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Continuous Integration and Continuous Deployment using Databricks ASSET BUNDLE #databricks #data You tube link - https://lnkd.in/g5w_XfYT Databricks Assets Bundles are an infrastructure-as-code (IaC) approach to managing your Databricks projects. Databricks Asset Bundles are a tool to facilitate the adoption of software engineering best practices, including source control, code review, testing, and continuous integration and delivery (CI/CD), for your data and AI projects. Bundles make it possible to describe Databricks resources such as jobs, pipelines, and notebooks as source files. These source files provide an end-to-end definition of a project, including how it should be structured, tested, and deployed, which makes it easier to collaborate on projects during active development. This video demonstrates how to deploy delta live table pipeline and workflow with asset bundles!

Deploying robust Data & AI assets on the Databricks Intelligence Platform can be enabled by Adopting Software Engineering best practices such as code versioning, testing, code packaging, and Continuous Integration and Delivery (CI/CD). In this article, I share with you how to leverage the latest tool for achieving such goal: Databricks Assets Bundles (DABs). Happy Bundling ! https://lnkd.in/dnnTkKrB

Curious to learn more about how CI/CD processes work with #MicrosoftFabric #DataFactory pipelines? Take a look at this wonderful new article by Connie Xu that walks through the basics of CI/CD with pipelines in Fabric! Thank you to Sean M. for your insights as always. https://lnkd.in/g6AFsjxK

Here’s an intriguing insight from a recent read on CI/CD in Databricks. Did you know that using CI/CD can help automate the entire process of software development and delivery? 🚀 By integrating short, frequent cycles and automation pipelines, teams can boost their reliability significantly compared to manual processes. This article emphasizes how crucial CI/CD is becoming, especially in data engineering and data science. With tools like Databricks Asset Bundles, project deployments can be managed effortlessly, allowing teams to focus more on innovation rather than tedious configurations. What are your thoughts? Is CI/CD already a part of your workflow? How do you think it can enhance data projects? #DataScience #DataEngineering #CICD #Automation #Databricks https://lnkd.in/gm7SpKHQ

Databricks DLT pipeline development made simple with notebooks No more context switching: develop your DLT pipelines in one single contextual UI and Catch errors faster and easily develop DLT code #databricks #dlt #datapipeline #dataengineering #databrickslearning

What are Databricks Asset Bundles and how to use it to improve your workflows. Databricks Asset Bundles are collections of job configurations, libraries, and other resources packaged together. They facilitate the automation and versioning of jobs, ensuring that your data workflows are reproducible and maintainable. With Asset Bundles, you can define the entire setup for your jobs, including cluster configurations, library dependencies, and execution parameters, all in a single place. Benefits of Using Asset Bundles ● Simplified Management: Bundles centralize all configurations, making it easier to manage and update workflows. ● Automation: Automate the deployment and execution of jobs, reducing manual intervention. ● Version Control: Keep track of changes and versions of your jobs and pipelines, ensuring consistency and reproducibility. ● Scalability: Easily scale your workflows by leveraging serverless compute resources, optimizing performance and cost. Real-Life Use Case: Managing a Serverless Pipeline using Databricks Asset Bundles Consider a scenario where you need to run a data pipeline on serverless compute resources to process streaming data. By utilizing Databricks Asset Bundles, you can enhance the efficiency and reliability of your data workflows, making it easier to manage and scale your projects.

Do you know how to manage DataOps in your organization? Here's a breakdown of Data and DataOps fundamentals - ✅ Continuous Integration - From committing to a pull request (PR) to validating and building artifacts, ensuring all changes are integrated continuously ✅ Continuous Delivery - Configuring deployment through various stages (DEV, STG, PROD) with approval gates and key vault configurations ✅ Development Process - Using a sandbox environment for individual developer workspaces and uploading notebooks and packages to Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) ✅ Deployment - Moving through DEV, STG, and PROD resource groups, deploying DACPACs, uploading notebooks, and ensuring integration tests run smoothly What challenges do you face in your DataOps journey? #data #dataops #fundamentals #theravitshow

Do you know how to manage DataOps in your organization? Here's a breakdown of Data and DataOps fundamentals - ✅ Continuous Integration - From committing to a pull request (PR) to validating and building artifacts, ensuring all changes are integrated continuously ✅ Continuous Delivery - Configuring deployment through various stages (DEV, STG, PROD) with approval gates and key vault configurations ✅ Development Process - Using a sandbox environment for individual developer workspaces and uploading notebooks and packages to Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) ✅ Deployment - Moving through DEV, STG, and PROD resource groups, deploying DACPACs, uploading notebooks, and ensuring integration tests run smoothly What challenges do you face in your DataOps journey? #data #dataops #fundamentals #theravitshow

Another addition in #databricks CI/CD feature and that is Databricks Asset Bundles to streamline the development of complex data, analytics, and ML projects for the Databricks platform What are Databricks Asset Bundles? https://lnkd.in/g-z4qgHF #yaml #cicd #databricks #infrastructureascode

Smart Claims for Insurance End to End project from Databricks Imagine having pre-built code, sample data, and step-by-step instructions all set up in a Databricks Notebook. This Smart Claims for Insurance End-to-End project is a game-changer for the industry. Here’s why it stands out: - Utilizes the Lakehouse paradigm to automate components aiding human investigation. - Employs Databricks features like Delta, DLT, Multitask-workflows, ML & MLFlow, and DBSQL Queries & Dashboards. - Unified Lakehouse architecture allows all data personas to work collaboratively on a single platform, contributing to a single pipeline. This comprehensive solution accelerates workflow efficiency while ensuring seamless collaboration. Kudos to Databricks for this innovative accelerator! Any interesting End to End Data engineering projects you came across recently, please share ? The link to the Databricks notebook is in the comments: