Powering the Internet of Things with In-Memory Computing

The total amount of data created by devices, driven by the Internet of Things (IoT), will reach 600 ZB per year by 2020, up from 145 ZB per year in 2015, according to the “Cisco Global Cloud Index: Forecast and Methodology, 2015–2020.” To deliver the anticipated benefits, these new IoT applications must ingest and analyze massive amounts of data being generated from multiple sources in real time. In-memory computing is the answer.

In-memory computing empowers new hybrid transactional/analytical processing (HTAP) solutions - also known as hybrid operational/analytical processing (HOAP) or translytical processing - which enable Internet of Things applications to ingest, process, analyze and react to massive amount of IoT data in real-time. By supporting real-time analytics on the operational dataset, immediate action based on the most recent information is possible.

The GridGain® in-memory computing platform can provide real-time performance and massive scalability to existing applications when deployed as an in-memory data grid. For new or rearchitected applications, GridGain can be deployed as an in-memory database for optimal performance. GridGain includes native integrations with a number of popular open source solutions for IoT applications.

Building an IoT Platform with Open Source Solutions

Vendors or open source projects often create native integrations to ensure their applications seamlessly work together and are easy to deploy. For example, an IoT platform can be created from the following stack of open source solutions which include native integrations:

  • GridGain – An in-memory computing platform built on Apache® Ignite for processing data in real time at massive scale which includes a persistent store feature and integrated machine learning and deep learning libraries
  • Apache® Kafka® – A streaming platform for publishing and subscribing to streams of records, durably storing streams of records, and processing streams of records as they occur
  • Apache® Spark – A unified analytics engine for large-scale data processing
  • Kubernetes® – Provides a system for automating the deployment, scaling and management of containerized applications across a server cluster

Vendors of solutions built on these open source projects as well as the open source projects are work to ensure simple, native integrations between them. These are mature solutions and offer a highly cost-effective path to developing and deploying large-scale IoT applications.

If your organization needs an IoT database acceleration solution for an existing Internet of Things application or an IoT database to create a new one, please contact GridGain Systems to learn more about how our in-memory computing solutions can help your business power your IoT platform.