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IoT Data Analytics in Real Time

Posted by Marbenz Antonio on September 21, 2022

Why IoT systems need real-time analytics

IoT systems have access to a huge number of devices that produce a lot of streaming data. For some pieces of equipment, a single occurrence could be important to understanding and reacting to the machine’s health in real time, emphasizing the significance of accurate, trustworthy data. While previous data analysis and storage can offer chances to enhance procedures, judgment, and results, real-time data is still important.

Smart grids, which contain sensors and smart meters, generate a vast amount of telemetry data that can be utilized for a variety of applications, such as:

• identifying anomalies, such as production flaws or process irregularities
• gadget predictive maintenance (such as meters and transformers)
• Operational dashboards in real-time
• Inventory management (in retail)
• supply chain improvement (in manufacturing)

Considering approaches to real-time IoT data analytics

Real-time analytics can be achieved, for example, by combining a time-series database (such as InfluxDB or TimescaleDB) or a NoSQL database (such as MongoDB) with a data warehouse and a business intelligence tool:

Why would one use an operational database and still require a data warehouse, according to this architecture? To select a special-purpose database, such as a NoSQL database for document data, or a time-series database (key-value) for low costs and good performance, architects take such a separation into account.

This separation, however, also produces a data bottleneck because the analysis of data requires transporting it from operational data storage to the warehouse. Additionally, NoSQL databases struggle with analytics, particularly real-time analytics and complicated joins.

Exists a better approach? What if a high-performance, general-purpose SQL database enabled you to achieve all of the above? To support time-series data, streaming data intake, real-time analytics, and potentially even JSON documents, you would require this kind of database.

Real-time architecture with IBM Cognos and SingleStoreDB

To provide real-time analytics, SingleStoreDB supports concurrent analytics for IoT data and quick ingestion with Pipelines (native first-class feature). You can utilize IBM® Cognos® Business Intelligence in addition to SingleStoreDB to assist you in making sense of all of this data. The earlier-described architecture then becomes:

Real-time analytics with SingleStoreDB & IBM Cognos

SingleStoreDB’s pipelines let you rapidly and constantly load data. It is possible to simultaneously ingest millions of events per second from data sources including HDFS, cloud object storage, and Kafka. This means that both organized and unstructured data can be streamed in for real-time analyses.

But hold on, things improve…

1. Using SingleStoreDB’s Code Engine Powered by Web Assembly (Wasm), data may also be used for real-time machine learning or to safely run application code imported into a sandbox.
2. You can use geospatial data with SingleStoreDB to, for example, factor in site locations or visualize the movement of materials across your supply chains.

Customers use SingleStoreDB for IoT applications in a variety of ways, with Armis and Infiswift being just two examples:

• To assist businesses in finding and securing IoT devices, Armis employs SingleStoreDB. Armis began with PostgreSQL, switched to ElasticSearch for greater search performance, and gave Google Big Query some thought before settling on SingleStoreDB for its comprehensive relational, analytics, and text search features. The Armis Platform, of which SingleStoreDB is now an important factor, gathers a variety of raw data from many sources (including traffic, asset, user, and more) before processing, analyzing, enriching, and aggregating it.
• Infiswift evaluated some databases before settling on SingleStoreDB. The technology behind SingleStore’s Universal Storage had a role in their decision (a hybrid table type that works for both transactional and analytical workloads).

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