From energy grids to smart meters, IoT systems access millions of devices that generate large amounts of streaming data. And for some equipment, a single event can play a critical role in understanding the health of a machine or system in real time.
Accelerating Real-Time IoT Analytics
For IoT systems that keep oil rigs running smoothly and lights on in residential areas, real-time analytics are crucial to identifying potentially harmful anomalies — creating alarms by reading meter data, and classifying unusual spikes or activity as warnings.
Even more, the right technology powering these systems can illuminate faulty grids or severed lines, reducing response times to action and ensuring systems don’t stay down for excessive periods.
Our webinar in partnership with IBM, “Accelerating Real-Time IoT Analytics With IBM Cognos & SingleStoreDB” explores how real-time data and analytics from energy grids, smart meters and other devices help enable a safe, sustainable environment that relies on the flawless functioning of its IoT systems.
Here’s a look at some of what Sugandan Barathy, Partner Solutions Manager at SingleStore and Robery Borovsky, Senior Data & AI Technical Specialist at IBM discussed.
Real-Time Analytics & IBM Data Fabric
Three key requirements for real-time IoT analytics
‘Real time’ doesn’t mean 1, 5 or even 10 minutes from now — it means immediately and with accuracy. For analytics and applications to truly function in real time, your technology needs:
- Ultra-Fast Ingest. Parallel, high-scale streaming data ingest that is capable of running millions of events per second — with immediate availability.
- Super-Low Latency. Blazing fast queries with sub-second latencies (no waiting minutes or hours for data), and immediate consistency.
- High Concurrency. Unparalleled scalability with millions of real-time queries across tens of thousands of users.
How SingleStoreDB turbocharges IBM Data Fabric
“Data fabric is truly a set of tools to democratize data access at scale,” says Barathy. This data fabric has three main properties:
- Data access, which may be physical or logical — meaning it resides in disks or disparate systems.
- Data governance, which involves data cataloging and automation
- Data Security, which oversees access, data privacy and usage policies.
“SingleStore connects to the data fabric using IBM Cloud Pak for Data,” Barathy goes on to explain. We have built a native connector that’s GA, that’s live, as of August 2022… what that helps you to do is it helps you to bring AI to your data.”
SingleStoreDB combines the best of both worlds — transactional (OLTP) and analytical (OLAP) workloads — into a single, powerful engine that enhances the connectivity and capability of IBM solutions like Cognos Analytics, Cloud Pak for Data and Watson. The result? A highly performant data engine designed to power the world’s real-time analytical applications
See more: SingleStoreDB With IBM
What is a real-time analytical application?
Historically, real time has been used for applications that are OLTP in nature — that is, they’re transactions that require high concurrency and real-time ingest, so writers do not get blocked in the database. SingleStoreDB checks this box, but also empowers dashboards with a variety of connectors that reside in our database ecosystem.
Ultimately, that enables us to combine analytic query shapes with the latency, concurrency and data freshness requirements of OLTP applications. The result? Real-time analytical applications with faster, fresher and more precise data.
Some examples of real-time analytical applications powered by IBM and SingleStoreDB include:
- IoT analytics
- Operational BI
- Ad hoc data discovery
- ML-driven analytics
- Historical reporting
Read the IBM blog: Real-Time Analytics on IoT Data
Get Real-Time Analytics With IBM & SingleStore On Demand
For more IoT analytics use cases, real-time pipeline ingest of data into SingleStoreDB and a look at dashboarding with IBM Cognos, watch the webinar on demand today.
