Industry executives and experts share their predictions for 2020. Read them in this 12th annual VMblog.com 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.
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About the Author
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.