11 Things You Should Know About IoT Processing Requirements

1 - 11 Things You Should Know About IoT Processing Requirements
2 - Plan for Speed
3 - Enrich Fast Data With Existing Information
4 - Make Intelligent Real-Time Decisions
5 - Make Real-Time Data Transparent for Operations
6 - It's All About a Pipeline
7 - Close the Loop With Analytics
8 - Always On, Always Ready
9 - Support Rich Interactions With Data
10 - Live in the Cloud
11 - Never Slow Down
12 - Use the Right Tool for the Right Job
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11 Things You Should Know About IoT Processing Requirements

by Chris Preimesberger

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Plan for Speed

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.

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Enrich Fast Data With Existing Information

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.

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Make Intelligent Real-Time Decisions

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.

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Make Real-Time Data Transparent for Operations

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.

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It's All About a Pipeline

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.

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Close the Loop With Analytics

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.

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Always On, Always Ready

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

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Support Rich Interactions With Data

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.

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Live in the Cloud

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

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Never Slow Down

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.

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Use the Right Tool for the Right Job

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.

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