IDG Contributor Network: How in-memory computing drives digital transformation with HTAP


In-memory computing (IMC) is becoming a fixture in the data center, and Gartner predicts that by 2020, . One of the benefits of IMC is that it will enable enterprises to start implementing hybrid transactional/analytical processing (HTAP) strategies, which have the potential to revolutionize data processing by providing real-time insights into big data sets while simultaneously driving down costs.

Here’s why IMC and HTAP are tech’s new power couple.

Extreme processing performance with IMC

IMC platforms maintain data in RAM to process and analyze data without continually reading and writing data from a disk-based database. Architected to distribute processing across a cluster of commodity servers, these platforms can easily be inserted between existing application and data layers with no rip-and-replace.

They can also be easily and cost effectively scaled by adding new servers to the cluster and can automatically take advantage of the added RAM and CPU processing power. The benefits of IMC platforms include performance gains of 1,000X or more, the ability to scale to petabytes of in-memory data, and high availability thanks to distributed computing.

, Rafique Awan from Wellington Management described the importance of HTAP to the performance of the company’s new investment book of rRecord (IBOR). Wellington has more than $1 trillion in assets under management.

But HTAP isn’t easy. In the earliest days of computing, the same data set was used for both transaction processing and analytics. However, as data sets grew in size, queries started slowing down the system and could lock up the database.

because it supports real-time analytics and situational awareness on the live transaction data instead of relying on after-the-fact analyses on stale data. IMC also has the potential to significantly reduce the cost and complexity of the data layer architecture, allowing real-time, web-scale applications at a much lower cost than separate OLTP/OLAP approaches.

To be fair, not all data analytics can be performed using HTAP. Highly complex, long running queries must still be performed in OLAP systems. However, HTAP can provide businesses with a completely new ability to react immediately to a rapidly changing environment.

For example, for industrial IoT use cases, HTAP can enable the real-time capture of incoming sensor data and simultaneously make real-time decisions. This can result in more timely maintenance, higher asset utilization, and reduced costs, driving significant financial benefits. Financial services firms can process transactions in their IBORs and analyze their risk and capital requirements at any point in time to meet the real-time regulatory reporting requirements that impact their business.

Online retailers can transact purchases while simultaneously analyzing inventory levels and other factors, such as weather conditions or website traffic, to update pricing for a given item in real time. And health care providers can continually analyze the transactional data being collected from hundreds or thousands of in-hospital and home-based patients to provide immediate individual recommendations while also looking at trend data for possible disease outbreaks.

Finally, by eliminating the need for separate databases, an IMC-powered HTAP system can simplify life for development teams and eliminate duplicative costs by reducing the number of technologies in use and downsizing to just one infrastructure.

The fast data opportunity

The rapid growth of data and the drive to make real-time decisions based on the data generated as a result of digital transformation initiatives is driving companies to consider IMC-based HTAP solutions. Any business faced with the opportunities and challenges of fast data from initiatives such as web-scale applications and the internet of things, which require ever-greater levels of performance and scale, should definitely take the time to learn more about in-memory computing-driven hybrid transactional/analytical processing.

This article is published as part of the IDG Contributor Network.