Hadoop Integration - Integrates with Hortonworks data platform and analyzes data across the insurance domains, in Hadoop clusters and ETL environments. It enables improved big data and advanced analytics from multiple sources. ESS loads data into Hortonworks Data Platform from sources like Flat files, XML, RDBMS tables and creates Hive metadata. End to end Insurance data gets streamlined for advanced analytics.
Now that you’ve loaded petabytes of data into your Hadoop cluster and you’ve built some Hive queries against it, what do you do with it? The queries are not fast enough to do ad-hoc discovery and you have no way to present the data in a meaningful way. You’d like to leverage the benefits of business intelligence you have in Bigdata Analytics, but don’t know the best practices around how to leverage it and how you can scale with the user and data growth.
Direct Access – Using Hadoop
The reporting driver for Hadoop will translate your analytic requests to Hive SQL and enable rich analytical capabilities and visualizations as soon as the data is loaded. However, practically speaking, the high latency of ad-hoc queries will quickly test your patience. Once the Bigdata Analytics business intelligence architecture has been built upon the physical tables in the Hive server, a great way to get around the latency issues is to leverage Visual Insights or Intelligent Cubes. With each, the idea is to bring as much of the data you intend to use into the iServer memory and use OLAP services for aggregation, pivoting, level metrics, and subtotalling. An added benefit of intelligent cubes is the ability to enable dynamic sourcing which allows ad-hoc queries to hit the cube transparently.
Consideration: If you decide to leverage the caching capabilities of Intelligent Cubes or Visual Insights, it’s important for users to be aware that:
If you are looking for Hadoop Integration Services contact ESS sales team at email@example.com