According to Forrester Research, the amount of data businesses retain for analytic purposes is growing at a rate of 50% per year, and in some industries such as Web, ecommerce, retail, telecommunications and government, the growth rate is even higher. Just a few years ago, the data used for business intelligence purposes was stored in a centralized data warehouse and a few departmental data marts. But now, the skyrocketing demand for better business intelligence data has created a vast array of distributed data repositories that run throughout organizations, which has resulted in increased complexity and costs for businesses wishing to maximize their use of analytic data.

To mitigate these issues, leading modern businesses such as Los Alamos National Labs, MIT Lincoln Lab, Cox Communications, and others have selected MySQL to power their growing data warehouse infrastructure. The growth of MySQL in the area of data warehousing recently prompted Gartner Group to include MySQL in their 2006 Magic Quadrant for Data Warehouse DBMS Servers.

MySQL is uniquely designed to easily handle the most common data warehousing use cases:

Data Marts
Traditional Data Warehouses
Large Historical/Archive Data Warehouses
Real Time Data Warehouses
MySQL offers other storage engines that can also be used for data warehousing as well. MySQL supports these key data warehousing features:

Data/Index partitioning (range, hash, key, list, composite) in MySQL 5.1 and above
No practical storage limits with automatic storage management
Built-in Replication
Strong indexing support (B-tree, fulltext, clustered, hash, GIS)
Multiple, configurable data/index caches
Pre-loading of data into caches
Unique query cache (caches result set + query; not just data)
Parallel data load
Multi-insert DML
Read-only tables
Cost-based optimizer
Wide platform support
Native Storage Engines
MySQL currently offers a number of its own native Storage Engines, including:

InnoDB
MyISAM
Cluster
Federated
Archive
Merge
Memory
CSV
Blackhole