Achieving end-to-end lineage in Databricks while allowing external users to access raw data can be a challenging task. In Databricks, leveraging Unity Catalog for end-to-end lineage is a best practice. However, enabling external users to access raw data while maintaining security and lineage integrity requires a well-thought-out architecture. This blog outlines a reference architecture to […]
Posts Tagged ‘Delta Live Tables’
Deletion Vectors in Delta Live Tables: Identifying and Remediating Compliance Risks
Deletion Vectors will be enabled by default in Delta Live Tables (DLTs) for materialized views and streaming tables starting April 28, 2025. Predictive Optimization for DLT maintenance will also be enabled by default. This could provide both cost savings and performance improvements. Our Databricks Practice holds FinOps as a core architectural tenet, but sometimes compliance […]
Delta Live Tables and Great Expectations: Better Together
Modern data platforms like Databricks enable organizations to process massive volumes of batch and streaming data—but scaling reliably requires more than just compute power. It demands data observability: the ability to monitor, validate, and trace data through its lifecycle. This blog compares two powerful tools—Delta Live Tables and Great Expectations—that bring observability to life in […]
Integrate Salesforce and Databricks
90% of Fortune 500 companies use Salesforce as their Customer Relations Management tool. I have ingested data from Salesforce into almost every database using almost every ETL tool. Every integration tool out there has a Salesforce connector; Salesforce even owns Mulesoft. The integration always worked, but it was rarely smooth. Its just something that you […]