BMC AMI Cloud Analytics can quickly transfer, transform, and integrate your mainframe data so you can add it to your data lake and use it in cloud-based artificial intelligence and machine learning (AI/ML) and analytics applications. Eliminate performance bottlenecks and put your data to work at the rhythm of your business, instead of the other way around.
With BMC AMI Cloud Analytics, clients can move petabytes of data between the mainframe and on-premises 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. BMC AMI Cloud Analytics uses zIIP to queue data for transfer over TCP/IP into the cloud. This speeds data extraction and transformation, making the processes highly scalable.
BMC AMI Cloud Analytics uses an ETL architecture to deliver mainframe-formatted data to object storage in the cloud, and then transform it via the target platform. In contrast, the antiquated extract, transform, load (ETL) approach uses mainframe CPU processing to transform data into open formats first, and then move it into the cloud.
CDC tools focus on replicating updates primarily in databases. BMC AMI Cloud Analytics does not impact online or batch windows, and directly delivers and transforms both live data from disk and historical data on tape to the cloud.
Mainframe data sources include:
Target cloud formats and services include:
With BMC AMI Cloud, we modernized legacy infrastructure, unlocked new levels of efficiency, and saved money. — Kent Swenson, Vice President Information Systems at America First Credit Union
