Boost Analytics With In-Memory Data Grids

by Sachin

Every enterprise today, if not completely digital, has a digital aspect or business component. There are large amounts of data to be processed at any given time in today’s always-connected, always-online world. Consumer-facing websites and online portals, require instantaneous data processing, the absence of which, would cause a data bottleneck that will cost a business one lost customer—and an indefinite amount of lost revenue.

In-memory data grids are a thing of the future for businesses, with speeds unmatched by any disk-based storage. Although RAM is more expensive than disk, the costs have become lower through the years that in-memory computing is becoming a more viable option for businesses and organizations that handle huge amounts of data.

In the era of digital transformation spearheaded by the internet of things (IoT), the main challenge is scaling performance of existing applications and systems while minimizing costs. In-memory data grids rise up to this challenge by providing speed and scalability improvements without constant replacement of existing data layers and applications. With in-memory computing, scaling systems is as simple as adding a new node to a cluster of server nodes in which in-memory data grids are deployed.

Although the in-memory data grid is a well established technology used by well established brands for hybrid transactional/analytical processing (HTAP) in real time, it is a continuously evolving platform that shows great promise. If your business or organization relies on real-time responsiveness, a scalable architecture, and data access from a distributed data layer, in-memory data grids are a cost-effective solution that will help increase data analytics efficiency at a reasonable cost.

Event-driven Analytics

This feature of in-memory data grids allows you to trigger a method whenever an event occurs. This is useful for immediate notifications of vital business events, such as canceled payments or transactions. The platform also provides contextualization of streaming and transactional data based on historical data at scale. Accuracy is maintained by feeding the machine learning vectors with these data sets so that models are continuously and constantly retrained. The combination of high performance speeds and scalability provide in-memory data grids the power to handle complex real-time machine learning queries.

In recent years, more models powered by analytics and machine learning have been developed and deployed—and in-memory computing has been instrumental in developing solutions that bridge the gap between transactional processing and analytics. HTAP, augmented transactions, and Translytical data platforms, which are powered by in-memory data grids, have taken the place of replicating operational data for analytics. These innovative methods offer real-time analytics in a fast feedback loop and prevent rear-view mirror analytics brought about by data duplication.

Instant Risk Analysis

In-memory data grids allow storage of business logic, analytics, and data that can be ingested from multiple sources in memory, where applications are also stored. This allows the platform to not only produce an analysis significantly faster but also make analysis predictive. Real-time advanced analytics is made possible by addressing huge amounts of streaming, hot, and historical data all at the same time. In-memory data grids also support machine learning applications to help provide instant insights, leveraged by collocated business logic within the memory fabric.

When it comes to assessing businesses for potential risk, an in-memory data grid can help prevent issues that can affect regulatory compliance, business operations, and even customer behavior patterns. Real-time insights help provide a deeper and immediate understanding of the issues and its impact and consequences, and the availability of this vital data allows companies and organizations to act accordingly and in a timely manner before minor issues become major concerns. Additionally, predictive analytics helps in real-time response and decision making whenever the need arises. In-memory data grids leverage the ability to ingest millions of events per second while also effectively analyzing the data to prevent undesired business occurrences, including cyber attacks, equipment breakdown, customer churn, and more.

Easy Scalability

Based on RAM data storage and indexing, in-memory data grids are able to achieve high speed and performance. Data processing and querying are more than 100 times faster than any other solution since everything is done in memory. Collocating data and the application itself in the same memory space reduces data movement over the network and does away with the need to access high-latency, hard-disk-drive-based or solid-state-drive-based data storage, making it an easily scalable platform.

Scalability is an important aspect, especially in big data processing. An in-memory data grid doesn’t rely on a single, centralized server to manage and provide processing capabilities to connected systems; instead, it is based on parallelized distributed processing for maximum scalability. This method of processing allows for computer networking that provides shared computer-processing capabilities to multiple computers across different locations. Scaling in-memory data grids can be done by simply adding a new node, since the platform is deployed on a cluster of server nodes that share available memory and CPU.

As gathered data becomes larger and more complex, the future of database management seems to be reliant on the cloud, effectively making on-premise storage the new legacy. Organizations will gradually migrate applications and systems into the cloud, and those who still require on-premise systems will adopt a hybrid configuration that supports both cloud and on-premise. In short, the future of any business will depend on agility, flexibility, and adaptability. The key is ensuring that your applications and services can run on both cloud and on-premise systems—and even using a combination of both.

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