Data Warehouse

A data warehouse is a central repository for data which will be used for reporting and analytics. Data comes into the data warehouse from [[Online Transaction Processing|transactional systems]], relational databases, or [[Data Lake|other sources]] usually on a regular cadence. Business analysts, data engineers, data scientists, and decision makers then access the data through [[Business Intelligence|business intelligence]] tools, [[SQL]] clients, and other analytics applications. Because the primary use cases for a data warehouse revolve around analytics, they typically use an [[Online Analytical Processing|OLAP]] technology for performance.

Data Warehouse Advantages

Consolidate data from multiple data sources into one “source of truth”
Optimized for read access which makes generating reports faster than using a source transaction system for reporting
Store and analyze large amounts of historical data

Data Warehouse Disadvantages

A significant investment of time and resources to properly build
Not designed for ingesting data in real-time (although they can typically handle near real-time)

When to use a Data Warehouse

Data warehouses are made for complex queries on large datasets. You should consider a data warehouse if you’re looking to keep your historical data separate from current transactions for performance reasons.

Popular Data Warehouses

[[Tools/Databases/Amazon Redshift|Amazon Redshift]]
[[Azure Synapse Analytics]]
[[Google BigQuery]]
[[Microsoft SQL Server]]
[[Snowflake]]

Data Warehouse Benchmarks

![[Learning Resources#Data Warehousing Learning Resources]]