Single Version of Truth
High ETL complexity and costs, data latency and redundancy, and batch window limits are just some of the IT challenges caused by legacy data warehouses. Using Hadoop as an Enterprise Data Hub can reduce traditional enterprise data warehouse (EDW) costs and improve performance.
By establishing Hadoop as an Enterprise Data Hub – where you can store and process all of your data in one place – you can run multiple transformation jobs and deliver information to multiple systems. The Enterprise Data Hub enables faster analytics and makes it possible to consolidate infrastructure (hardware and software) within the Hadoop infrastructure.
Modern Enterprise Data Hub Architecture
An enterprise data hub is a single, consolidated, fully populated data archive that gives unfettered user access to analyze and report on data, with appropriate security, as soon as the data is created by the transactional or other source system. The enterprise data hub is made possible with open source big data tools.
Hadoop ETL Modernization
Traditional ETL processes are not adequate for big data operations – they can’t handle the volume or provide results quickly enough to be actionable.Traditional ETL Pain Points
- Not able to meet production schedules
- ETL complexity– cost of software and management
- Time to setup ETL data sources for each project
- Latency in data (up to weeks in some cases)
- EDW unable to handle load
- Mainframe workload over-consuming capacity
- Multiple copies of data—no single version of truth
MetaScale helps customers establish Hadoop as an Enterprise Data Hub to be able to source data once in its most granular form and re-use many times without further sourcing efforts. With a Hadoop Enterprise Data Hub, ETL can be transformed to ELT (T-T-T).