When Zaharia started work on Spark around 2010, analyzing "big data" generally meant using MapReduce, the Java-based ...

Abstract: DNA-based data storage has emerged as a compelling alternative to traditional media due to its ultra-high information density and long-term stability. However, the high read cost caused by ...

Compare the top 5 customer identity and access management (CIAM) platforms in 2026 to find the right fit for your product's ...

The proliferation of AI is changing the nature of cyberattacks, with enterprises exposed to targeted, fast-moving threats. Gaps in governance and guardrails around AI adoption are expanding the attack ...

With Lakewatch, Databricks presents an open SIEM based on Lakehouse. AI agents are intended to automatically detect and triage threats in data pools.

Accenture (NYSE: ACN) and Databricks have deepened their collaboration to help businesses worldwide harness enterprise data more effectively and rapidly expand the use of sophisticated AI applications ...

The launch of Genie Code, analysts say, signals Databricks’ growing ambition to turn its lakehouse platform into the environment where enterprise AI systems build, run, and manage data workflows.

Databricks Inc. today introduced Genie Code, an artificial intelligence agent designed to automate complex data engineering and analytics tasks. The move extends the rapid evolution of agents from ...

Amjad Masad’s Replit allows users to build apps together like they’re doodling on a white board. It also made the Jordanian immigrant a billionaire along the way. Two years ago, Replit CEO Amjad Masad ...

AI coding agents have become one of the fastest-growing categories in enterprise software. In the span of just a few years, these development tools have evolved from simple autocomplete assistants ...

‘Agents of Chaos’: New Study Shows AI Agents Can Leak Data, Be Easily Manipulated Your email has been sent As enterprise AI agent adoption accelerates, a new study exposes a governance gap that leaves ...

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then ...