Organizations everywhere are grappling with how to manage their growing big data sets from ERP and e-commerce systems, log files, sensor data, social media and more. Apache Hadoop provides a cost-effective enterprise data hub (EDH) to store, transform, cleanse, filter, analyze and gain new value from all kinds of data.
Specific uses cases include:
- Data Reservoir or “Data Lake”: Collecting raw data which was previously too expensive to store and process. Data is managed and governed here and can also act as an online archive for data infrequently accessed.
- Data Refining: Optimize the process of integrating diverse data types from multiple sources to discover relationships. Parse, cleanse, transform, and integrate data.
- Big Data Exploration: Perform investigative analytics on large data volumes of unknown value. Apply a combination of SQL-on-Hadoop, machine learnings, statistics, and graph analysis techniques to unlock new insights and improve operational analytics such as anomaly detection and recommendations.
- Data Warehouse Optimization: Capture, store, and refine incoming big data in an enterprise data hub (EDH) to free up valuable processing and storage space on the data warehouse for mission-critical reporting and analysis. Create online archive of infrequently queried data.
- Mainframe Optimization: Offload data and batch processing to Hadoop to free up expensive MIPS cycles and modernize the enterprise data architecture.