Generating Coding Tests for LLMs: A Focus on Spark SQL
Introduction Applying Large Language Models (LLMs) for code generation is becoming increasingly prevalent, as it helps you code faster and smarter. A primary...
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Training Highly Scalable Deep Recommender Systems on Databricks (Part 1)
Recommender systems (RecSys) have become an integral part of modern digital experiences, powering personalized content suggestions across various platforms. These sophisticated systems and...
How to perform change data capture (CDC) from full table snapshots using Delta Live Tables
All the code is available in this GitHub repository . Prior to reading this blog we recommend reading Getting Started with Delta Live...
Ingest data from SQL Server, Salesforce, and Workday with LakeFlow Connect
We’re excited to announce the Public Preview of LakeFlow Connect for SQL Server, Salesforce, and Workday. These ingestion connectors enable simple and efficient...
DLT pipeline development made simple with notebooks
We’re just a couple weeks removed from the biggest Data + AI Summit in history, where we introduced Databricks LakeFlow , a unified...
Introducing Databricks LakeFlow: A unified, intelligent solution for data engineering
Today, we are excited to announce Databricks LakeFlow, a new solution that contains everything you need to build and operate production data pipelines...
Introducing the Open Variant Data Type in Delta Lake and Apache Spark
We are excited to announce a new data type called variant for semi-structured data. Variant provides an order of magnitude performance improvements compared...
Databricks Assistant Tips & Tricks for Data Engineers
The generative AI revolution is transforming the way that teams work, and Databricks Assistant leverages the best of these advancements. It allows you...
Announcing the State Reader API: The New "Statestore" Data Source
Databricks Runtime 14.3 includes a new capability that allows users to access and analyze Structured Streaming 's internal state data: the State Reader...
PySpark in 2023: A Year in Review
With the releases of Apache Spark 3.4 and 3.5 in 2023, we focused heavily on improving PySpark performance, flexibility, and ease of use...