The Varigence Blog

Implementing row condensing to only capture actual data changes during Persistent Staging and Data Vault loads.

How to perform a silent, unattended BimlFlex installation using a batch file for consistent and efficient deployment across multiple machines.

In the "Introduction to Databricks and BimlFlex Integration" session, attendees learn how to integrate Databricks with BimlFlex using a practical example. The presentation covers establishing connections to diverse data sources, efficient data storage using landing and persistent connections, and delves into advanced configurations within Databricks. The session also emphasizes building Databricks notebooks, optimizing Azure Data Factory pipelines, and managing data storage, staging, and archiving. Moreover, a detailed look into the Azure Data Factory instance highlights the significance of storing artifacts and scripts in GitHub for streamlined deployment.

BimlFlex ensures that businesses can tailor their data solutions precisely to their needs, ensuring efficiency and adaptability in an ever-evolving digital landscape

BimlFlex's next version will include Databricks automation support, extending to all data pipeline layers, and has received positive feedback from customers. This development can save time and costs, improve data quality, and provide robust data insights for businesses. An upcoming blog post will provide instructions on configuring and implementing a fully automated Databricks solution.

Discover how BimlFlex, a metadata-driven framework, can help you automate Change Data Capture (CDC) on Azure Data Factory (ADF), creating efficient, scalable, and reusable solutions with significantly less manual effort. Learn how to overcome common CDC implementation challenges, including full load or initial load and restarting CDC, and improve the overall efficiency of your data integration process.