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1/12/2021 8:48:28 AM
Persistent memory makes its way into business applications
Predictions 2021,Digital Transformation,Persistent Memory
App Developer Magazine
Persistent memory makes its way into business applications


Persistent memory makes its way into business applications

Tuesday, January 12, 2021

Richard Harris Richard Harris

Nikita Ivanov, founder, and CTO of GridGain predicts that in 2021 businesses will start focusing on rapidly scaling out applications to meet challenges of digital transformation, digital integration hubs will power transformations, HTAP adoptions will increase, and persistent memory makes its way into business applications.

In 2021, more businesses will focus on rapidly accelerating and scaling out applications to meet the challenges of digital transformation.

In 2020, the COVID-19 pandemic drove many businesses, especially those in food delivery, eCommerce, logistics, and remote access and collaboration services, to dramatically scale-out and upgrade infrastructure to maintain high application performance in the face of surges in website visitors, delivery requests, sales transactions, video streaming and more.

Many of these businesses found that the fastest approach to maintaining or improving performance while simultaneously increasing application throughput was to deploy a distributed in-memory data grid (IMDG) – built using an in-memory computing platform such as Apache Ignite – that can be inserted between an existing application and disk-based database without major modifications to either. The IMDG improves performance by caching application data in RAM and applying massively parallel processing (MPP) across a distributed cluster of server nodes. It also provides a simple path to scale out capacity because the distributed architecture allows the compute power and RAM of the cluster to be increased simply by adding new nodes. 

In 2021, IMC platforms will become easier to use and the number of knowledgeable IMC practitioners will continue to grow rapidly. This will enable IMC adoption to spread across more industries and to a wider pool of companies. As a result, more businesses will be better positioned to take advantage of IMC for rapid application acceleration, not just for a response to the demands of COVID, but also to meet new strategic and competitive demands as the pandemic threat abates.

Digital integration hubs will power digital transformations

In 2020, enterprises of all sizes continued to push forward with and even accelerate their digital transformations. In 2021, many of these businesses will want to leverage their expanding digital infrastructure to drive real-time business processes powered by 360-degree views of customers and their business. This demand will lead to widespread adoption of real-time digital integration hubs (DIHs), also known as API platforms, smart data hubs, or smart operational datastores. 

Powered by an in-memory computing data grid (IMDG), a DIH creates a high-performance data access layer for aggregating a subset of data from multiple source systems. These source systems may include relational and NoSQL databases, data warehouses, data lakes, and streaming data that may reside in public or private clouds, on-premises datacenters, mainframes, or SaaS applications. The aggregated data in the DIH can be simultaneously accessed by any number of business applications at in-memory speeds. The IMDG powering the DIH supports a range of APIs, including key-value and SQL, and some even offer ACID transaction support.  A synchronization layer, or change data capture layer between the data sources and the IMDG, ensures that the data in the in-memory cache is constantly updated as changes are made to the underlying datastores.  

With an IMC-powered DIH, businesses can launch a variety of critical real-time initiatives, from predictive analysis and recommendation engines to automated business decision making, that instantly react to real-time cross-organization data views, whether in financial services, healthcare, logistics, or a range of other industries.

Persistent memory makes its way into business applications

Persistent memory (PM) solutions such as Intel Optane ensure RAM is not flushed when the servers restart. Instead, data that resides in persistent memory is immediately available for processing at in-memory speeds following a reboot. Some persistent memory solutions also offer the ability to insert more PM RAM per server than traditional DRAM, enabling more memory in a single server, which enables in-memory speeds for much larger datasets on the same server. Combining persistent memory technologies with an in-memory computing platform, such as Apache Ignite, can enable a comprehensive infrastructure for achieving high performance and massive scalability of production applications for both existing and greenfield use cases. This will be particularly useful for high performance, 24x7 applications for industries such as financial services and where high performance, cost-effectiveness, and immediate recovery from any server restarts is crucial.

In 2021, we will start to see a significant acceleration of the adoption of persistent memory in business applications. This will take the form of code updates to business applications and the adoption of persistent memory as a standard option with an increasing number of third-party solutions.

In-process HTAP will see growing adoption 

HTAP – or hybrid transactional/analytical processing – enables simultaneous transaction and analytics processing on the same dataset. By eliminating time-consuming ETL processes required to move transactional data into an analytical data store, HTAP can power real-time digital business models across a range of verticals, including financial services, e-commerce, healthcare, transportation, and many more. 

“In-process HTAP,” a term created by Gartner, are HTAP systems that can drive real-time machine learning into business processes by combining HTAP with machine learning (ML) models that are trained continuously using the HTAP data. Powered by an IMDG, such continuous learning environments allow real-time updates to the machine learning model used in the associated business applications. For example, to minimize new loan scams, a bank could continuously update its machine learning model of what indicates a loan fraud attempt based on the incoming real-time data of new loan applications. It can then seamlessly push the resulting updated model into its production systems.

The maturing of in-memory computing platforms in 2020, combined with soaring demand to roll out applications that can update their machine learning models in real-time will drive an increasing rate of adoption of in-process HTAP in 2021.

About Nikita Ivanov

Nikita Ivanov, founder, and CTO of GridGain Systems has led GridGain in developing advanced and distributed in-memory data processing technologies. Nikita has more than 20 years of experience in software application development, building HPC and middleware platforms, and contributing to the efforts of other startups and notable companies, including Adaptec, Visa, and BEA Systems. In 1996, he was a pioneer in using Java technology for server-side middleware development while working for one of Europe’s largest system integrators. Nikita is an active member of the Java middleware community and a contributor to the Java specification. He is also a frequent international speaker with more than 50 talks at various developer conferences in the last 5 years.

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