According to a blog post by SHARE, Mainframes run 30 billion transactions per day, hold 80 percent of the world’s business data and handle 90 percent of all credit card transactions. So, if you bought something online today, more likely than not, a mainframe made that possible.
The new normal brought about by the COVID pandemic has been a major disrupter across so many industries. It’s certainly been an accelerator for the massive shift to digital, with a boom in virtual interactions and software to enable the minimization of physical contact. Conference calls are now ubiquitous, companies are holding virtual events, and many people are choosing to order food online, as opposed to going to the supermarket or the restaurant. Touchless is now the preferred form of purchasing nearly everything. This shift, which, again, is sector agnostic, is driving demand for the mainframe, which was built to handle high-volume transactions.
Although the mainframe is best designed to handle high-volume transactions with speed and security, using analytics to gain insights from the data being processed can become costly and time intensive. You could take a brute force approach and directly query the mainframe system to access the data. But, this only increases your already hefty MIPS bill and risks negatively impacting your production systems and the experience of your end-users and customers.
Another approach is to offload the data from the mainframe for analytics and other services; however, batch file transfers involves complex, custom coding, requiring highly skilled workers. Batch file transfers can also consume a lot of resources to maintain and run. The results are large files that you must move somewhere, only for the analytics to be run somewhere else on out-of-date data.
In order to cope and be successful in this new normal, the best option is real-time data streaming. However, without the correct tools, you could be looking at a huge amount of manual tuning and optimizing work to support the data and analytics demands of your entire enterprise, taking away precious time from other essential business tasks.
This is where the Qlik Data Integration platform can help you.
- Offload Processing Via Continuous Replication. Replicate data continuously and automatically to one or more targets, supplying real-time access to the data in other platforms.
- Reduce the MIPS Processing Overhead. Employ an agentless, log-based Change Data Capture solution supporting DB2, VSAM and IMS, offering minimal impact to production systems and replicating data at point of occurrence.
- Optimize Data Transfer to Cloud. Support direct and optimized endpoints (connectors) to all the major cloud platforms.
- Capture Once, Deliver To Many Targets. Our platform provides a unique log stream capability that saves data changes from the transaction log of a single-source database and enables them to be applied to multiple targets, without the overhead of reading the logs for each target separately.
- Automate The Data Pipeline For Faster Time To Insights. The Qlik Data Integration platform saves data engineers valuable time, enabling them to automate the availability of accurate, trusted data sets and transform them into analytics-ready data for their business. This automates the entire data warehouse lifecycle and the creation of managed data lakes without coding.
To learn more on how you can go about modernizing your mainframe data to unlock the value held within and accelerate your data analytics, read our whitepaper, “Mainframe Data Modernization,” by clicking here.