11 Things You Should Know About IoT Processing Requirements

by Chris Preimesberger

Ingest high velocity and large volumes of data produced by sensors and devices in real time. Naturally, a capacious storage system—either on-site or in the cloud—is mandatory.

Combine the incoming data stream with sensor management information, status, user preferences and configured policies, as the data arrives, to augment and enrich events for downstream processes.

Make per-event, data-driven decisions based on immediate context, not just historical averages. Operationalizing big data means using your big data analytics to drive per-event, real-time decisions, alerts and notifications to optimize your business.

Enable analytics on real-time data for operations monitoring, BI and C-level dashboards. Don’t leave your real-time stream opaque for operational monitoring. Open the window on what’s happening now.

Integrate the real-time ETL (extract, transform and load) of the normalized data stream to your data warehouse for online analytical processing (OLAP) and exploration. Ingest, enrich, and analyze and drive decisions on data in real time, then store the processed stream to your data pool for historical analytics and data science.

Make the results of historical analytics available to real-time decisions by enabling high-speed serving of OLAP reports in combination with that real-time data.

High availability, durability and predictable performance are fundamental IoT data processing requirements.

Operationalizing real-time data means making multi-factored decisions using current and historical data. This requires ACID (atomicity, consistency, isolation and durability) processing to filter, enrich, and transform and respond to incoming events.

IoT relies on cloud services for scale, reliability and distribution. Your fast data solution must be cloud deployable.

Real time means keeping up. Your system must maintain high performance under a high concurrent load and be able to process millions of events per second.

Solving problems at scale requires using the right tool for the right job, and your tools must work together. Ecosystem integration is a must to function as part of a total fast data, big data strategy.