Data Warehouse

Data Storage and Sources
Updated on:
June 13, 2024

What is a Data Warehouse

A data warehouse is a centralized repository that integrates data from multiple sources into a consistent, cleansed and standardized schema optimized for analytics and reporting. It serves as a single source of truth for enterprise data.

Data warehouses transform raw data into structured formats using ETL processes to enable business intelligence and analytics. They are a core component of modern data lakes and data orchestration pipelines.

What does it do/how does it work?

A data warehouse consolidates data from transactional systems, databases, IoT devices, social media and other sources into a unified schema. It applies data cleansing, transformations, aggregations and business logic to present integrated views of business data.

Analytical tools and dashboards can then run high-performance queries against the integrated data in the warehouse to drive business insights, forecasts and decision making.

Why is it important? Where is it used?

Data warehouses enable using data for strategic business intelligence as opposed to just transactional operations. They provide the trusted information backbone for analytics across sales, marketing, finance, supply chain and more.

With a single integrated view of enterprise data, data warehouses deliver the reporting, segmentation, forecasting and predictive models essential for data-driven management and optimization of business processes.


What are the main components of a data warehouse?

A data warehouse is a centralized repository that integrates data from multiple sources to support analytics and reporting. The key components provide capabilities for data integration, storage, management, and access.

  • Central and integrated database for unified data storage.
  • Schema structured for analytics like star/snowflake schemas.
  • ETL tooling for data manipulation, transfer and integration.
  • Metadata repository for data definitions and mappings.
  • Access tools such as query, reporting, development, mining and OLAP tools.

When should you use a data warehouse?

Data warehouses enable consolidated data and are well-suited for certain use cases needing integrated data at scale for analytics.

  • When you need integrated data for reporting and analytics.
  • To implement enterprise standard schemas and semantics.
  • To optimize complex analytical workloads.
  • When you need historic data for trends and insights.
  • When you need to integrate data from multiple sources into a single source of truth.

What are key data warehouse challenges?

Data warehouses come with inherent complexities around scale, operations, and governance:

  • High cost of enterprise-scale implementations.
  • Maintaining data synchronization with sources.
  • Scaling size and query performance as data grows.
  • Inflexible and slow ETL processes.
  • Providing self-service access and governance.
  • Maintaining data quality over time.
  • Ensuring data security and compliance to relevant regulations.

What are examples of data warehouse solutions?


Related Entries

Data Lake

A data lake is a scalable data repository that stores vast amounts of raw data in its native formats until needed.

Data Processing Engine

A data processing engine is a distributed software system designed for high-performance data transformation, analytics, and machine learning workloads on large volumes of data.

Data Orchestrator

A data orchestrator is a middleware tool that facilitates the automation of data flows between diverse systems such as data storage systems (e.g. databases), data processing engines (e.g. analytics engines) and APIs (e.g. SaaS platforms for data enrichment).


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