Meta Data Glossary

data democratizationself-service analyticsdata catalog

What Is Data Democratization?

Data democratization is the practice of making data accessible, understandable, and usable across an organization — not just for data specialists. Learn what it requires, where it fails, and why governance is what makes democratization safe.

What Is Data Privacy?

Data privacy is the right of individuals to control how their personal information is collected, used, and shared. Learn the legal frameworks (GDPR, CCPA), technical privacy controls, and how data governance enables privacy compliance at scale.

data privacyGDPRCCPA

synthetic dataAI training datadata privacy

What Is Synthetic Data?

Synthetic data is artificially generated data that preserves the statistical properties of real data without containing actual personal information. Learn how it's generated, its use cases, and where it fits in a governed data strategy.

data fabricdata architecturemetadata management

What Is Data Fabric?

Data fabric is an architecture that creates a unified, intelligent data management layer across heterogeneous environments using active metadata, knowledge graphs, and automated integration.

data meshdomain ownershipdata as a product

What Is Data Mesh?

Data mesh is a decentralized data architecture based on four principles: domain ownership, data as a product, self-serve infrastructure, and federated governance.

column-level lineagedata lineagefield-level lineage

What Is Column-Level Lineage?

Column-level lineage tracks how individual data fields flow and transform across pipelines. Essential for BCBS 239, GDPR data mapping, impact analysis, and AI training data provenance.

master data managementMDMgolden record

What Is Master Data Management (MDM)?

Master data management (MDM) creates a single, trusted source of truth for critical business entities like customers, products, and suppliers. Learn MDM architecture, implementation approaches, and how it relates to data governance.

unstructured datadocument managementAI data processing

What Is Unstructured Data?

Unstructured data — documents, emails, images, audio, video — makes up 80–90% of enterprise data. Learn what it is, why it's hard to govern, and how AI makes it usable.

ETL vs ELT: What's the Difference and Which Should You Use?

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the two dominant patterns for moving data between systems. Learn how they differ, when to use each, and why lineage and governance matter for both.

ETLELTdata pipeline

data pipelinedata engineeringETL

What Is a Data Pipeline?

A data pipeline is an automated sequence of processes that moves, transforms, and delivers data from source systems to consumers. Learn about pipeline architecture, types, governance, and observability.

data lakehouseDelta LakeApache Iceberg

What Is a Data Lakehouse?

A data lakehouse combines the low-cost storage of a data lake with the query performance and governance capabilities of a data warehouse. Learn how the lakehouse architecture works, who it's for, and what governance it requires.

DataOpsdata engineeringCI/CD

What Is DataOps?

DataOps applies DevOps and lean manufacturing principles to data engineering — automating pipelines, accelerating delivery, and building quality in. Learn the practices, benefits, and where DataOps meets data governance.

data discoverydata catalogmetadata

What Is Data Discovery?

Data discovery is the process of finding, understanding, and evaluating data assets across an organization. Learn how modern data catalogs automate discovery, the role of metadata, and why discoverability is the foundation of data-driven culture.

medallion architecturebronze silver golddata lakehouse

What Is Medallion Architecture?

Medallion architecture organizes data lakehouse storage into Bronze, Silver, and Gold layers — each with increasing quality and structure. Learn the pattern, its benefits, and how to govern data across layers.

reverse ETLdata activationoperational analytics

What Is Reverse ETL?

Reverse ETL moves processed data from a data warehouse back into operational tools like CRMs, ad platforms, and customer success systems. Learn how data activation works and what governance it requires.

Apache Icebergopen table formatdata lakehouse

What Is Apache Iceberg?

Apache Iceberg is an open table format for large-scale analytic datasets. It brings ACID transactions, schema evolution, and time travel to data lakehouses. Learn how it compares to Delta Lake and Hudi.

data classificationsensitive dataPII

What Is Data Classification?

Data classification is the process of organizing data into categories based on sensitivity, type, or business value. Learn how automated classification powers data governance, GDPR compliance, and security.

data maskingdata anonymizationPII protection

What Is Data Masking?

Data masking protects sensitive information by replacing it with realistic but fictional values. Learn static vs dynamic masking, common techniques, and how masking supports GDPR compliance and secure analytics.

AI-ready datadata for AILLM data quality

What Is AI-Ready Data?

AI-ready data is enterprise data that is accurate, well-documented, discoverable, and governed — prepared for reliable use in AI models, LLMs, and agentic workflows. Learn what it takes to get there.

GraphRAGgraph RAGknowledge graph

What Is GraphRAG?

GraphRAG combines knowledge graphs with retrieval-augmented generation to give LLMs richer, relationship-aware context. Learn how it reduces hallucinations and enables complex enterprise Q&A.

data sharingDelta SharingSnowflake Marketplace

What Is Data Sharing?

Data sharing enables organizations to securely exchange data across teams, clouds, and company boundaries. Learn the models, platforms like Delta Sharing and Snowflake Marketplace, and the governance it requires.

change data captureCDCreal-time data

What Is Change Data Capture (CDC)?

Change data capture (CDC) continuously tracks and streams database changes to downstream systems in near real-time. Learn log-based CDC, tools like Debezium, and how CDC fits into modern data pipelines.

data vaultdata vault modelinghub link satellite

What Is Data Vault?

Data Vault is a data warehousing modeling methodology built for scalability, auditability, and resilience to change. Learn Hubs, Links, Satellites, and when to choose Data Vault over Kimball or Inmon.

dbt modelsdbt transformationsdata build tool

What Is a dbt Model?

A dbt model is a SQL SELECT statement that defines a data transformation in dbt (data build tool). Learn materializations, the staging-marts layer pattern, and how dbt models generate metadata for governance.

dbt testsdbt data qualitydbt testing

What Are dbt Tests?

dbt tests are automated data quality checks that run after transformations to validate your data. Learn built-in generic tests, custom singular tests, and how dbt testing integrates with data observability.

dbt lineagedata lineage dbtdbt DAG

What Is Data Lineage with dbt?

dbt automatically generates data lineage through ref() and source() functions, creating a DAG of all transformations. Learn column-level lineage, OpenLineage integration, and connecting dbt to enterprise governance.

vector databaseembeddingssimilarity search

What Is a Vector Database?

A vector database stores high-dimensional embeddings and enables similarity search at scale. Learn how vector databases power RAG, semantic search, and enterprise AI — and why they need data governance to be reliable.

multi-agent systemsAI agentsagent orchestration

What Are Multi-Agent Systems?

Multi-agent systems are architectures where multiple AI agents collaborate to complete complex tasks. Learn orchestration patterns, enterprise use cases, and the governance they require.

What Is LLMOps?

LLMOps is the discipline of operationalizing large language models in production — covering deployment, monitoring, evaluation, versioning, and cost management. Learn how LLMOps extends MLOps and what data teams need to know.

LLMOpsMLOpsLLM

AI observabilityLLM monitoringAI monitoring

What Is AI Observability?

AI observability is the practice of monitoring, understanding, and debugging AI systems in production — covering output quality, hallucination rates, drift, and governance compliance.

responsible AIAI ethicsEU AI Act

What Is Responsible AI?

Responsible AI is the practice of developing and deploying AI systems that are fair, transparent, accountable, and safe. Learn the core principles, regulatory landscape, and why data governance is the foundation of responsible AI.

large language modelLLMtransformer

What Is a Large Language Model (LLM)?

A large language model (LLM) is a neural network trained on massive text corpora to understand and generate language. Learn how LLMs work, their limits, and why data governance matters for enterprise AI.

What Is Prompt Engineering?

Prompt engineering is the practice of crafting inputs to language models to get reliable, accurate, and useful outputs. Learn the key techniques, why context quality matters more than prompt tricks, and how governed data makes prompts work.

prompt engineeringLLMRAG

What Is AI Hallucination?

AI hallucination is when a language model generates plausible-sounding but false information. Learn why LLMs hallucinate, the business risks this creates, and how governed data infrastructure reduces hallucination in enterprise AI.

ai hallucinationLLMRAG

context engineeringLLMRAG

What Is Context Engineering?

Context engineering is the practice of designing and managing the information delivered to an AI model's context window to maximize output quality. Learn why it matters more than prompt crafting and how governed data makes it tractable.

fine-tuningLLM fine-tuningLoRA

What Is Fine-Tuning an LLM?

Fine-tuning adapts a pre-trained LLM on domain-specific data to improve performance on targeted tasks. Learn when to use it vs. RAG, and what governance training data requires.