Stop Thinking, Just Do!

Stream Computing - Examples

General Examples

• Monitoring streams of low-level sensor inputs and emitting high-level interpretations of the raw data
• Responding to streams emanating from sensor inputs by emitting actuator commands
• Monitoring streams of data from Intrusion Detection sensor inputs and emitting alerts
• Monitoring streams of video data and interpreting scenes, scene changes, etc.

InfoSphere Streams Examples

• Traffic control system which takes in GPS information from public vehicles and caculates deviations from normal to recommend alternative routes
• Real-time correlation of information from multiple neonatal ICU monitors to detect potential life threatening conditions up to 24 hours earlier than an experienced ICU nurse

The Need for Stream Computing

A need for real-time analytics on BIG data

• Volume
  Millions/Billions of events per second
  Terabytes/Petabytes per day

• Variety
  All kinds of data
  All kinds of analytics

• Velocity
  Insights in microseconds

• Agility
  Dynamically responsive
  Rapid application development

###Traditional Computing

• Historical fact finding
• Find and analyse information stored on disk
• Batch paradigm, pull model
• Query-driven: submit queries to static data

###Stream Computing

• Current fact finding
• Analyze data in motion - before it is stored
• Low latency paradigm, push model
• Data-driven: bring data to the analytics

What is Stream Processing?

• Relational databases and warehouses find information stored on disk
• Streams analyses data before you store it
• Databases find the needle in the haystack
• Streams finds the needle as it’s blowing by

References

[1] Brian Williams, IBM InfoSphere Streams Developer Education Section 1, October 2013.