Clusterpoint is deployed in organizations that need to :
- break down data silos and provide unified access to information assets
- derive best value from unstructured content by using search based analytics
- deliver next generation mobile apps using Google-like search for fast data access
Clusterpoint solutions include:
- Data Unification – data or metadata is extracted from existing systems (or data silos) and then blended into a centralized Clusterpoint database. This approach is typically used to drive explorative analytics and to speed delivery of powerful search based apps.
- Data Hub – data from internal systems is mashed up with external feeds to build a centralized hub. That hub is then used to spin out data to downstream systems for consumption or analysis. Clusterpoint excels as a data hub when extracts are based on complex text searches.
- Product Catalog – for managing thousands of complex product types in a single database. When used as a product catalog, Clusterpoint speeds the delivery of POS applications, e-commerce sites and mobile apps; guaranteeing consistency across sales and partner channels.
- Big Data Search – tens of terabytes of data are loaded, indexed and stored in real-time for explorative analysis. Clusterpoint’s approach to big data fits with organizations that want to use rich text searches at the core of their analyses and have a real need for low latency.
Clusterpoint also makes a bunch of sense for those that want big data benefits but simply don’t qualify for the petabyte Hadoop club.
If you are wondering where Data Unification ends and a Data Hub starts then don’t. No surprise that many of our deployments lie somewhere between one solution and another.