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Make Streaming IoT
Analytics Work for You
Kanishk Mahajan & Dhruv Kumar
Hortonworks May 2016
2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
IoT - Relevance
 7.2 Billion active SIM Cards
 25 Billion connected things
 500 Million tweets per day
– Average life of a tweet is 18 minutes
 2 Zettabytes per year of Global IP Traffic
– 80% is unstructured
3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
The Four Vs of Big Data http://www.ibmbigdatahub.com/infographic/four-vs-big-data
4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
5 Attributes of a Streaming Platform
 Ingest
 Process
 Analyze
 Respond
 Visualize
5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Data Flow versus Stream Analytics for IoT
 Data Flow
– Ingest and route terabytes data into a ”unified firehose”
– Actively performance manage the latency and quality of these data flows –
• Across high variability of data formats, size of data and speed of data
 Real Time Stream Analytics
– Sub second event processing with linear scalability to billions of events
– Predictive Analytics at Scale
• Real time data aggregation across edge nodes while processing 10s of millions of events and
100s of gigabytes per second
– Guaranteed no data loss and events processed in order
6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
6 Key Focus Areas for Building a Streaming Platform for IoT
1. Common Abstraction Layer
2. Latency
3. Lambda Architecture
1. “Orchestrate” over static and real time data
4. Scale-out
5. Rapid Application Development
6. Data Visualization
7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
1. Common Abstraction Layer
– Select one or more streaming engines
– Select one or more cloud providers
– Select one or more resource managers
– Select one or more event sources
– …Future Proof
8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
2. Latency
– 500 millisecond or less- Dashboards, Security Incidents, Asset Performance
– 20 milliseconds or less - Ad Networks, Preventive Maintenance
– …
9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
3. Lambda Architecture
– Integrate static and real time data
– Enrichment
– Orchestration of Batch workflows
– Predictive Classification and Scoring
10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
4. Scale Out
– Linear Scale out or scale down
– Resource Management
– Handle Transient workloads
11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
5. Rapid Application Development
– Industry vertical specific applications
– Aggregations
– Filters
– Multi Stream Correlations
– Splits
– Joins
– Normalizations
– Business Rules Editor
12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Focus Areas for Streaming Platform for IoT
6. Data Visualization
– Time Series Visualizations
– Metrics Dashboarding
– Trends
– Comparisons
– Thresholds
– Custom UI extensions
13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
IoT - Real Time Stream Analytics versus Streaming Engine
 Abstracts underlying Streaming Engine- Storm, Spark Streaming, Flink..
 OOTB Support for multiple (cloud) event sources - Kafka, AWS Kinesis, Azure Event Hub
 Built-in Operators for Complex Event Processing
 Built-in Real Time Dashboarding- Metrics and Events
 PMML Support
 Pluggable Workflow Management
 Business Rules Editor and Rapid Application Development Framework
 Cloud Deployment
 Scalable Architecture
 Handles different latency requirements
IoT: The Big Picture
15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
© Hortonworks Inc. 2011
Hortonworks IoT Platform – The Big Picture
Page 15
Data Acquisition
Edge Processing
Real Time Stream Analytics
IoT Services
Rapid Application Development
IoT
ANALYTICS
CLOUD
16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
IoT Edge
Data Acquisition & Processing
Device Specific
Protocol
Device Data
Acquisition
Edge
Processing
IoT Analytics
CloudHTTP(s)
Edge Processing
• Reliable Delivery
• Buffering and Flow Control
• Simple Event Processing
• Edge Analytics
High Availability
• Scale out and Clustering
• Multi Data Center Support
Security
• 2 way SSL
• Data Element Masking
• Flexible Routing based on Data
Sensitivity
Kafka Enablement
• Data Ingest and Drain based on
Kafka Consumer and Producer
Support
17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Device Management
Agent
• Send Diagnostics
• Receive F/W updates
• Connectivity Logs
Enrollment &
Authentication
• 3-legged OAuth
based flow
• Encryption
• Confidentiality
Registry
• Device Metadata
Catalog
User Linkage
• Device - User Linkage
• May be many - many
between Device and
User
• Temporary or
Persistent
De-
Authorization
• Deregister
• Unlink User
• Disable
Use Cases - Connected Car
19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Connected Car - Generator of Big Data:
http://www.slideshare.net/AditiTechnologies/how-internet-of-things-iot-is-reshaping-the-automotive-sector-infographic
20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Connected Car - Use Cases http://gelookahead.economist.com/infograph/car-os/
21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Reference Architeucture: Connected Car Platform and Hortonworks
For more details contact sales@hortonworks.com
Hortonworks Data Platform
Core Data
Engineers (CAD)
Systems of Record (ERP)
Customers (CRM)
22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Apache NiFi Demo: https://www.youtube.com/watch?v=r4NsWbE4_-I
(Download here: http://hortonworks.com/products/hdf/
23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Questions?
http://community.hortonworks.com

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Make Streaming IoT Analytics Work for You

  • 1. Make Streaming IoT Analytics Work for You Kanishk Mahajan & Dhruv Kumar Hortonworks May 2016
  • 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved IoT - Relevance  7.2 Billion active SIM Cards  25 Billion connected things  500 Million tweets per day – Average life of a tweet is 18 minutes  2 Zettabytes per year of Global IP Traffic – 80% is unstructured
  • 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved The Four Vs of Big Data http://www.ibmbigdatahub.com/infographic/four-vs-big-data
  • 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved 5 Attributes of a Streaming Platform  Ingest  Process  Analyze  Respond  Visualize
  • 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Data Flow versus Stream Analytics for IoT  Data Flow – Ingest and route terabytes data into a ”unified firehose” – Actively performance manage the latency and quality of these data flows – • Across high variability of data formats, size of data and speed of data  Real Time Stream Analytics – Sub second event processing with linear scalability to billions of events – Predictive Analytics at Scale • Real time data aggregation across edge nodes while processing 10s of millions of events and 100s of gigabytes per second – Guaranteed no data loss and events processed in order
  • 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved 6 Key Focus Areas for Building a Streaming Platform for IoT 1. Common Abstraction Layer 2. Latency 3. Lambda Architecture 1. “Orchestrate” over static and real time data 4. Scale-out 5. Rapid Application Development 6. Data Visualization
  • 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 1. Common Abstraction Layer – Select one or more streaming engines – Select one or more cloud providers – Select one or more resource managers – Select one or more event sources – …Future Proof
  • 8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 2. Latency – 500 millisecond or less- Dashboards, Security Incidents, Asset Performance – 20 milliseconds or less - Ad Networks, Preventive Maintenance – …
  • 9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 3. Lambda Architecture – Integrate static and real time data – Enrichment – Orchestration of Batch workflows – Predictive Classification and Scoring
  • 10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 4. Scale Out – Linear Scale out or scale down – Resource Management – Handle Transient workloads
  • 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 5. Rapid Application Development – Industry vertical specific applications – Aggregations – Filters – Multi Stream Correlations – Splits – Joins – Normalizations – Business Rules Editor
  • 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Focus Areas for Streaming Platform for IoT 6. Data Visualization – Time Series Visualizations – Metrics Dashboarding – Trends – Comparisons – Thresholds – Custom UI extensions
  • 13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved IoT - Real Time Stream Analytics versus Streaming Engine  Abstracts underlying Streaming Engine- Storm, Spark Streaming, Flink..  OOTB Support for multiple (cloud) event sources - Kafka, AWS Kinesis, Azure Event Hub  Built-in Operators for Complex Event Processing  Built-in Real Time Dashboarding- Metrics and Events  PMML Support  Pluggable Workflow Management  Business Rules Editor and Rapid Application Development Framework  Cloud Deployment  Scalable Architecture  Handles different latency requirements
  • 14. IoT: The Big Picture
  • 15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved © Hortonworks Inc. 2011 Hortonworks IoT Platform – The Big Picture Page 15 Data Acquisition Edge Processing Real Time Stream Analytics IoT Services Rapid Application Development IoT ANALYTICS CLOUD
  • 16. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved IoT Edge Data Acquisition & Processing Device Specific Protocol Device Data Acquisition Edge Processing IoT Analytics CloudHTTP(s) Edge Processing • Reliable Delivery • Buffering and Flow Control • Simple Event Processing • Edge Analytics High Availability • Scale out and Clustering • Multi Data Center Support Security • 2 way SSL • Data Element Masking • Flexible Routing based on Data Sensitivity Kafka Enablement • Data Ingest and Drain based on Kafka Consumer and Producer Support
  • 17. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Device Management Agent • Send Diagnostics • Receive F/W updates • Connectivity Logs Enrollment & Authentication • 3-legged OAuth based flow • Encryption • Confidentiality Registry • Device Metadata Catalog User Linkage • Device - User Linkage • May be many - many between Device and User • Temporary or Persistent De- Authorization • Deregister • Unlink User • Disable
  • 18. Use Cases - Connected Car
  • 19. 19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Connected Car - Generator of Big Data: http://www.slideshare.net/AditiTechnologies/how-internet-of-things-iot-is-reshaping-the-automotive-sector-infographic
  • 20. 20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Connected Car - Use Cases http://gelookahead.economist.com/infograph/car-os/
  • 21. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Reference Architeucture: Connected Car Platform and Hortonworks For more details contact sales@hortonworks.com Hortonworks Data Platform Core Data Engineers (CAD) Systems of Record (ERP) Customers (CRM)
  • 22. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache NiFi Demo: https://www.youtube.com/watch?v=r4NsWbE4_-I (Download here: http://hortonworks.com/products/hdf/
  • 23. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Questions? http://community.hortonworks.com