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Neo4j's GraphAware acquisition targets new customer segment
The purchase adds analysis capabilities for government agencies that work on top of the vendor's graph database, expanding its target audience to include analysts.
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Big data integration techniques and best practices to adopt
Data integration in big data systems is even more complex now because of AI. To succeed, it requires a strategy built on new approaches and strong data management.
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Context is the make-or-break layer for AI in production
Your AI model might not be the reason it fails in production. Missing or unclear context throws off the model, leading to untrustworthy outputs that the business can't act on.
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Microsoft boosts Fabric to make it a foundation for AI
New features that feed agents contextually relevant data add breadth to the platform and keep its data and AI capabilities current in a competitive market.
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Data science and analytics News
New Tableau leader talks vendor's evolution in era of AI
With longtime data and analytics providers pivoting to find their role within AI workflows, Mark Recher takes over during a time of transition for the vendor.
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Database Management News
Actian targets secure, compliant AI with new vector database
The vendor's portable database enables organizations in heavily regulated industries to build AI tools without risking accidental data exposure or regulatory violations.
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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 Get Started
Context is the make-or-break layer for AI in production
Your AI model might not be the reason it fails in production. Missing or unclear context throws off the model, leading to untrustworthy outputs that the business can't act on.
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Data Integration Get Started
Big data integration techniques and best practices to adopt
Data integration in big data systems is even more complex now because of AI. To succeed, it requires a strategy built on new approaches and strong data management.
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Data Governance Evaluate
Why enterprise AI depends on the semantic layer
Semantic layers are moving from BI tools into the core analytics stack as AI agents query enterprise data, requiring governed definitions for consistent interpretation.
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Optimize Your Cloud Migration with AWS & Infoblox
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Storage
Dell Technologies Partner Dell Tech Advantage
Learn how a consolidated approach to storage can modernize your storage which will increase efficiency and can decrease costs.
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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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Why enterprise AI depends on the semantic layer
Semantic layers are moving from BI tools into the core analytics stack as AI agents query enterprise data, requiring governed definitions for consistent interpretation.
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Context is the make-or-break layer for AI in production
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Why AI forces security-first governance
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Tracing data lineage in AI systems
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Problem Solve
Data observability for AI helps curb poor model performance
Data quality issues get amplified in AI applications. Models can produce confident -- but misleading -- forecasts and conclusions without observability safeguards.
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10 big data challenges and how to address them
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Data sovereignty expands beyond compliance boundaries
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Power-constrained data architecture curbing AI ambitions
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Big data integration techniques and best practices to adopt
Data integration in big data systems is even more complex now because of AI. To succeed, it requires a strategy built on new approaches and strong data management.
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Data governance metrics: Measure success, identify issues
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How agentic AI governance tackles data, security challenges
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How to develop a data governance strategy: 7 key steps
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Data Management/Data Warehousing Basics
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Get Started
Big data integration techniques and best practices to adopt
Data integration in big data systems is even more complex now because of AI. To succeed, it requires a strategy built on new approaches and strong data management.
-
Get Started
Context is the make-or-break layer for AI in production
Your AI model might not be the reason it fails in production. Missing or unclear context throws off the model, leading to untrustworthy outputs that the business can't act on.
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Get Started
Power-constrained data architecture curbing AI ambitions
Rising power demands and grid interconnection delays are hampering enterprise AI efforts and altering data strategies, workload placement and resilience planning.
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Data governance
Neo4j's GraphAware acquisition targets new customer segment
The purchase adds analysis capabilities for government agencies that work on top of the vendor's graph database, expanding its target audience to include analysts.
-
Data integration
Microsoft boosts Fabric to make it a foundation for AI
New features that feed agents contextually relevant data add breadth to the platform and keep its data and AI capabilities current in a competitive market.
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Data management strategies
Snowflake barrage adds more AI development, analysis tools
A streaming data service and tools that provide agents with contextual awareness highlight the latest from the vendor as it constructs a foundation for agentic enterprises.
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