The vision of Hadoop as more than a data store is finally a reality. Thanks to advances from SQL query engines like Spark SQL, Impala, Presto and Hive on Tez, big data technologies are now accessible for business analytics. With Looker, analysts can build a data model across all their data in Hadoop - easily transforming raw data into meaningful metrics and finally allowing business teams to access and explore years of stored data in Hadoop data sets.
Overcome analytic challenges on Hadoop data:
- Get more from your Hadoop cluster by analyzing the data where it sits
- No need to move or transform the data prior to performing analysis
- Interact with your data through a familiar language - SQL on Hadoop
- Create a single source of truth for your enterprise that’s governed by a data model
Our platform has achieved unprecedented growth in deployments across both enterprise and cloud service providers because it succeeds in easily and cheaply capturing huge amounts of unstructured data. As a data store, the Hadoop File System (HDFS) has quickly emerged as a standard for storing and managing petabytes of data of all types. However,The ability to turn this growing deluge of data into actionable intelligence and useful interactive applications has been a more difficult and complicated problem to solve. What are the challenges and tradeoffs for implementing interactive analytics for Hadoop? The early success of the Hadoop platform has inspired exciting many exciting conversations between business, academic and scientific professionals about the possibilities now available with such a wealth of data. This resulted in a dizzying array of databases and programmatic approaches being built in an attempt to extract value from Hadoop data.
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