Qlik Unveils New Podium Data-Powered Data Management Product

Qlik Unveils New Podium Data-Powered Data Management Product

Source: Qlik

Qlik has announced the release of its new Podium Data-powered data management solution called Qlik Data Catalyst 4.0. Qlik entered the broader data management marketplace last July with its acquisition of Podium Data, and this product is the first fruit of that merger. The release is highlighted by support for single server and AWS-based architectures, as well as support for data sources beyond those within the Hadoop ecosystem. Users can also curate and catalog data on a single server without a Hadoop cluster.

Data Catalyst includes ties to the Amazon cloud and improved support for transient EMR clusters and S3, as well as the ability to publish to RDBMS. The product allows users to infer and label data elements based on data structure and context through a new relationship inference engine. The metadata is visible in the catalog and can be pushed to Qlik Sense or set aside as a new data set or group for later analysis. Users can also import relationships defined in catalogs or modeling tools into Catalyst.

Qlik can leverage data relationships from both the new relationship inference engine and other tools when publishing a collection of related data sets in Qlik Sense. This allows customers to utilize augmented analytics and machine learning functionality within Qlik Sense more simply. Administrators also gain improved monitoring of data preparation, delivery and analysis.

In a statement to the media, the company’s Managing Director of Enterprise Data Management (and former Podium Data CEO) Paul Barth said of the release: “Qlik Data Catalyst is expanding to meet customer data management needs wherever their data resides and in whatever form it takes. This brings more order and value to all of an organization’s data today and in the future.”

This announcement comes on the heels of Qlik’s late-2018 Associative Big Data Index release, a tool that makes Qlik’s associative experience available on top of very large data volumes.

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