The BEEM data glossary

Plain-language definitions of the integration, warehouse and pipeline terms data teams actually run into — with their use cases and limits.

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Data lakehouse
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A data glossary collects the definitions of the concepts behind data integration, storage and governance. Each entry explains one term in plain language, when you would use it, and where it stops being the right tool.

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Data democratization
Governance & quality
Giving non-technical teams safe, direct access to the data they need, instead of routing every question through a data team.
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D
Data integration
Integration
The process of combining data from many separate systems into a single, consistent view a business can use.
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D
Data lakehouse
Storage & warehouse
A single platform that combines a data lake's raw storage with a data warehouse's structure and fast queries in one system.
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D
Data mesh
Architecture
A decentralized approach to data architecture where individual domain teams own their data as products, instead of one central team owning all of it.
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D
Data observability
Pipelines & orchestration
Continuous monitoring of data freshness, volume and schema, so breakages surface before the business notices them.
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D
Data pipeline
Pipelines & orchestration
An automated series of steps that moves data from where it is created to where it is used.
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D
Data profiling
Governance & quality
Examining a dataset to measure its structure, completeness and quality before you model it or trust it.
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D
Data silos
Governance & quality
Data trapped in separate systems that don't talk to each other, so no single view shows the full picture.
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D
Data warehouse
Storage & warehouse
A central store that holds structured data from across a business so it can be analyzed in one place.
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D
ELT
Integration
Extract, Load, Transform: raw data is loaded first, then transformed inside the warehouse.
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E
Enterprise data warehouse (EDW)
Storage & warehouse
A company-wide central warehouse that unifies data from every department into one store for organization-level reporting and analytics.
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E
ETL
Pipelines & orchestration
Extract, Transform, Load: the three-step process that moves data out of business systems, cleans it, and loads it into a central store for analysis.
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E
ETL vs ELT
Pipelines & orchestration
ETL transforms data before loading it into the warehouse; ELT loads raw data first and transforms it inside the warehouse.
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E
iPaaS
Integration
Integration Platform as a Service: a cloud platform for connecting apps, data and systems without building integrations from scratch.
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I
Modern data stack
Architecture
The set of cloud tools — ingestion, warehouse, transformation, BI — that replaced the single monolithic platform.
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M
0 definitions live in this first wave — new entries go live every week as the glossary grows.
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