Internet of Things (IoT) Databases and Analytics Powered by In-Memory Computing
Gartner expects the Internet of Things (IoT) to have over 20 billion connected things by 2020. This many connected devices transmitting information will require an enormous amount of processing to derive value from this data. To cope with this rapid growth of the Internet of Things, successful IoT database platforms will need a data architecture which leverages in-memory computing. These architectures will address the significant challenges in terms of speed, scalability, variable workloads, and other issues created by IoT applications.
The Architecture of HTAP for IoT
The architecture of the Hybrid Transactional/Analytical Processing (HTAP) component of IoT analytics and transactions is similar to the Lambda architecture defined by Nathan Marz for Big Data applications. It takes advantage of both stream- and batch-processing methods. The Lambda architecture includes the following layers:
- A high-speed layer — a real-time processing and transactional engine (typically something like a caching system and a compute grid, such as Redis and Spark)
- A batch/storage layer— data storage with an analytical or historical processing engine, such as Hadoop with Hive
The Lambda architecture of the HTAP component of the Internet of Things deals with event-stream processing, fast analytics, and storing data for advanced and long-term historical analysis, when necessary.
For companies just getting started with IoT, combining multiple technologies can require a significant investment in terms of skill set. While it is possible to find people who know each of a variety of technologies, it is not easy to find people who know all of them. There is a lot of complexity involved.
The GridGain in-memory computing platform provides a way to simplify the HTAP architecture for IoT databases and analytics. It addresses the needs of both transactional and analytical processing and also provides persistency and event processing — all in a high-speed, linearly scalable platform. And GridGain is just one core technology with one skill set to learn.
Internet of Things Use Cases for GridGain Technology
According to 451 Research, 65% of companies are using IoT. 69% of organizations gather data from end points and 94% of those companies use it for business purposes. The highest usage is among Utilities (92%) and Manufacturing (77%).
The IoT data comes from:
- Datacenter IT Equipment (51%)
- Cameras and Surveillance Equipment (34%)
- Smartphones and End Users (30%)
- Buildings and Other Structures (21%)
- Environmental Sensors (15%>
- Factory Equipment (14%)
- Automobiles/Fleet Equipment (11%)
- Retail Operations (8%)
- Medical Devices (7%)
Some GridGain Internet of Things Clients
- ThingWorx – A part of PTC, ThingWorx solutions bring together the physical and digital worlds to reinvent the way companies create, operate, and service products. Global manufacturers and ThingWorx partners and developers can capitalize on the promise of the IoT today.
- Silver Springs Networks - With over 24 million delivered devices, cities, utilities, and companies on 5 continents use the company’s high-performance IoT network and data platform.
Other GridGain Resources About IOT
- White Paper - High Performance Data Architectures for the Internet of Things
- Webinar Recording - Apache® Ignite™: Real-Time Processing of IoT-Generated Streaming Data
- Webinar Recording - Boost Performance of Financial Services IoT Projects with In-Memory Computing
- Webinar Recording – High Performance Data Architectures for the Internet of Things (IoT)