This article describes how HARDlight built a Databricks-native A/B testing analysis framework to automate and standardize mobile game experimentation at scale.
This article explains what analytic applications are, how they work, and how they differ from traditional BI tools.
This article introduces Intelligent Document Processing (IDP), an AI-powered technology for extracting and processing information from documents like PDFs, emails, and forms.
This article explains the Relational Data Model, its core components, and its role in modern RDBMS.
This post describes how baseball teams use Databricks to convert high-fidelity pitch data into game decisions through AI agents and a governed data lakehouse.
Databricks has been recognized as a Customers' Choice in the Gartner Peer Insights Voice of the Customer for Analytics and Business Intelligence Platforms.
This post introduces AutoCDC in Lakeflow Spark Declarative Pipelines as a declarative alternative to hand-coded Change Data Capture and Slowly Changing Dimension pipelines.
This article covers how enterprises use Google Cloud Platform for data engineering and generative AI, with concrete architectural patterns and real-world use cases.
Databricks announces Lakewatch, an open, agentic SIEM built on lakehouse architecture to counter AI-driven cyberattacks at machine scale.
Databricks introduces Lakewatch, an Open Security Lakehouse, built in partnership with National Australia Bank (NAB) to address modern AI-era cyber threats at enterprise scale.
This post explores building a RAG-based code knowledge assistant using three chunking strategies and evaluating them with MLflow.
This guide covers the most widely adopted MLOps frameworks and how to evaluate them for production machine learning deployments.
