TIBCO ComputeDB In-memory Database Fused with Apache Spark

Unleash the power of data to meet business demands

The TIBCO ComputeDB™ in-memory optimized analytics database, based on Apache Spark™ and Apache Geode™ (open-source version of GemFire™), delivers high throughput, low latency, and high concurrency for unified analytic workloads. Combine interactive and streaming analytics with artificial intelligence in a single, easy to manage distributed cluster.

Benefits

Speed-of-thought Analytics

Innovate and compete more effectively by analyzing and gaining actionable insights from all your data, regardless of its volume, velocity, or variety. Enable analytics at true interactive speeds and eliminate the need for many database optimization tasks.

Higher Performance, Lower TCO

Realize higher performance and lower TCO for the enterprise through seamless integration of Apache Spark (as a big data computational engine) with Apache Geode (as an in-memory transactional store with scale-out SQL semantics). Streaming analytics, OLTP/OLAP, and machine learning in a single integrated database helps data scientists and data engineers work together more effectively to tackle your biggest challenges.

Any Data, Any Source, Any Cloud

Explore and analyze any data across sources such as streaming; Parquet; Hive; and JSON/XML, NoSQL, relational, and cloud databases; text files, and more.

Flexible Compatibility

Run your Apache Spark programs without changes because the TIBCO ComputeDB in-memory database is 100% compatible with Apache Spark. Applications access the data using JDBC/ODBC/REST, or simply use the enhanced Spark API via Scala, Java, R, or Python.

Capabilities
High Performance & Low-latency

High Performance & Low-latency

This low latency, highly available in-memory database with shared memory management and optimizations increases performance speed up to 20X over native Apache Spark, and scales to support large Apache Spark compatible stores.

Data Mutability

Data Mutability

Native mutability semantics supported from Apache Spark APIs and ODBC/JDBC connections use the usual semantics of insert/update/delete, and new APIs are available through our SQL extension.

Approximate Query Processing

Approximate Query Processing

Intelligent sampling assists in analyzing the largest datasets. Combines state-of-the-art approximate query processing techniques and a variety of data synopses to ensure interactive analytics for both streaming and stored data.

Deployment on Premises or Cloud

Deployment on Premises or Cloud

Deploy on premises using commodity servers, as an Amazon Cloud service, or through Docker or Kubernetes containers. Also available as a Spark package and accessible from any Spark application as a dependency.

High Availability & Recovery

High Availability & Recovery

In-memory synchronous redundancy within a cluster, across racks for HA, or across data centers for disaster recovery. With asynchronous replication, data becomes consistent eventually and can be persisted on disk, each replica an independent copy. Active-active WAN replication, too.

High Concurrency/Elasticity

High Concurrency/Elasticity

Thousands of concurrent ODBC and JDBC clients can simultaneously connect to a cluster. Handle both multiple low latency short operations and complex queries that iterate over large datasets.

Apache, Spark and Apache Spark are trademarks of the Apache Software Foundation" https://www.apache.org/