Leveraging Birst’s leading data warehouse automation technology, the Birst In-Memory Database is optimized exclusively for analytics and integrated completely into Birst’s end-to-end BI platform. By optimizing data structures for analytics and managing data in-memory, database operations execute with maximum efficiency, achieving dramatic gains in responsiveness.
Standard JDBC Interface
Birst’s in-memory database has a standard JDBC interface and can integrate with any tool or infrastructure that supports JDBC. It enables IT to deliver BI applications that adapt quickly to changing requirements while business users can perform complex analysis such as multi-pass queries and complex expressions on the fly. This removes the limitations common to traditional BI and data discovery tools.
Analytics at the Speed of Thought
Birst’s SQL-based, columnar database is designed to allow users to transform enterprise data into intelligent analytics at superior performance. Business users now have a way to explore all their data relationships—unconstrained by the inherent limitations of single-table or associative in-memory models implemented with discovery tools. Standards-based and open, the Birst In-Memory Database also doesn’t come with the jaw-dropping price tag of the largest vendor’s in-memory databases. Key benefits include:
- Complex analytics, made easy: A real relational database capable of complex (e.g. multi-pass) and ad hoc analysis of sophisticated relationships not possible with data discovery tools such as QlikView and Tableau
- Sophisticated queries: Support for all types of SQL queries, sub-queries and complex expressions
- High data compression: Data compression rates typically ranging from 1:3 and 1:10. Data is decompressed on the fly and cached for optimal reuse
- Standards-based: An in-memory database that is based on the universal database standard query language, SQL. Birst’s database is interoperable with existing applications and allows for open access via JDBC
- Performance: Columnar data storage and parallel processing speeds performance for analytics to achieve dramatic gains in responsiveness
- Low Cost:Intelligent data decompression for optimal price performance
