Articles

Compare data catalogs, data governance, and data lineage with a 2026 enterprise guide. Learn what each solves, where they overlap, and how Galaxy’s semantic layer ties them together.

A practical reference architecture for enterprise metadata management. Learn how catalog, lineage, semantic layer, and governance work together to make data AI-ready.

Agentic AI needs trusted state plus shared meaning. Learn how distributed SQL and Galaxy’s semantic foundation unify entities, governance, and context for reliable agents.

Comparing Galaxy with Collibra, Alation, and Purview for Snowflake governance. Learn where catalogs, lineage, and compliance end—and semantic context begins.

Unify hybrid cloud metadata so Power BI and Tableau agree. Learn how a knowledge graph semantic layer prevents semantic drift and improves governance.

Learn how to map SQL tables to RDF with R2RML and open-source tools like Ontop and D2RQ. Step-by-step workflow, patterns, and architecture trade-offs.

Compare RAG, knowledge graphs, and semantic layers for enterprise AI. Learn tradeoffs in accuracy, explainability, governance, and why GraphRAG approaches win in 2026.

Build an enterprise semantic layer that aligns KPI definitions, unifies customer and revenue metrics, and makes analytics and AI trustworthy. Architecture plus an implementation checklist.

Semantic Data Unification vs MDM: Learn the key differences in architecture, entity resolution, governance, and AI readiness—and when to use each.

Discover the best data mapping platforms for 2026, including Galaxy, Informatica, Tamr, and Stardog. Learn what matters for AI-ready semantic mapping.

Automated semantic modeling unifies fragmented customer data into an ontology-driven knowledge graph, enabling accurate entity resolution, AI-ready analytics, and explainable governance.

Learn what ontology mapping is, why it matters for data governance, and how it connects schemas, silos, and tools into a semantic layer and single source of truth.

Discover the 13 best data quality tools in 2026 for reliable analytics and AI, from observability platforms like Monte Carlo and Metaplane to semantic data platforms like Galaxy, Collibra, Ataccama, Palantir, and more—compare pricing, use cases, and capabilities across hybrid, multi‑cloud, and AI-driven data estates.

Explore the top graph databases for 2026. Compare Neo4j, AWS Neptune, TigerGraph, and more. Learn how graph technology powers analytics, ontologies, and enterprise AI.

Discover what a data product is, how it differs from raw datasets, and why product-thinking for data is critical for breaking silos, improving data quality, and powering trustworthy AI in modern enterprises.

Compare the top 10 ETL tools for data warehousing in 2026, including Galaxy and leading platforms for AI and semantic integration. Get clear advice on choosing the right data integration and ETL solutions for your enterprise.

Fragmented enterprise data needs semantic infrastructure. Explore the best knowledge graph platforms for AI-ready data in 2026.

Context engineering playbook for AI agents: how to prevent context rot, design semantic layers and knowledge graphs, and keep enterprise agents reliable at scale.

Master Data Management (MDM) explained: definition, golden records, implementation styles, entity resolution, AI-native MDM, and key benefits for enterprise data teams.

Compare the best enterprise knowledge graph platforms for AI reasoning in 2026. See how Galaxy, Palantir, Stardog, Neo4j and others support GraphRAG, MDM, and governance.

Entity resolution links fragmented records into accurate, unified entities across systems. Learn how deterministic, probabilistic, fuzzy, and graph-based methods work, where they’re used in healthcare, finance, and customer 360, and how semantic platforms like Galaxy turn them into an AI-ready foundation.

Semantic data unification blueprint for enterprises: use knowledge graphs, semantic layers, and entity resolution to break silos, power AI, and deliver true customer 360.

Compare the top semantic layer tools for real-time enterprise analytics in 2026. See how platforms like Galaxy, Palantir, Timbr, Stardog, and more unify metrics, ontology, and AI-ready semantics across your data stack.

Entity resolution identifies, links, and merges duplicate records across systems to create unified customer, product, and supplier views for enterprise AI.

Learn how knowledge graphs power enterprise AI, GraphRAG, and agents by grounding LLMs in verified business context and relationships.

Graph analytics turns fragmented enterprise data into a unified semantic layer, powering accurate AI reasoning, GraphRAG, and context-rich decisions in 2026.

Ontology makes AI analytics reliable by defining shared business meaning. Learn how ontology grounds AI systems and why it’s essential for AI-ready companies.

AI-ready enterprises need more than ETL. Compare the top data integration platforms of 2026—Galaxy, Palantir, Fivetran, Informatica, and more—to choose a semantic, governed data foundation for trustworthy AI.

Learn what a knowledge graph is, how it connects entities and relationships, and why enterprises use it for Customer 360, fraud detection, governance, and AI. Includes benefits, standards, and challenges.

See 2025's leading modern data platforms compared—insights on integration, AI readiness, scalability, and real-world adoption. Structured for enterprise leaders.

Understand the strategic role of enterprise architects across all seven layers of AI model architecture—from infrastructure to application. Learn why semantic context, interoperability, and knowledge governance are the backbone of successful, trustworthy, and scalable AI.

What are AI agents? Learn how autonomous, agentic AI systems use reasoning, tool use, and memory to automate enterprise workflows. See types, benefits, risks, and best practices.

Confused about semantic layers in modern analytics? This 2025 playbook explains what a semantic layer is, best architecture options, and how leaders can avoid hype and build for real AI readiness, interoperability, and governance.

Explore the top SaaS data integration platforms for 2025. Get an expert breakdown of features, pricing, and real-world advice to find the right solution for your business and enable true data interoperability.

Learn how to integrate enterprise data with AI using governance, ontologies, and knowledge graphs, plus practical steps for agent-driven automation.

Learn how to use AI in business without risking data security or compliance. Get steps for safe adoption, team training, and aligning AI to business goals.

Explore how GenAI reasoning models create a true cognitive layer for enterprise automation—enabling context-rich, self-improving decisions that outpace classic automation. See how semantic data and knowledge graphs are key for AI readiness.

Discover how ontology-based semantic layers unify business logic, boost data consistency, reduce duplication, and enable AI-readiness for modern enterprises.

AI agent protocols set the standards for agent-to-agent communication and interoperability. Learn the benefits, examples, and what to consider for effective multi-agent AI systems.

Learn how enterprise ontology, semantic layers, and knowledge graphs enable AI agents to understand, reason, and act on business data effectively.

Learn what AI agent communication means, how intelligent agents collaborate, and why semantic context is crucial for true AI and multi-agent interoperability.

Discover the four essential pillars for effective data and AI governance. Learn how unified governance, risk management, security, and compliance drive scalable, trustworthy AI.

A semantic layer bridges technical data and business meaning, unlocking analytics, interoperability, and AI-readiness. Learn how semantic layers unify, govern, and give context to your data.

Learn what agentic reasoning is, why it matters for next-generation AI, real-world examples, and how knowledge graphs and ontology power context-aware, adaptive AI in the enterprise.

Learn how to choose the best graph database for your needs. Compare native vs. multi-model options, see what really impacts scale, interoperability, and AI-readiness, and select a platform that enables shared understanding for your data and team.

Discover the essential building blocks for an AI-ready data foundation. Learn why unified access, real-time performance, and semantic interoperability are critical for enterprise AI success.

Agentic reasoning is changing AI in the workplace—moving beyond search to real task automation. Here’s what agentic AI is, why it matters, and how ontologies and knowledge graphs are powering the next generation of enterprise AI.

Knowledge graphs unlock agentic AI. Learn how ontologies, semantic context, and shared memory give AI agents the structure needed for reasoning, memory, and reliable enterprise workflows.

Discover what SaaS integration is, why it’s critical for businesses of any size, and how it unlocks automation, data consistency, and business agility. Learn pitfalls, benefits, and the future of unified data.

Struggling with conflicting numbers and siloed reports? Learn what really causes disparate data, why data unification matters, and how to build a foundation for consistent, actionable insights across your organization.

How agentic IT architecture transforms enterprise AI adoption. Learn the 11 essential layers, design principles, and roadmap for building a semantic, agentic enterprise.

Discover the top 17 data integration tools for 2025. Cut through the noise and learn how to pick the right data integration platform for your enterprise, analytics, or AI projects.

Explore the DPROD Ontology—the open standard for semantic, interoperable, and discoverable data products. See why ontologies are critical for AI readiness, data mesh, knowledge graphs, and enterprise data governance.

Learn the practical differences between knowledge graphs and embeddings, and how combining both can unlock context, explainability, and scalable AI in the enterprise.

See why traditional knowledge graphs fall short and how RDF*, hypergraphs, and graph embeddings enable explainable, AI-ready enterprise data architectures.

Discover how ontologies enable smarter, more trustworthy AI by structuring data, enabling semantic reasoning, and boosting transparency. Learn to unify data and build explainable AI with enterprise ontologies.

Discover the top graph database use cases — from customer 360 to fraud detection and GenAI. Learn how leading companies unlock value from their connected data.

Compare RDF and LPG data models for enterprise knowledge graphs. Discover why RDF's semantic foundation delivers scalable, interoperable, and AI-ready data.

Discover how knowledge graphs accelerate, clarify, and automate master data management. Align business and engineering, avoid rework, and build a shared source of truth.