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GridGain 2020 Predictions: In-Memory Computing and HTAP in 2020 - Real-Time Business Processes Get Real

VMblog Predictions 2020 

Industry executives and experts share their predictions for 2020.  Read them in this 12th annual series exclusive.

By Nikita Ivanov, CTO and founder, GridGain

In-Memory Computing and HTAP in 2020 - Real-Time Business Processes Get Real

Gartner has predicted that by 2020, in-memory computing will be incorporated into most mainstream products. This is not surprising because mature in-memory computing platforms can deliver up to a 1,000x performance improvement over disk-based databases. In 2020, as more companies adopt these platforms in search of those performance gains, two critical trends will accelerate.

First, organizations are now migrating from their bifurcated architectures that rely on separate transactional and analytical databases to a unified hybrid transactional/analytical processing (HTAP) architecture or to digital integration hub architectures. HTAP enables simultaneous transaction and analytics processing on the same dataset. By eliminating the time-consuming extract, transform, load (ETL) process, HTAP is powering real-time digital business models and IoT applications across a range of verticals, including financial services, software, e-commerce, retail, online business services, healthcare, telecom, transportation and other major sectors. 

The second in-memory computing trend that will accelerate dramatically in 2020 is the adoption of in-memory computing solutions for digital integration hubs. Beyond the well-established general use of in-memory data grids to create digital integration hubs, a new use case specifically for mainframe computing is emerging. Many Fortune 100 companies rely on mainframe computing for mission-critical, high-value transaction processing. These systems are often paired with a separate data lake deployed off the mainframe, typically using Apache® Hadoop®.

With the announcement that the GridGain in-memory computing platform has been optimized for the IBM z/OS operating system, businesses can now deploy digital integration hub architectures that allow them to run real-time analytics across combined operational and historical datasets.

This capability allows them to accelerate the digital transformation of core systems and power real-time business processes by creating comprehensive views across their data. For example, banks can now create comprehensive 360-degree customer views by leveraging their operational and data lake data to power applications such as customer upsell and cross-sell opportunities.

As a result of these two accelerating trends, 2020 will be the year when organizations of any size can run real-time business processes based on analytics across their entire data estate, leveraging existing infrastructure investments, in some cases even including mainframes, to power their real-time customer interactions.


About the Author

Nikita Ivanov 

Nikita Ivanov is founder and CTO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita provides the vision and leadership at GridGain to develop the world's top in-memory computing platform, now used by thousands of organizations around the globe to power business-critical systems and enable digital transformation initiatives.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe's largest system integrators in 1996.

He is an active member of Java middleware community, contributor to the Java specification, and holds a Master's degree in Electro Mechanics from Baltic State Technical University, Saint Petersburg, Russia.

Published Friday, January 31, 2020 7:29 AM by David Marshall
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