Power Your ETL Processing with Spark
As the center of gravity of data moves toward the Hadoop ecosystem, data integration professionals are looking for the best options to efficiently move and process big data loads.
Spark offers powerful in-memory processing that Developers need for developing ETL applications on large data sets, but the benefits of the platform can be limited by hand-coding inefficiencies.
Watch this on demand webinar on “Power Your ETL Processing with Spark” to learn how Hydrograph - the next-generation open source, GUI-based ETL tool - provides an easy-to-use option that:
- Connects to any operational system to bring structured, semi-structured and unstructured data into Spark
- Automates data warehouse offload to Spark
- Provides a flexible open source framework that can easily move ETLs to new technologies (such as Hadoop and Spark in Cloud) to avoid obsolescence
Hadoop has been a proven workhorse for big data processing, but if Spark is the platform you are using to maximize speed, then watch now to learn how Hydrograph can accelerate your ETL development in the big data ecosystem.
Who Should Watch?
- VP, Enterprise Information Management
- Director, Enterprise Architecture
- Data Integration Manager
- ETL Developer
Why Watch?
- Future-proof ETLs
- Drive Spark adoption
- Cut associated ETL development costs
- Avoid exposure to coding inefficiencies
- Support your open source strategy
