Systems | Development | Analytics | API | Testing

To fully take advantage of AI, your organization needs a solid foundation of automated data integration, context engineering, and an agentic harness.

A single source of truth doesn’t just come from centralizing data. You have to engineer context from it, too.

Two engineers used AI coding agents to design, build, and ship a production-ready status page — here's what worked, what didn't, and what we'd do differently.

Keep track of new connectors and product releases.

You can now execute DDL and DML operations through the Managed Data Lake Service.

Interoperability means the optionality to adapt to rapidly changing future demands.

Automated data integration enables AI and analytics based on mission-critical operational data stored in IBM Z.

Most data stacks weren’t built for AI agents. Take the ODI Assessment to find out if yours is ready.

From identifying targets with proprietary market data to closing on time and separating cleanly in a divestiture — having all of your data in one place can make or break your M&A.

For humans and agents alike to use data effectively for future workloads, they need unified context, reduced friction, and flexibility.