The objectives of the AXLE project are to greatly improve the speed and quality of decision making on real-world data sets. AXLE brings together a diverse group of researchers covering hardware, database kernel and visualisation experts all focused on solving the needs of extremely large data analysis. Real data sources have many difficult characteristics. Sources often start small and can grow extremely large as business initiatives succeed, so the ability to grow seamlessly and automatically is at least as important as managing large data volumes once you know you have them. Extremely large data stores have added concerns such as data quality, privacy, security, and auditability. Aspects of the project will include: Scalability engineering—autopartitioning, compression; Security, privacy, and audit techniques; Visual analytics; Advanced architectures for hardware and software; Validation will be carried out by industrial consortium partners with access to large volumes of private medical data, as well as other wide ranging data from other interested parties. Software features will be released as commercially-usable open source code, and submitted for wide use as core parts of the PostgreSQL database or Orange visualisation project, or pluggable extensions for these tools. Project web site: http://axleproject.eu/