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Data & AI

A data warehouse is a centralised, integrated repository of structured historical data from multiple operational systems, optimised for analytical queries and business intelligence reporting rather than transactional processing.

Unlike operational databases (OLTP) optimised for high-speed individual row inserts and updates, data warehouses are columnar stores (OLAP) optimised for scanning large volumes of data across many rows to compute aggregations — queries like 'total revenue by region for the last five years' that would cripple a production database. Data enters a warehouse through ETL or ELT pipelines that extract from source systems (CRM, ERP, web analytics), transform data into a consistent format and schema, and load it into the warehouse on a scheduled or streaming basis. A star schema or snowflake schema organises fact tables (events and transactions) and dimension tables (customers, products, time) for efficient analytical joins. Leading data warehouse platforms include Snowflake, Google BigQuery, Amazon Redshift, and Databricks, all of which decouple storage from compute to enable elastic scaling.

Example

A retail chain consolidates sales data from 120 stores, its e-commerce platform, and its loyalty programme into a Snowflake data warehouse, enabling analysts to run cross-channel revenue attribution reports that previously took days in seconds.

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