IoT systems access millions of devices that generate large amounts of streaming data. For some equipment, a single event may prove critical to understanding and responding to the health of the machine in real time, increasing the importance of accurate, reliable data. While real-time data remains important, storing and analyzing the historical data also creates opportunities to improve processes, decision-making and outcomes.
Smart grids, which include components like sensors and smart meters, produce a wealth of telemetry data that can be used for multiple purposes, including:
Identifying anomalies such as manufacturing defects or process deviations
Predictive maintenance on devices (such as meters and transformers)
Real-time operational dashboards
Inventory optimization (in retail)
Supply chain optimization (in manufacturing)
Considering solutions for real-time analytics on IoT data
One way to achieve real-time analytics is with a combination of a time-series database (InfluxDB or TimescaleDB) or a NoSQL database (MongoDB) + a data warehouse + a BI tool:
This architecture raises a question: Why would one use an operational database, and still need a data warehouse? Architects consider such a separation so they can choose a special-purpose database — such as a NoSQL database for document data — or a time-series database (key-value) for low costs and high performance.
However, this separation also creates a data bottleneck — data can’t be analyzed without moving it from an operational data store to the warehouse. Additionally, NoSQL databases are not great at analytics, especially when it comes to complex joins and real-time analytics.
Is there a better way? What if you could get all of the above with a general-purpose, high-performance SQL database? You’d need this type of database to support time-series data, streaming data ingestion, real–time analytics and perhaps even JSON documents.
Achieving a real-time architecture with SingleStoreDB + IBM Cognos
SingleStoreDB supports fast ingestion with Pipelines (native first class feature) and concurrent analytics for IoT data to enable real-time analytics. On top of SingleStoreDB, you can use IBM® Cognos® Business Intelligence to help you make sense of all of this data. The previously described architecture then simplifies into:
Real-time analytics with SingleStoreDB & IBM Cognos
Pipelines in SingleStoreDB allow you to continuously load data at blazing fast speeds. Millions of events can be ingested each second in parallel from data sources such as Kafka, cloud object storage or HDFS. This means you can stream in structured — as well as unstructured data — for real-time analytics.
But wait, it gets better…
Once data is in SingleStoreDB, it can also be used for real-time machine learning, or to safely run application code imported into a sandbox with SingleStoreDB’s Code Engine Powered by Web Assembly (Wasm).
With SingleStoreDB, you can also leverage geospatial data — for instance to factor site locations, or to visualize material moving through your supply chains.
Armis and Infiswift are just a couple of examples of how customers use SingleStoreDB for IoT applications:
Armis uses SingleStoreDB to help enterprises discover and secure IoT devices. Armis originally started with PostgreSQL, migrated to ElasticSearch for better search performance and considered Google Big Query before finally picking SingleStoreDB for its overall capabilities across relational, analytics and text search. The Armis Platform, of which SingleStoreDB now plays a significant part, collects an array of raw data (traffic, asset, user data and more) from various sources — then processes, analyzes, enriches and aggregates it.
Infiswift selected SingleStoreDB after evaluating several other databases. Their decision was driven in part because of SingleStore’s Universal Storage technology (a hybrid table type that works for both transactional and analytical workloads).
Want to learn more about achieving real-time analytics?
Join IBM and SingleStore on Sep 21, 2022 for our webinar “Accelerating Real-Time IoT Analytics with IBM Cognos and SingleStore”. You will learn how real-time data can be leveraged to identify anomalies and create alarms by reading meter data, and classifying unusual spikes as warnings.
We will demonstrate:
Streaming data ingestion using SingleStoreDB Pipelines
Stored procedures in SingleStoreDB to classify data before it is persisted on disk or in memory
Dashboarding with Cognos
These capabilities enable companies to:
Provide better quality of service through quickly reacting to or predicting service interruptions due to equipment failures
Identify opportunities to increase production throughput as needed
Quickly and accurately invoice customers for their utilization