Offload Mainframe Processing to Hadoop Using Diyotta
Rapidly increase Hadoop ROI by offloading legacy batch processes with Diyotta’s mainframe capabilities.
Offloading Mainframe Data to Hadoop
Diyotta addresses the challenges below and provides an easy, drag & drop interface to quickly and efficiently build the data pipelines without any technical knowledge on SnowSQL. Additionally, you can convert the existing stored procs or scripts into metadata driven processes that can run in a set-based manner on Snowflake using Diyotta.
Eliminate Legacy Platform Limitations
Organizations that have mainframe systems typically have large data volumes and processes buried within legacy applications. Often this data is difficult to access for analytics. Diyotta uses business logic to parse and load EBCDIC data into Hadoop to exploit its distributed processing capabilities. Once deployed, mainframe data can be used to address data mining and analytics needs quickly and efficiently.
Ingest EBCDIC files by importing COBOL copybooks
Seamlessly supports complex structures like OCCURS, REDEFINES and multiple code pages
Leverages distributed computing power of Hadoop to parse massive volumes of EBCDIC data
Accelerate data availability by ingesting, transforming and provisioning mainframe data into Hadoop
Enrich data by performing transformations before loading to final target structures
Enhance data analytics by combining mainframe data with new data sets
Diyotta leverages Hadoop and makes it easier for organizations to offload mainframe batch processes without requiring extensive technical skills. Unleash mainframe data for comprehensive analytics and actionable intelligence.