Skip to main content Skip to footer

Big Data Glossary

What is In-memory Computing?

Definition of In-memory Computing

Crosser Page Break Icon

What is In-memory Computing?

In-memory computing is a technology that allows data to be processed and analyzed directly in a computer's memory, rather than being stored on disk. It is a way to speed up the processing of large amounts of data by keeping the data in the computer's random access memory (RAM) instead of reading it from disk.

In-memory computing uses specialized software, such as in-memory databases, in-memory data grids, and in-memory data processing platforms, that are optimized to take advantage of the high-speed access and low-latency of memory. These software can process and analyze data in real-time, providing faster response times and enabling more complex and sophisticated data processing and analytics.

In-memory computing is particularly useful for real-time analytics, where data needs to be analyzed and acted upon quickly. It can be used for various use cases such as financial fraud detection, real-time marketing, and real-time risk management. In-memory computing can also be used to accelerate big data and data warehousing workloads, allowing organizations to analyze and make decisions faster.

It is important to note that in-memory computing requires a significant amount of memory, and it can be costly, it also requires careful handling of the data and appropriate data governance to ensure that the data is used ethically and legally

Introducing Crosser

The All-in-One Platform for Modern Integration

Crosser is a hybrid-first platform that in one Low-code platform has all the capabilities that you traditionally would need several systems for.

In one easy-to-use platform:

Platform Overview

Crosser Solution for Data Mining

Explore the key features of the platform here →

Want to learn more about how Crosser could help you and your team to:

  • Build and deploy data pipelines faster
  • Save cloud cost
  • Reduce use of critical resources
  • Simplify your data stack