venturebeat

2025-10-01

Databricks set to accelerate agentic AI by up to 100x with ‘Mooncake’ technology — no ETL pipelines for analytics and AI

Many enterprises running PostgreSQL databases for their applications face the same expensive reality. When they need to analyze that operational data or feed it to AI models, they build ETL (Extract, Transform, Load) data pipelines to move it into analytical systems. Those pipelines require dedicated data engineering teams, break frequently and create delays measured in hours or days between when data is written to a database and when it becomes available for analytics.

For companies with large numbers of PostgreSQL instances, this infrastructure tax is massive. More critically, it wasn't designed for a world where AI agents gene [...]

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venturebeat

2025-11-14

Databricks: 'PDF parsing for agentic AI is still unsolved' — new tool replaces multi-service pipelines with single function

There is a lot of enterprise data trapped in PDF documents. To be sure, gen AI tools have been able to ingest and analyze PDFs, but accuracy, time and cost have been less than ideal. New technology fr [...]

Match Score: 193.26

venturebeat

2025-11-04

Databricks research reveals that building better AI judges isn't just a technical concern, it's a people problem

The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place.That's where AI judges are now playi [...]

Match Score: 116.45

venturebeat

2025-10-20

Agentic AI security breaches are coming: 7 ways to make sure it's not your firm

AI agents – task-specific models designed to operate autonomously or semi-autonomously given instructions — are being widely implemented across enterprises (up to 79% of all surveyed for a PwC rep [...]

Match Score: 112.61

venturebeat

2025-11-12

How Deductive AI saved DoorDash 1,000 engineering hours by automating software debugging

As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunt [...]

Match Score: 83.56

venturebeat

2025-11-03

AI coding transforms data engineering: How dltHub's open-source Python library helps developers create data pipelines for AI in minutes

A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in minutes using tools that would have required entire specialized teams just mont [...]

Match Score: 76.53

venturebeat

2025-10-27

MiniMax-M2 is the new king of open source LLMs (especially for agentic tool calling)

Watch out, DeepSeek and Qwen! There's a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic tool use — that [...]

Match Score: 72.41

venturebeat

2025-10-29

The missing data link in enterprise AI: Why agents need streaming context, not just better prompts

Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.The challenge is infrast [...]

Match Score: 69.68

venturebeat

2025-10-23

Research finds that 77% of data engineers have heavier workloads despite AI tools: Here's why and what to do about it

Data engineers should be working faster than ever. AI-powered tools promise to automate pipeline optimization, accelerate data integration and handle the repetitive grunt work that has defined the pro [...]

Match Score: 61.46

venturebeat

2025-10-26

From human clicks to machine intent: Preparing the web for agentic AI

For three decades, the web has been designed with one audience in mind: People. Pages are optimized for human eyes, clicks and intuition. But as AI-driven agents begin to browse on our behalf, the hum [...]

Match Score: 50.97