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Paxata Announces First Apache Spark-Powered Data Prep Runtime Fabric

Paxata releases new adaptive workload management for enterprise big data workloads.

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.

Paxata, a provider of self-service data preparation for analytics, has released its Fall ’18 update to its Adaptive Information Platform. The release includes a new adaptive workload management capability that delivers an elastic resource allocation service on several orchestration frameworks, including Microsoft Azure HDInsight, Kubernetes, and Apache Hadoop YARN. The new offering also enables dynamic scaling of large data prep workloads across ephemeral clusters to lower cost and improve performance.

Building on Paxata’s Spark-based data prep engine, the company now offers a data prep runtime fabric that can dynamically allocate, execute, and release processing resources to dramatically reduce infrastructure and compute costs when running automated batch jobs. The Paxata engine now runs on Kubernetes, a technology for container orchestration

The new adaptive workload management feature lets enterprises choose how to define their own interactive data volumes. Users interactive data prep isn’t limited to small, fixed samples; Paxata is flexible enough to size the interactive dataset per tenant, meaning organizations can align its use to meet specific enterprise use cases and requirements.

To learn more about the new release, visit

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