[Webinar] Protect Your Most Trusted Mainframe Data Across the World in an Age of Heightened Cyber-security
Model9 Gravity’s ability to quickly transfer, transform, and integrate your mainframe data allows you to add it to your data lake and use it in AI/ML and Analytics cloud applications. Eliminate performance bottlenecks and put your data to work at the rhythm of your business, instead of the other way around.
With Model9 Gravity, clients can now move PetaBytes of data between the mainframe and on-premise object storage or cloud object storage such as Amazon Simple Storage Service (Amazon S3) or Azure Blob Storage. The use cases are limitless, ranging from simply moving a file to an object storage platform inside the walls of your data center to moving large files for integration into a cloud application running on a public cloud.
Traditional ETL-dependent processes required picking data sets one at a time. Model9 Gravity uses zIIP to queue data for transfer over TCP/IP into the cloud. This speeds data extraction and transformation, making the processes highly scalable.
Model9 Gravity uses ELT (extract, load, transform) architecture to deliver mainframe formatted data to object storage in the cloud, and then transform it via the target platform. In contrast, the antiquated ETL (extract, transform, load) approach uses mainframe CPU processing to transform data into open formats first, and then moves it into the cloud.
Change Data Capture (CDC) tools focus on replicating updates primarily in databases. Model9 Gravity does not impact online or batch windows, and directly delivers and transforms both live data from disk and historical data on tape in the cloud.
Mainframe data sources include:
Db2 image copy, VSAM data sets, Sequential data sets, Partitioned data sets, Extended format data sets, Db2 archive logs, COBOL copybooks.
Target cloud formats and services include:
JSON, CSV, XML file formats, Amazon Athena, Aurora, QuickSight and Redshift, Microsoft Azure HDInsight and SQL Data Warehouse, Google BigQuery and BigTable, Snowflake, Apache Spark, Hadoop, Splunk