Today’s intelligence agencies have data from all over the world in all kinds of formats — and they need a solution that can do both very simple and extremely complex data processing and network analysis tasks.
The US Government is actively deploying MapR as the replacement for other Hadoop distributions to better process and analyze fast growing data in a much more cost effective method. MapR’s enterprise-grade features such as Mirroring and Snapshots allow Federal agencies to comply with Continuity of Operations (COOP) requirements. Direct Access NFS™ enables agencies to leverage existing applications. MapR also includes other features that make Hadoop easy, dependable and fast. MapR is currently deployed across IC, DoD and Civilian agencies.
Government agencies and departments use MapR for many use cases, including the following:
- Customer insights - The defense and intelligence industries need to collect and compare information from various sources in order to handle security threats, solve investigations and identify potential security issues. The data rates and volumes available from classified and open sources are prodigious as are the consequent computing demands. Hadoop adoption in the intelligence agencies has been substantial.
- Fraud detection - Demands on social services are increasing due to aging populations and high unemployment. Hadoop can help track and capture fraudulent charges and manage new trends in legitimate service usage.
- Micro targeting - Hadoop has helped government applications by increasing the amounts of information available to identify and reduce criminal activity.
- Operations - Determining the most effective way to allocate resources to achieve operational goals is resulting in numerous Hadoop pilots. One example is helping police forces, strained by tight government budgets, to more effectively allocate patrols to maximize their effect in deterring crime.
- Research and Innovation - NASA and other research institutes need to map the universe, both for research purposes and for more practical goals, such as better planning of satellite routes and identifying landing spots on the moon. Astronomical research and large-scale physics experiments generate huge data-sets putting extreme demands on storage and processing infrastructure.