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Oracle AI Database update aims to ease developing agents
New vector indexing capabilities and prebuilt agents are designed to simplify building cutting-edge applications and could help differentiate the tech giant from competitors.
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Q&A: The gap between AI ambitions and data readiness
Ataccama CEO Mike McKee discusses why most organizations aren't ready for AI, why ROI failures are largely a people problem and what the data management industry keeps overlooking.
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Understanding the layers of the AI‑ready modern data stack
Data infrastructure and practices need an upgrade for the AI era. A modern data stack takes a layered approach that aligns teams and delivers governed, trusted data.
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How to build an effective big data strategy
Big data initiatives won't deliver business benefits without a comprehensive strategy to guide data management and analytics work. Here's how to build one.
Trending Topics
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Data science and analytics News
Domo doubles down on AI with latest platform additions
New features such as an AI Library, a framework for building agents and an MCP server are all aimed at better enabling customers to operationalize their AI initiatives.
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Database Management Evaluate
The database is the new battleground for enterprise AI
Agentic AI puts new pressure on enterprise databases, exposing gaps in the data access, security enforcement and tooling fragmentation that block production deployment.
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Data Warehousing News
Latest AWS data management features target cost control
As the volume and complexity of enterprise data estates increase, and the size of data workloads grows due to AI development, the tech giant aims to help users reduce spending.
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Data Management Strategies News
Oracle AI Database update aims to ease developing agents
New vector indexing capabilities and prebuilt agents are designed to simplify building cutting-edge applications and could help differentiate the tech giant from competitors.
-
Data Integration News
Alation automates governance with latest AI-powered suite
Outcome-based governance, including the newly launched Curation Automation, turns time-consuming data management tasks over to agents and could be a differentiator for the vendor.
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Data Governance Evaluate
Q&A: The gap between AI ambitions and data readiness
Ataccama CEO Mike McKee discusses why most organizations aren't ready for AI, why ROI failures are largely a people problem and what the data management industry keeps overlooking.
Sponsored Sites
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Virtual Machines
AWS & Red Hat
Simplify App Modernization and Innovation with Red Hat OpenShift Service on AWS
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Generative AI
Enhancing Generative AI for Independent Software Vendors (ISVs)
Transform your software and deliver enhanced experiences to your end users when you integrate with the Amazon Q Business index. Access secure, multi-app data through an API to deliver smarter, context-rich experiences to your end users. Join leading providers in revolutionizing applications with composable AI features.
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Data Management
Intel and Microsoft: Intelligent Edge to Cloud Solutions
Microsoft and Intel build on long-standing co-engineering efforts to enable differentiated services within Azure. By combining innovative software and services with cutting-edge hardware, the Intel and Microsoft partnership delivers state-of-the-art-edge to cloud solutions for Industrial IoT and computer vision edge AI, SAP on Azure, high-performance computing (HPC), confidential computing, hybrid cloud, Microsoft SQL Server, AI, analytics, and more.
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The database is the new battleground for enterprise AI
Agentic AI puts new pressure on enterprise databases, exposing gaps in the data access, security enforcement and tooling fragmentation that block production deployment.
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Q&A: The gap between AI ambitions and data readiness
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12 top open source databases for enterprise use
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Why agentic AI demands both structured and unstructured data
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Problem Solve
SLM vs. LLM: Rightsize data architecture to optimize AI use
It doesn't need to be a binary choice. Enterprises are warming up to smaller AI models to meet compliance and cost needs while reserving large models for complex jobs.
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AI data governance guidance that gets you to the finish line
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How to cut data loss risks when employees leave
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Residual access failures put data at risk
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Manage
Data lineage documentation matters for enterprise reliability
Data lineage is critical to enterprise success. Tracing a data asset's journey through pipelines helps improve data quality, speeds up corrections and builds trust in analytics.
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Build trust on a federated governance model
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Data governance responsibilities now belong in the C-suite
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How executives can build a responsible AI framework
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Data Management/Data Warehousing Basics
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Get Started
Understanding the layers of the AI‑ready modern data stack
Data infrastructure and practices need an upgrade for the AI era. A modern data stack takes a layered approach that aligns teams and delivers governed, trusted data.
-
Get Started
How to build an effective big data strategy
Big data initiatives won't deliver business benefits without a comprehensive strategy to guide data management and analytics work. Here's how to build one.
-
Get Started
SLM vs. LLM: Rightsize data architecture to optimize AI use
It doesn't need to be a binary choice. Enterprises are warming up to smaller AI models to meet compliance and cost needs while reserving large models for complex jobs.
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Vendor Resources
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- Navigating the Future of Enterprise IT: Trends Shaping 2026 –Analyst Report
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Data management strategies
Oracle AI Database update aims to ease developing agents
New vector indexing capabilities and prebuilt agents are designed to simplify building cutting-edge applications and could help differentiate the tech giant from competitors.
-
Data management strategies
Snowflake intros SnowWork to automate tasks with AI
The platform, which is now in research preview, is designed to provide users with agent-powered collaboration capabilities that can automate actions and workflows.
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Data management strategies
Qdrant raises $50M in funding to fuel vector database growth
With VCs cautious about investing in data management providers, the financing, which will be used for R&D and go-to-market initiatives, serves as validation of the vendor's vision.
Search Data Management Definitions
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