Are you looking for the best way to migrate and replicate your mainframe data? Google Cloud and Confluent have teamed up to provide an end-to-end solution for connecting your mainframe application data with the advanced analytics capabilities of Google Cloud.
In this article, we will discuss how you can use Confluent Connect to replicate messages from IBM MQ and Db2 to Google Cloud. This allows you to work with your mainframe data in the cloud, and enables you to build new applications and analytical capabilities using Google Cloud’s machine learning solutions. You also benefit by reducing impact on your production mainframe workloads, and reducing general purpose compute costs. In other words, you can continue using your mainframe to run your mission-critical business workloads while setting your data in motion for innovation.
Here’s an example use case that demonstrates how using the Confluent MQ connector with Google Cloud can impact your bottom line. One of our customers is saving millions of dollars per year on mainframe cycles by leveraging z Integrated Information Processor (zIIP) engines for data processing.
Moving these workloads to zIIP, off of GP (general purpose) compute, and away from CHINIT (Channel Initiator) routes directly leads to reduced MSU licensing. As an example, a customer in the financial services industry saw a 50% reduction in CPU usage per message. These cost savings can enable you to direct budget resources toward differentiating activities, such as commercializing your valuable mainframe data to open up new revenue streams and improve customer service.
On the technical side, Confluent guarantees exactly-once message semantics, preserives message order and unleashes that data to be accessed by existing and new applications that need a high throughput, low latency event driven architecture. This means that you can rely on the accuracy and consistency of your data in Google Cloud as if you were querying it directly from your mainframe database.