plantilla de CloudFormation

is a big data and AI start-up that uses geospatial data to monitor environmental parameters. Our goal is to become the . Blue Sky Analytics Bloomberg of Environmental data for environmental monitoring, ESG (environment, social, and governance) due-diligence and climate risk assessment In other words, we aim to be the go-to source for all environmental data to drive sustainable decision making. We believe that safeguarding the global market against climate risk and other climate-change-induced threats requires temporally and spatially continuous high-resolution datasets that inform us of pollution levels, water quality, emissions, fires, changes in soil composition, etc. We asked ourselves, “what if there was a platform that presented environmental data the way we have access to financial data?” Sounds like an impossible dream, right? That’s why we need a technology stack that’s built to power our mission and a team too young to not know what’s impossible (our average age is 24.8). …and, our air quality and farm fire crisis solutions have won us (Healthy Cities Solutions) and the , so there’s power in believing in and pursuing what others may deem impossible. MIT Solve ‘Space Oscar’ from European Space Agency In this post, we’ll outline our technology — which handles historical as well as continuous data — and talk a bit about we’ve architected our platform to give spatial-temporal context to pre-existing data, what we’ve learned along the way, and what’s next. In short, you’ll get a glimpse of how we are making sense of terabytes of raw data and turning them into environmental insights. Our goal? You’re inspired to follow your dreams and build your own “impossible” projects. how What we do (and why we do it) We started this journey by creating a high-resolution database for monitoring air pollution in India, called , and we aim to cover surface water monitoring in 2020, land composition and urban informatics in 2021, and other environmental parameters in the coming years. BreeZo Currently, despite the alarming air pollution levels in India, monitoring is inadequate. There are , which are unequally distributed, and, as a result, large parts of the country go unmonitored. around 200 government monitors across India Our products aim to bridge this data gap by using satellite data and public monitors to provide a more comprehensive picture of the air pollution crisis.We make our data available to customers via accessible APIs and platforms; our approach centers on ensuring public access (democratising high-resolution environmental information) and facilitating policy-making and enterprise decisions (allowing corporations and government entities to understand current environmental situations as they craft initiatives). But, what we can’t monitor, we can’t solve. Climate change is a global challenge that we must come together to fix; it poses a huge risk and will have grave effects on public health, ecology and the financial system. Thus, it’s vital for governments, industries, and scientists to collaborate and come up with innovative solutions — leveraging AI, big data, and space technology — to prevent and mitigate negative outcomes. That’s where we come in: we create a platform that brings the much-needed high-quality data individuals need to make data-driven sustainable decision making, such as ways to transition from carbon-intensive economies to low-carbon approaches. The Tech Stack: How we do it and why our time-series database matters to us In order to create a large-scale environmental monitoring platform from geospatial data, we needed a database (specifically a time-series database (TSDB)) that could handle enormous quantities of spatial time-series data. We looked at various options, and landed on . Timescale DB The most common way to solve our “problem” is to use NoSQL databases, which can be treated as streams. However, that would mean that we would need to write our logic to implement the spatial and temporal context into our data. As we monitor environmental parameters using satellite data, the ability to handle and fill null values is especially critical. We had a lot of hacked together ways to query data with null filling and deal with data irregularities, using multiple cron jobs for each source and code at the application layer to do spatial queries. Essentially, we were spending a lot of time managing our time-series data and not enough time analysing it. For example: we wrote one script for null-filling, which was very slow to begin with — and we had to write it in bash, separate from our main app, to ensure it was performant enough and didn’t stop the main thread. So, NoSQL databases weren’t enough. But, what could we use? We looked at options like Amazon Timestream, but found that, while it works for IoT data, it didn’t work for our scenario: we handle not just IoT, but satellite data as well. For us, TimescaleDB proved to be particularly unique among TSDBs, allowing us to power spatial queries on time-series data and having its roots in Postgres. Some important features for our adoption were continuous aggregation, gap-filling null values, time bucketing, and, most importantly SQL support. Timescale has brought a change in our development paradigm, as it serializes everything in a timely order, and we can store, query, and analyze our temporal and spatial data using SQL (directly from the database). Example We have a database table where we store all the pixels at 10 kilometer grid for all of India, and we run spatial queries for individual states (taking into account cloud cover, which act as NULL values, and averaging them over that time). India is 3.3. million square kilometers, which comes out to be 33,000 rows of daily emission (with a 10 kilometer grid). Now, we can run a spatial join and average it over time. 3.3*1000000/10*10 = 33,000 Measurements (Raw satellite data), from multiple satellite sources downsampled to common denominator -- Measurements table
CREATE TABLE measurements (
	u_wind NUMERIC( , ),
	v_wind NUMERIC( , ),
	albedo NUMERIC( , ),
	aod469 NUMERIC( , ),
	aod550 NUMERIC( , ),
	aod670 NUMERIC( , ),
	aod865 NUMERIC( , ),
	aod1240 NUMERIC( , ),
	aod_s5p NUMERIC( , ),
	blh NUMERIC( , ),
	temperature NUMERIC( , ),
	recorded_at DATE,
	grid GEOMETRY
); 10 4 10 4 10 4 10 4 10 4 10 4 10 4 10 4 10 4 10 4 10 4 Maps of districts, states & region -- Enum shape types 
CREATE TYPE shapes_type AS ENUM ( , , , ); 
-- Create shape table 
CREATE TABLE shapes ( id UUID NO-T NULL, name VARCHAR( ), TYPE shapes_type, shape GEOMETRY ); for 'Country' 'State' 'District' 'Region' 255 Sample problem or analysis So, how would someone actually apply or use this information? Let’s say we’re a District Magistrate who wants to set weekly KPIs for emissions in an area where farmers burn crops on a daily basis. To get a baseline to inform our decision, we’d want to know the weekly emissions average emissions in each district over the last 3 months, focusing on those in our state. This is a pretty complex (and powerful) geospatial query, but it’s just one example of the type of analysis we do on a daily basis. Crunching 33,000, 3 30 data points SELECT
	name,
	-- Aggregates all the points one district
	json_agg(json_build_object( , datetime, , u_wind, , v_wind, , albedo, , aod469, , aod550, ,
 aod670, , aod865, , aod1240, , aod_s5p, ,
 temperature, , blh)) AS pollutants
FROM (
	-- Selects all the districts Punjab
	SELECT
		name,
		shape
	FROM WHERE
		TYPE = -- Selects all the state
		AND ST_WITHIN(shape, (
				SELECT
					shape FROM WHERE
					name = ))) AS Districts
	LEFT JOIN (
		-- Select all the points last week daily average and within Punjab
		SELECT
			time_bucket_gapfill ( , recorded_at, NOW() - interval ,
 NOW()) AS datetime, grid, avg(u_wind) AS u_wind, avg(v_wind) AS v_wind,
 avg(albedo) AS albedo, avg(aod469) AS aod469, avg(aod550) AS aod550,
 avg(aod670) AS aod670, avg(aod865) AS aod865, avg(aod1240) AS aod1240, 
avg(aod_s5p) AS aod_s5p, avg(temperature) AS temperature, avg(blh) AS blh
		FROM WHERE
			-- Get the points within week
			recorded_at < NOW()
			AND recorded_at > NOW() - interval -- Get the point only Punjab
			AND ST_WITHIN(grid, (
					SELECT
						shape FROM WHERE
						name = ))
		GROUP BY
			grid, datetime) AS Records
		-- Join the points based on geometry
		ON ST_Within(grid, Districts.shape)
		-- group them together
	GROUP BY
		name in 'datetime' 'u_wind' 'v_wind' 'albedo' 'aod469' 'aod550' 'aod670' 'aod865' 'aod1240' 'aod_s5p' 'temperature' 'blh' in "shapes" 'District' `Districts` in `Punjab` "shapes" 'Punjab' in with '1 day' '1 week' "measurements" 1 '1 week' in "shapes" 'Punjab' finally As mentioned previously, we collect an enormous amount of data from various sources, namely 1000+ ground monitors across India and satellite missions, like . To power our platform, we use TimescaleDB built-in functions to build chunks that allow us to store historical data in an easily accessible, scalable way. For example, we might have 12 values for March 1, 2014, but we only need for that day for historical analysis. Sentinel 5P one value When we started: We had 100s of rasters for all the satellite images we were ingesting. As a result, we wrote a custom Python script for every analysis, which was time-consuming and tedious. Post the TimescaleDB transition: We store all the ingested data in the database and simply extract raster values. We use geospatial JOINs, utilising TimescaleDB and PostGIS to combine geospatial data and time-series data and perform time-slicing. This allows us to gain insights and store them, make complex spatial-temporal queries, and carry out deeper analysis. Simply put, no more reading 100s of raster files or writing custom scripts for each analysis; we just dump into our database, and use SQL to analyze and find solutions. Compression of data and storage complexities are areas we no longer need to stress about. Moreover, we don’t have to worry about storing data in a time-series fashion, or optimization on that time-series data. And, as mentioned earlier, we’re able to efficiently handle null filling, data aggregation, and time slicing, making our development process significantly less complicated. All of this is to say that, if you’re building a data-intensive solution like we are or combining various types of data, TimescaleDB is a great place to start working with time-series data. It’s helped us expedite our mission of commanding the environmental data space, and the possibilities are endless: you can pace it according to your needs, as well as infinitely scale as you grow. Previously published at https://medium.com/blue-sky-thinking/global-environmental-data-stack-for-climate-action-a5c3ed494091

Building a Global Environmental Datastack for Climate Action

If you haven’t been living under a rock (or been on this platform at all), you may (read: will) be aware of the following events: Elon Musk bought Twitter for $44bn. He then renamed it X, a throwback to the X.com domain he has owned since the 90’s and the precursor to PayPal Now that that’s covered, some thoughts on why he did this, where it’s headed and how every user might get involved. Why Twitter? The party line in the lead-up to and ever since the Twitter takeover has been that Twitter is the world’s “digital town square”. Prior to acquisition, some stated concerns that the purchasing party had with the platform were its restrictions on free speech. In an ideal world, the town square is a place for open communication, serendipitous interaction and open commerce. Under the prior regime, Twitter displayed restricted characteristics of the first two features of the town square. My bet in writing this is that the vision for X will try and comprehensively and unapologetically service all 3. Twitter has a number of characteristics that make it uniquely ripe for a platform mission of this scale. It is a unique source of real-time news - no other platforms (with possibly the exception of TikTok) are as uniquely geared to delivering a stream of takes as soon as action happens in the real world. As far as social platforms go, it is a unique source of understanding people’s unconscious thoughts - there is less filter (and substantially more shitposting) on Twitter than other platforms. These two characteristics bear a lot of similarities to WeChat, a platform for which Musk has expressed in the past. Similar to Twitter, WeChat’s origins were in an unfiltered* feed of people’s messages benefitting from ‘gotta-have-this’ network effects. admiration Through this stream of low-effort, low-barrier user generated content, WeChat was able to easily become a place for the breaking of news in real time. These elements created an extremely valuable network of data. The logical next step from here was to introduce the launch of ‘mini-apps’ - platform-native applications built by third-party developers to utilise WeChat’s network to offering augmented possibilities within the platform to compete with, well, every other tech company. X could be a vision of WeChat built for a more independent, more global audience. As such, while the platform structure may remain similar, the apps may look very different. This, in my opinion, is what so many fail to understand about western ‘replications’ of these super-apps being ill-fated (excuse the meme below). What’s been done so far? Since Elon’s takeover, a few defining changes have been made to the platform that may be indicative of longer-term strategic moves. Creators can charge whatever fee they want for their ‘true fans’ to access exclusive content. Creator Subscriptions. A decentralised form of moderation that allows users to provide additional context to potentially misleading Xeets. This Community Notes. Twitter extended the long-form tweet limit to 10,000 characters. It also expanded its video capabilities, as best exemplified by Tucker Carlson’s relocation of to the platform. Extended Content Limits. his content Payouts to creators based on their contribution to platform ad revenue, denominated as a % of ad revenue derived from ads placed in the replies to their Xeets. Ad-sharing agreements. This is the big one. Reducing the prominence of scams and bots is the obvious first-order effect. However, it also could serve a broader economic purpose. The first generation of consumer-ready LLMs benefitted from having free reign over the internet to train their models over whatever data they wanted. This has become the subject of major copyright cases. X’s API restrictions force those looking to scrape the platform for data to pay for the contributions X’s rich dataset makes to their LLMs (or similar), creating a fairer economic model for all involved. The logical next (creator-friendly) step would be, as they have done with ad sharing, to payout network participants for what percentage of their data is being paid for as part of these API charges. API Restrictions & Rate Limits. What’s next? I don’t hold out hope for X to become a fully decentralised protocol in the manner of the Dorsey-era Bluesky initiative. However, given Elon’s ambition starting this foray into social, I do expect it to become platformised. This (fairly aggressive) assumption is based off of two things: 1) Elon’s long-stated ambition to build a super-app of some variety, and 2) the re-application of the X.com domain to open up pursuit of old goals. ‘X-as-a-Platform’ in this sense isn’t referring to you just being able to Xeet whatever you want, whenever you want on the platform (as has always been the case). Instead, I refer to either of these platform definitions of choice: A venue for creating and facilitating interactions between different groups (e.g. buyers and sellers, but not limited to this) A stage from which applications can be built. Based on what we’ve seen and heard so far, a lot of the plumbing will be done by Twitter themselves. Already we have heard of them allude to in-house features that will “allow people to conduct their entire financial world” from within the platform. It has arguably made the greatest leap of any mass-scale social platform in terms of rewarding creators via the ad revenue sharing system introduced this month. As talented as the remaining engineers at Twitter may be, the scope for their delivery of novel products & tools is no match for the widespread creativity and productive powers of As such, I believe that to build the ‘financial world’ of Musk’s imagination, a real platform strategy is in order. Where is the low hanging fruit to be built for this ecosystem? What might be built for the X economy? Novel transaction rails for novel interactions The idea of “conduct(ing) your entire financial world” from within X is a headline grabbing concept. To enable this first and foremost requires rails for processing these financial transactions. Cryptocurrencies (coin-agnostic) offer a perfect manner for doing this in a censorship-resistant, self-custodial and programmable fashion for users on the platform. Payments for creator subscriptions or Reddit-style rewards do not have to be done via some meaningless platform-native token, but rather in the self-custodial currency of choice that users can hold and transact via X. Rather than having to deal with the muddle of SWIFT codes, bank account numbers or even wallet addresses, users will be able to direct transactions simply via account handles. Feature Markets Got a request for feature on Twitter? Say, for example, I want to be able to create debate markets within comments sections, whereby comments are sorted by Agree/Disagree sentiment to a statement tweet. The most liked comments on either side of the argument are presented side-by-side at the top. I want the winner of this debate to be able to extract a reward. Even if it is not a central priority for Twitter to develop internally or rollout across the platform, why shouldn’t the developer be able to deploy these kinds of features in a sandbox environment to a group of opt-in users that are interested in seeing these kinds of i) experimental features or ii) balanced idea markets? After deploying from a developer suite (which could be a paid service based on its potential economic upside), developers stand to win rewards based on any of i) revenues from any features that they create with some kind of financial element or ii) the portion of total Twitter revenue derived from engagement with these features (including ads). makes this idea sound a bit more novel than it really is - the reality is that they are extremely analogical to marketplaces on platforms like WordPress, Shopify or even Google extensions. For whatever reason, such functionality has been limited to date in the world of social. Outside of social, it is the backbone of gargantuan markets: while maybe a bit broader in scope as a platform, Apple’s App Store contributed $86.8bn in platform revenues in 2022. Feature markets IP & Data Licensing Mechanisms Beyond the ad revenue sharing scheme that is in place at the moment, there should be other ways for creators to monetise their content (particularly for creators whose content is ripe for being repurposed elsewhere). Further, in order for developers to benefit from the potential programmability of X’s data, there need to be responsible, legally viable frameworks in place for use of customer data. Arweave’s Universal Data License ( ) represents a good proxy of these kinds of systems in that it allows creators to dictate how any given piece of content can be used or remixed whilst earning rewards when it is used for external purposes. UDL Custom algorithms For all the work that X’s algorithm puts into keeping you delighted, it is inevitable that everyone gets annoyed with their stream. If I have a day where I want to look at golf content, I don’t want to be tagged as wanting to view that for the rest of my life. If I’m using Twitter for professional purposes but got easily distracted by entertainment news every now and then, I don’t want these 5-minute forays to destroy Twitter as a professional tool for the rest of my career. Sure, features like Lists etc all serve to give the user input into what enters their feed, but this then has the effect of limiting the amazing serendipitous post that pops up in your feed that makes Twitter such a great platform. Enter custom algos. Developers can play around and deploy different thematic algorithms for users to test. In one example, developers may able to create a proxy of what any given influencer’s feed may look like. I, for one, would be very curious to see what Elon’s doomscrolling experience looks like compared to mine. At the cross-platform level, this would likely be a read-only function. It is hard to imagine X giving up the reigns to what kinds of content get rewarded or distributed at scale. However, once advanced community tools are built-in, why not allow developers to play with different reward mechanisms for different communities? The average Crypto Twitter user likely gets value out of very different styles of Tweets/replies than, say, someone on Movie Twitter. Once these communities can be effectively delineated, a lot of design space opens up for novel reward mechanisms. To date, Twitter’s own pre-Elon development has been the first and only mover in developing these kinds of ‘algorithm marketplaces’. This will be an interesting space to watch. How big this market could be is dependent on your views towards what portion of the $231bn annual social media market is determined by algorithms versus other factors such as marketing or community. Bluesky Cogsec Apps Ever since the events of 2016, misinformation and informational integrity have been core themes in the social space. How can we better protect people against this kind of misinformation? Do people even want to be protected or have balanced opinions? Creating an open market for cogsec applications that act as a form of ‘cybersecurity’ against misinformation would be a boon to any platform’s claims of being a bastion of free speech. These tools can adopt a variety of different features to de-bias certain pieces of information. Much of this is very speculative, and would require user opt-in to avoid being entirely Orwellian: content filters to repackage tweets with less emotional appeal whilst maintaining integrity of the original message or reputational slashing for accounts that are repetitively tagged as producing misleading content by Community Notes contributors are two examples. Community Notes is actually a step in the right direction as far as cogsec goes on the platform - the most obvious extension here is to allow (and possibly even encourage) users to moderate i) Community Notes themselves and ii) any measures taken by X as a platform to censor certain content on the platform. Beyond this, the idea of rewards for reputable and reliable user contributions to Community Notes seems a likely extension of this first-phase of platform revenue sharing mechanisms. Commerce Infrastructure For most, buying useless shit is a key tenet of the operation of their financial world. As such, ‘arm-the-rebels’ style commerce tools are likely to creep into the X platform (apologies, Shopify). I believe that, first and foremost, we are not far away from centralised features that allow people to sell directly from their profile or embed checkouts within tweets. Better Community Tools Community building on X today is mostly a function of its content algorithm. I like startup content, I fall in with startup people on Twitter. How can this be made more proactive, and how can these small subcultures on the platform create their own mini-apps without having to secede from the platform to places like Discords or Slacks? To the above, I think it is likely that X will enter the work communications space for remote teams. With this comes the opportunity to rebuild the kind of plugins and additional apps seen on Slack, Discord and their ilk from the ground up. Prediction markets & other voting mechanisms In these early days of Elon-era Twitter/X, we have seen many company-level decisions be informed by simple Yes/No polls from his handle. How much further can these kind of trends go across a broader range of potential applications? Prediction markets have been a polarising topic since the first large scale, skin-in-the-game markets were launched around the time of Ethereum (Augur being the first mover in this case, followed by successors such as Polymarket, and to name a few). Manifold Markets Kalshi If these products failed to achieve breakout growth, what makes a prediction market on X any different? The first factor is . The aforementioned existing prediction market platforms are used by small, esoteric audiences seeking gains from supposed informational advantages. X.com has an inbuilt audience of ~330mm monthly active users who are on the platform because they have opinions (or want to find new ones). What better way to prove your value in the market of opinions than creating financially staked markets from it? distribution The second factor is X’s now proven ability to . Similar to the ad revenue sharing scheme, prediction markets features on Twitter could work by allowing users to create their own markets based on assertions they make in their Xeets. If users make sufficiently provocative or challenging assertions, one would assume that the prediction markets for these assertions are particularly hot and might allow the original author to take a share of the market for their provoking thoughts. share revenue with its creators This creates a financial incentive for creators to publish thought-provoking mini-content on the platform, and allows ‘reply guys’ to also get in on the action by providing opposing/supporting evidence to influence their side of the prediction market. In this model, these prediction markets can be organised more organically and allow for second-order pieces of information supporting or opposing the cause to play an influential role in market outcomes. Novel platform analytics One of the things that web3 has done best (and has been best for crypto twitter) is the depth of analytics openly available to anyone in the space. Data from block explorers can be repackaged and contorted into easily readable formats for anyone interested to easier digest exactly what is happening on chain. Nothing like this has achieved success at scale for social. Sure, anyone with the requisite level of skill can develop their own sentiment analysis programs or data visualisation techniques based on what is happening, but platforms don’t make it easy for the everyday user to play around with and explore different elements of platform activity. Why not introduce -style analytics platforms for granular data on social platform activity? At the topline, there would be full transparency around platform activity - for example, how likes/RTs/replies are distributed among power users of the platform or how bot activity is trending over time. Where it gets very interesting is if these platforms can then open-source sentiment analysis tools. In this world, we could see analysts developing publicly available dashboards displaying changes in tone from different account categories over time, common triggers of trends and much much more. Dune

The Economy of Elon Musk's X

ইলন মাস্কের এক্স এর অর্থনীতি

Die Wirtschaft von Elon Musks X

La economía de la X de Elon Musk

L'économie du X d'Elon Musk

एलोन मस्क की एक्स की अर्थव्यवस्था

イーロン・マスクのXの経済

Elon Musk의 X의 경제

A economia do X de Elon Musk

Elon Musk'un X'inin Ekonomisi

Nền kinh tế của Elon Musk's X

埃隆·马斯克 X 的经济

Nowadays analyzing big data has become a significant business tool. It’s constantly changing the way brands operate and market their products across various fields and industries. In fact, every company today, regardless of size, is considered to be a data business in some measure. Therefore having a well-thought-out data strategy is ultimately important in case you want to achieve success these days. So if you’re still drowning in statistics trying to collect as much information as possible, stop. Here are some basic guidelines on how to use data analytics to your advantage and help your business thrive. Upgrade user experience There are hundreds of ways that can help you create the best UX possible. But one of the most effective and accessible tools any company can use to enhance user experience is . data All because it allows building better consumer profiles. Gather information from your website, analyze the Facebook page or any other web-based content of yours and voila! You have a comprehensive overview of who your clients are, what they like, when they attend your platform, and, what is more essential, how they perceive your brand. Of course, it’s not as simple as it seems. Because — surprise — this information is absolutely useless unless you learn to extract the specific patterns of customer’s behavior that affect user experience. But once you find them, you’re good, having brilliant data-driven engaging UX that can easily win customer loyalty. Deliver superb client service Processing information in real time is essential for top-grade service. With the help of big data, you can send product notifications once your clients enter the mall, navigate them to the store or even identify their preferences in one click. http://fortune.com/2017/06/08/nordstrom-weighs-going-private/ And if it sounds too futuristic and non-viable to you, take a look at such brands as Nordstrom. This fashion retailer into their business strategy. For instance, their shops are equipped with digital devices that give recommendations based on the client’s purchase history both online and offline — practically replacing live personal shoppers. has already integrated data insights Optimize prices With the help of data analytics, you can constantly adjust the prices to the current situation. It helps you monitor the competitor’s pricing, growth or decline in demand, customer interest, market trends and so on. The examples are countless: e-commerce stores that give a last-minute discount to avoid customer leaving the shopping cart, airlines that change ticket cost according to destination’s popularity or hotels that lower their prices during a storm. All those manipulations help not only maximize the income but even discover certain buying patterns that can play a major role in a company’s future. Prevent customer turnover https://blog.rjmetrics.com/2013/01/08/ecommerce-churn-rate/ Losing customers? Use a predictive churn model to find out who of your clients are about to break ties with your company and why. This technique processes historical purchase and behavior data of the lost customers, arranges it into a pattern and searches for a match. If some correspondence has been found, the tool indicates the clients who are at risk, giving the company a chance to initiate remedial actions. Plus, it helps business owners understand the main reason of churn, which in some cases may be extremely hard to define. Personalize and customize While most of your competitors use “one size fits all” business strategy, you can create experience perfectly tailored to your consumer’s needs and interests . Processing big data analytics allows you not only personalize service and communication but deliver unique custom products. As Jim Vernon, CEO and founder of first AI-based custom jewelry company and the first worlds digital diamond expert , says: “Customers are tired of going to retailers and simply picking a ready-made engagement ring off the shelf, almost feels anti-climatic to them; they want something special made just for them”. RockHer Rosi Forecast demand more accurately Thanks to data analytics, nowadays companies can go far beyond outdated demand forecasting techniques. Instead of using obsolete methods built upon the age-old adage, you can apply big-data advanced analytics and radically improve the accuracy of your predictions. It is especially helpful for big supply chains and large retailers that need to decide precisely how much, when and which products should be stocked in inventory. Optimize delivery and distribution https://images.unsplash.com/photo-1494412685616-a5d310fbb07d?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=750&q=80 By monitoring GPS and telemetry data along with real-time traffic and weather situation you can improve the quality of your delivery service. For instance, find optimal routes, shorten order-to-delivery time or even foresee the upcoming orders. “How is it possible?”, you would ask. Well, take a close look at they have patented nearly four years ago. As soon as you realize that big brands already use and benefit from it, the idea of shipping products before they have even been ordered won’t seem to you so blue sky anymore. Amazon’s anticipatory shipping system Boost advertising effectiveness Big data analytics also helps business owners analyze marketing channels and spread the financial resources accordingly. So forget about wasting budget on ineffective advertising. Instead, analyze the behavior of each consumer and use click-by-click data to identify what ads your customers better respond to. If you know that third of your clients ignore web banners, but read the personalized notifications about the same product, you would definitely think twice before spending money on those banners again. To sum it up Using data analytics is a killer business strategy. Even though it takes time and lots of effort to figure it out, it gives your priceless insights and opportunity to achieve your business goals faster than ever before. We all understand that the days of relying on gut feelings in business have passed. The era of “intuition is everything I need” has ended and today nothing works best than data-driven strategy and analytics. So don’t be afraid to experiment and step out of your comfort zone. Use data analytics to your own benefit and revolutionize your business while others bury their head in the sand.

How Data Analytics Can Revolutionize Your Business Strategy and Unlock Success

Before I tell you how The Price has saved the Kingdom of DeFi, here is a story of how it all began. PureFi Once upon a time, in the faraway Kingdom of DeFi, a massive and explosive change took place. All the holders were happy since the growth of the Kingdom was driven by a massive influx of liquidity in the market. During these happy times, anyone was able to go through a simple process of exchanging their money for a coin without completing any KYC/AML verifiable processes. Then, invest the coins in a project with great potential, and, crossing fingers, watch the investment grow. In other words, holders could easily create wallets and start trading without disclosing any personal data. The Kingdom of DeFi was prospering as its technology could open up an enormous amount of opportunities for honest holders, up until one day, the evil bad actors came and occupied the Kingdom. Some of the honest market players stopped trading entirely and the rest kept going but wouldn’t stop complaining about having sleepless nights, worrying whether their coins were “pure” enough. The pure blue sky was covered with heavy gray clouds. The bad actors were the curse of the Kingdom because they served a function of laundering “dirty money” and systematically put the good actors and their financial activities at risk. Kingdom’s local police called FAFT couldn’t recognize the good actors from bad ones anymore. The Kingdom of DeFi was in complete chaos. Even the good actors who added a tangible value to the rebuilding of the global financial system could be approached and denied any further activity related to DEXs or providing liquidity to liquidity pools by FATF. Truth be told, the legends about evils like were horrifying! Around $32 million were taken away from the investors just like that! Unfortunately, the investors were not the only ones at high risk. DeFi100 Holders unwillingly took the high risk of picking up someone else's “dirty” money trail once they decided to withdraw their assets back. Liquidity pool operators took responsibility for the funds that the pool held. The value of the product was completely demolished once it wasn’t credible for honest holders anymore. It was especially hard to earn users’ trust when the product didn’t comply with money laundering and governmental KYC regulation. There was no way DEX traders could classify coins on their own. By only keeping the history of each transaction, no one was able to defend whether the buy or the sell was “clean”. Prince , the one-step compliance protocol for decentralized finances, was developed by AMLbot as a project of Hacken. He aimed to provide a full-cycle solution for crypto asset analytics and AML/KYC procedures on the DeFi market. PureFi PureFi connects provides crypto asset analytics to protect liquidity pool users and honest market players. Analytics results are available via VC (verifiable credentials). Each VC certificate contains information about asset price, AML/KYC, and other metadata - so it can be used as a full-fledged AML/KYC verification for any legal/validation issues. The content of the certificate is encrypted and available only from the public key list (whitelist). Whitelisted participants get data from the certificate with zero-knowledge proof (ZK SNARK). In this way, the VC certificate empowers its owner to prove to the verifier that a statement is true without disclosing any facts excluding the statement’s validity. This article is part of the hosted by HackerNoon in partnership with . Decentralized Internet Writing Contest Free TON Join the contest for your chance to of TON crystals every month. win up to $1500 worth Looking for more articles like this? Be sure to subscribe to our newsletter in the footer below!

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