- Streamline Databricks Workflows with Azure DevOps Release Pipelines
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The process of developing and deploying applications is complex, time-consuming, and often error-prone. The use of release pipelines helps to streamline this process and automate the deployment of code and data. Databricks is a popular cloud-based platform used for data engineering, data science, and machine learning tasks. Azure DevOps is a powerful tool for managing the entire software development lifecycle, including build and release management. In the blog “Streamline Databricks Workflows with Azure DevOps Release Pipelines”, we will explore how to build release pipelines for Databricks using Azure DevOps. We will look at the steps required to set up a pipeline for Databricks. By the end of this post, you will have a good understanding of how to build efficient and reliable release pipelines for Databricks using Azure DevOps.
Do you have a big data workload that needs to be managed efficiently and effectively? Are the current SQL workflows falling short? Writing robust Databricks SQL workflows is key to get the most out of your data and ensure maximum efficiency. Getting started with writing these powerful workflow can appear daunting, but it doesn’t have to be. This blog post will provide an introduction into leveraging the capabilities of Databricks SQL in your workflow and equip you with best practices for developing powerful Databricks SQL workflows