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Tervela Turbo Solves Big Data Analytics Bottlenecks

Supports real-time business analytics with reliable big data capture and scalable distribution.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Tervela, a data fabric provider, announced a new product built to help businesses drive adoption of big data analytics for mission-critical, time-sensitive business processes. Tervela Turbo, based on Tervela’s data fabric platform, is a high-performance data movement engine that feeds mission-critical applications with large-scale, streamed data. The product was built for real-time business intelligence and operational analytics.

Big data is playing a growing and critical role in day-to-day business operations, helping companies compete more effectively, and become more efficient, but difficulties in capturing this data and delivering it to front-line business systems has slowed down what companies can do with the data at their fingertips. Tervela Turbo solves these challenges, enabling companies to:

  • Improve service levels for mission-critical systems that rely on big data

  • Save storage and bandwidth costs by reducing data replication and data duplication needs

  • Ensure that original “golden” data is archived without modification for auditing and security

  • Layer and stream analytics directly into data processing for inline decision-making

Tervela Turbo achieves these benefits by capturing data from enterprise sources, making it available wherever it is needed, and distributing it to the explosion of downstream applications that want to use it.

“Tervela Turbo enables companies to build more powerful analytics systems that tap into the enterprise data stream without disrupting mission-critical systems that rely on the same data sources,” said Barry Thompson, CTO of Tervela. “Data architects can feed their Hadoop environments with the same information that's going into their data warehouses. Businesses are pushing the edge of what's capable for analysis in enterprises and can now do so with confidence.”

Tervela Turbo allows enterprise data organizations to acquire, share, and distribute data from hundreds of data sources to diverse and scaled out downstream environments. Tervela Turbo addresses the critical areas that organizations need to leverage big data and powerful analytics for day-to-day operations:

  • Real-time data capture: Businesses operate 24x7, but traditional data analytic environments were built to deliver information on a daily basis or longer. Tervela Turbo ensures that data is delivered downstream as it comes in, as fast as applications can consume it, to ensure that real-time analytics can function smoothly and without interruption.

  • Continuous, reliable access to data: Tervela Turbo provides fault tolerance, burst management, and other features to ensure that data is delivered reliably under any circumstance to the analytic systems that are making decisions about business operations.

  • Data distribution across multiple platforms: Analytic systems are growing and diversifying at an alarming rate. Tervela Turbo ensures that any application can tap into the same enterprise data stream without interfering or disrupting the other applications that rely on that data. New analytic systems can be built at a rapid pace, tested in the field, and deployed in production without risk to existing mission-critical business systems.

  • Connecting an explosion of data producers and consumers: Tervela Turbo allows companies to collect data from hundreds of thousands or millions of end-user devices such as mobile phones, smart meters, web logs, sensors, and more. Once captured and analyzed, information can be sent to a similarly diverse set of consumers, in-store systems, logistics centers, or used for other purposes.

Tervela Turbo delivers numerous features for high-performance, parallel data loading across multiple heterogeneous data repositories, supporting structured, pre-structured, and unstructured data. These features include:

  • Hadoop Loader: High-performance data loading into Hadoop processing or HDFS repositories, and integration of Hadoop as a massive data store and transformation engine.

  • Teradata Loader: High-performance, parallel loading into Teradata for active-active data warehouse configurations to support big data disaster recovery.

  • Database Replicator: Changed data capture and parallel replication for databases including Oracle, Microsoft SQL Server, MySQL, IBM DB2, and others.

  • File Replicator: File system replication and synchronization.

  • Web and Mobile Assistant: Embedded data capture and distribution using publish/subscribe for web and mobile applications using Javascript and Node.js APIs.

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