One of the big challenges with big data analytics is corralling massive amounts of information into formats that can be used to identify trends, spot problems, predict likely outcomes, and gain other knowledge that can be used to inform decisions. Thus one feature that is vital for a successful big data analytics system (yet is often overlooked) is the need to make the data “over-the-counter” in that the data’s viewers are assisted in easily understanding the data and using it correctly (just as an over-the-counter product must offer labeling and other features to ensure its contents are used correctly). This is especially essential for “high stakes data” used to make decisions with major consequences. A set of data system/reporting standards (involving effective implementation of a help system, supplemental documentation, user-friendly data visualization, etc.)
- Easily access data stored in Hadoop and execute Pig, Hive, and MapReduce data transformations.
- Execute data quality jobs inside Hadoop to generate clean data. Explore and Visualize
- Quickly visualize your data stored in Hadoop, discover new patterns, and publish reports.
Analyze and Model
- Apply domain-specific, high-performance analytics to data stored inHadoop.
- Uncover patterns and trends in Hadoop data with an interactive and visual environment for analytics.
Deploy and Execute
- Automatically deploy analytic models to score data stored inside
- Hadoop. Reduce data movement and get results faster.