Data Management

DocLang aims to make documents readable by AI, not humans

Development of the AI-native DocLang document format raises questions about its impact on human workers, as well as on governance and accountability.

Databricks’ OpenSharing targets the ‘integration tax’ of enterprise AI

The zero-copy credential model enables cross-platform sharing of AI assets, promising lower overhead, stronger governance, and faster deployment across partners and teams.

10 MCP servers to connect LLMs with databases

Use these official MCP servers to interact with the leading database platforms via natural language through your LLM-assisted tools.

AWS targets a longtime cloud migration blocker with SQL Server license portability

Enterprises can now bring existing SQL Server licenses to Amazon RDS, making it easier to move operational data closer to AWS analytics and agentic AI services.

Failback in Seconds: Customers Share Disaster Recovery Stories From the Data Center

The next AI breakthrough won’t come from bigger models, but from better data

Just as with LLMs, success in other frontiers of AI will require access to large volumes of high-quality data. That will require deliberate, research-driven dataset design.

Snowflake’s Horizon Context aims to give AI agents a common understanding of the business

These new capabilities could help CIOs reduce the complexity of deploying AI agents into production, analysts say.

What Snowflake Summit 2026 signals about enterprise AI

The next phase of enterprise AI will be decided less by models and more by how enterprises govern, secure, and operationalize AI across fragmented environments.

Snowflake to acquire MCP-focused Natoma to boost governance for AI agents

The acquisition could help enterprise CIOs struggling to deploy agentic workflows spanning heterogeneous systems via MCP while positioning the vendor as the orchestration layer for AI agents, analysts say.

Salesforce extends its headless push into enterprise data via Informatica

The update could spare developers much of the integration and custom plumbing work typically required to build AI-driven workflows, although CIOs should be wary of cost, accuracy headaches, analysts say.