Azure Data Lake
A scalable cloud storage service (Azure Data Lake Storage Gen2) built on Azure Blob Storage, designed for big data analytics workloads. Supports hierarchical namespaces for efficient file operations and fine-grained access control.
Woordenlijst
Azure data en AI-begrippen uitgelegd
IITS definieert de kernbegrippen van Azure data-engineering, AI-agents, RAG, LLMOps, compliance en datagovernance — in het Nederlands en Engels.
Microsoft Fabric
A unified SaaS analytics platform from Microsoft that integrates data engineering, data integration, data warehousing, real-time analytics and Business Intelligence in a single product. The successor to Azure Synapse Analytics, built on OneLake.
Data Lineage
The end-to-end record of where data originates, how it moves, how it transforms, and where it lands. Essential for compliance, audit trails, and debugging broken data pipelines.
Medallion Architecture
A data engineering pattern that organises data into bronze, silver and gold layers. Raw data lands first, validated data becomes reusable, and curated data supports analytics and AI use cases.
GDPR
General Data Protection Regulation. Requires organisations to protect the personal data and privacy of EU citizens and affects how data platforms and AI systems should handle access, retention and auditability.
RAG
Retrieval-Augmented Generation. An AI architecture where an LLM retrieves relevant company knowledge before answering, making responses more grounded, traceable and useful for internal workflows.
AI Agent
A software system that uses an AI model, tools, policies and workflow logic to complete a task or assist a user within defined operational boundaries.
Data Lakehouse
A data architecture that combines the flexible storage of a data lake with the data management and query performance features of a data warehouse. Microsoft Fabric's OneLake is a data lakehouse implementation. Eliminates the traditional ETL pipeline between lakes and warehouses.
Azure Landing Zone
A pre-configured, scalable and secure Azure environment implementing the Azure Cloud Adoption Framework (CAF) guidelines. Provides networking, identity, governance and security baselines so workloads can be deployed on a solid foundation — without configuring everything from scratch.
ETL / ELT
Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are data integration patterns. Modern cloud platforms (Azure Data Factory, Databricks, Fabric) prefer ELT: raw data is loaded first, then transformed inside the platform where compute is elastic and cost-efficient.
Data Governance
The collection of policies, processes, standards, metrics, roles and tools that help organisations manage their data as an asset. Includes data quality, data cataloguing, metadata management, data stewardship and compliance documentation. Microsoft Purview is the primary tool for data governance on Azure.