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Databricks intros OpenSharing, a new standard for sharing AI
The open source protocol modernizes collaboration by enabling teams to share AI assets such as AI models and agent skills across domains and with external partners.
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Unstructured data needed, but often untapped, for agentic AI
AI development initiatives hinge on the quality and completeness of the underlying data, but research from BARC shows that many organizations struggle to operationalize key data.
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.
News
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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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 News
Unstructured data needed, but often untapped, for agentic AI
AI development initiatives hinge on the quality and completeness of the underlying data, but research from BARC shows that many organizations struggle to operationalize key data.
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Data Integration Problem Solve
8 data integration challenges and how to overcome them
Data integration in modern architectures faces eight challenges, from preserving lineage at scale to serving AI and analytics workloads, each explored with practical strategies.
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Data Governance News
Databricks intros OpenSharing, a new standard for sharing AI
The open source protocol modernizes collaboration by enabling teams to share AI assets such as AI models and agent skills across domains and with external partners.
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Virtual Machines
AWS & Red Hat
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Information Management
PwC customer contact transformation powered by Amazon Connect.
Solving complex service challenges with the right blend of people, process and technology – enabling organisations to move beyond outdated contact centre models and embrace scalable, agile solutions that meet today’s demands.
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Cloud
Optimize Your Cloud Migration with AWS & Infoblox
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Find Solutions For Your Project
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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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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
8 data integration challenges and how to overcome them
Data integration in modern architectures faces eight challenges, from preserving lineage at scale to serving AI and analytics workloads, each explored with practical strategies.
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Data observability for AI helps curb poor model performance
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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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Manage
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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Govern citizen development to avoid data pipeline downtime
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How agentic AI governance tackles data, security challenges
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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.
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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
Databricks intros OpenSharing, a new standard for sharing AI
The open source protocol modernizes collaboration by enabling teams to share AI assets such as AI models and agent skills across domains and with external partners.
-
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.
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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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