New York – Strata Data Conference – Qlik® continues to execute on its mission to supply enterprise customers with end-to-end data management and analytics solutions with two new big data offerings - the latest version of the Podium Data® product and the initial release of Qlik’s Associative Big Data Index available this month. Enterprises need to unlock data’s full value to help users accelerate action and outcomes. With these latest releases, Qlik continues to aggressively streamline the enterprise data journey from raw data source to end user insights.
“Qlik is committed to improving enterprise data readiness and analytics scale,” said Mike Potter, Qlik CTO. “Delivering a new version of Podium alongside the first version of the Qlik Associative Big Data Index is a clear signal to the market: Qlik is ready to help customers drive more value from all of their data across their entire user base - whether big, small, or any combination thereof.”
“Optimizing data from raw inputs to insights is proving to be the real game-changing advantage for businesses,” said Paul Barth, Qlik’s Managing Director of Enterprise Data Management. “Untangling data, bringing higher value to data assets and making data easier to work with is at the core of our Podium product enhancements.”
Podium Data: Increased Agility with Enhanced Smart Data Catalog and Intelligent Rules Engine
The latest release of the Podium Data product contains many new features to help users derive more insights and benefit from Podium’s smart data catalog. Two key aspects, the new catalog module and intelligent rules engine, both accelerate time-to-answers for all users who rely on data.
- New Catalog Module – Podium’s smart data catalog now delivers richer search and data drill-down capabilities. New Operational, Quality and Popularity data metrics within the smart data catalog give users an Amazon-like shopping experience when searching, browsing, previewing and choosing information from the data marketplace. Used standalone or integrated with Qlik, the new catalog module helps customers tap into all their data wherever it resides, expanding enterprise data management capabilities to transform their raw data into governed, analytics-aware information resources.
- Intelligent Rules Engine – Based on the popular open source Drools rules management system, Podium’s code-free, customizable rules engine analyzes incoming data and takes appropriate action based on outcome. For example, this feature can be used for intelligent data identification and action on sensitive data such as personally identifiable information (PII), PCI data, and data subject to GDPR regulations. Other examples include the ability to automatically launch custom scripts or send notifications to data stewards when unusual data patterns are observed.
Qlik Associative Big Data Index: The Associative Difference® for Big Data Analytics
The Qlik Associative Big Data Index delivers Qlik’s patented associative experience on top of extremely large-scale data sources, allowing users to freely explore and search big data repositories, including full access to all the underlying details, while leaving the data where it resides. This governed, high-performance Associative Engine can be deployed within big data repositories, eliminating the need to transfer and prepare the data elsewhere before it can be analyzed. Qlik’s Associative Big Data Index includes the ability to:
- Continuously provide a complete, up-to-date view of the data since the index immediately updates alongside data source updates;
- Leverage Docker containers, allowing organizations to be platform agnostic and freeing them from being tied to a single big data repository and related strategy;
- Distribute processing across a cluster to maximize performance using Kubernetes to tackle even the largest scale data sources.
As previously announced at Qonnections 2018, the initial fall release of the Qlik Associative Big Data Index is a services-led offering. Interested customers should contact their local Qlik representative for more details about this initial release or visit https://www.qlik.com/us/bi/big-data for more information.