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Kafka and Stream Processing, Taking Analytics Real-time, Mike Spicer

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Stream processing analyzes data in motion before it is stored, allowing for real-time analytics with low latency. Kafka is well-suited for stream processing due to its speed, scalability, durability, and ability to act as a universal hub. Real-time analytics can handle many use cases like customer intelligence, IoT, and security. Examples include a telco using stream processing for real-time advertising and Thompson Reuters using it for news ingestion and analytics. Stream processing can analyze data from the edge to the center in real-time to detect and predict insights and enable immediate actions.

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Kafka and Stream Processing, Taking Analytics Real-Time Mike Spicer - Lead Architect, IBM Streams
Traditional Processing Stream Processing Data Repository Data Query request response Real-Time Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics Historical fact finding Find and analyze information stored on disk Batch paradigm, pull model Query-driven: submits queries to static data
Stream Processing
What Makes Kafka ideal for Stream Processing FAST – •  A single Streams Kafka Source/Sink can Consume/Produce 100,000’s msgs/sec SCALABLE – •  Partitioned Kafka Topics work with parallel Streams Kafka Sources •  Parallel sources in the same Consumer group can consume 1,000,000’s msgs/sec DURABLE – •  Kafka is distributed and replicated •  Messages are logged and replayable for a configured period •  Streams Kafka connectors support Guaranteed Processing •  Source supports exactly once (& at least once) semantics •  Sink supports at least once semantics A UNIVERSAL HUB – •  Hub connecting all applications and data sources •  Isolation between Producer and Consumer
Streaming Analytics Can Handle Many Use Cases IBM Streams is being applied in many use cases – •  Market and Customer Intelligence •  Revenue, Upsell / Cross Sell •  Personalized Customer Experience •  Network Analytics •  IoT, Connected Car and Telematics •  National / Cyber Security, PII & PCI Data Leakage •  Health and Improved Patient Outcomes •  Operational Optimization Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Insight Presentation Insight Presentation Insight PresentationRead the Case Study Read the Case Study Read the Case Study Read the Case Study Read the Case StudyRead the Case Study Read the Case Study Insight Presentation Read the Case StudyInsight Presentation Read the Press ReleaseRead the Abstract
Example Real-Time Analytics Use Cases North American Telco Real Time Advertising – •  Click thru rate and Revenue up 50% •  ~30M in memory profiles, 500 SPSS models •  Purchases, Web click stream, CDRs, IPTV viewing, Behavioral events •  Total events ~1.2B per day, 210K per second •  Average Latency 8ms Thompson Reuters Eikon – •  News Ingest and Analytics •  News, Market Data & Meta Data Streams to HBase •  Signal App: Real time Technical Analysis •  Bollinger Band, Simple moving average, etc. •  VolSurf: Real time volatility surfaces •  200k instruments, 100k msgs/sec Multichannel @ Website Predictive Models Scoring, Segmentation, Analysis, Association Target Advertising Platform (Campaign Management) Transactions from all customers Descriptive •  Age •  Gender •  Family situation •  Zip code Transactions from this customer •  Cardholder since YYYYMM •  Average transaction value •  Monthly transaction value •  Categories purchased •  Brands purchased Interactions •  Web registration •  Web visits •  Customer service contacts •  Channel preference Attitudes •  Satisfaction scores •  Shopper type •  Eco score Customers Capture: Search keywords Page content Cookies IP addresses Device info Actions within a window of time In-Motion Behavior Analysis Match with Global Id Map keywords to attributes and classification hierarchy Invoke behavior models/ scores Advertisers IBM Streams Inges&on   Technology   SDI  Data   (Metadata)   Elektron   (Market  data)   News   Others…   IBM Streams
Real-Time Analytics from the Center to the Edge with Quarks for edge analytics on device or gateway – •  Lightweight embedded streaming analytics runtime •  Analyze events locally on the edge •  Reduce communication costs by only sending relevant events Device Hub – •  Device management •  Message broker (including MQTT & Kafka) •  Public device hub API supports custom device hub IBM Streams for streaming analytics – •  High performance, full featured streaming analytics •  Build windows of state and correlate across devices •  Have access to data-of-record systems, e.g. medical history •  Control edge device based upon analytics •  Central job management/health summary •  Automatic application connectivity Cluster Gateway Edge DeviceEdge Device Messaging (MQTT, Kafka etc.)
Real-Time Analytics – What Are You Waiting For? The World is real-time, analyze it in real-time – • Acquire events as they happen • Analyze in real-time to detect and predict insights • Act immediately to change outcomes Forrester Research described the following key takeaway in their recent Wave report – • All Data Is Born Fast “All data originates in a flash, whether it is from Internet-of- Things (IoT) devices, web clicks, transactions, or mobile app usage. But traditional analytics is done much, much later. Why wait? AD&D pros can use streaming analytics embedded in applications to get actionable value tout de suite. So what are you waiting for? Streaming analytics solutions can capture perishable insights on real-time data to bring immediate context to all IoT, mobile, web, and enterprise apps.” The Forrester Wave™: Big Data Streaming Analytics Platforms, Q1 2016 The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
Thank You!

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Kafka and Stream Processing, Taking Analytics Real-time, Mike Spicer

  • 1. Kafka and Stream Processing, Taking Analytics Real-Time Mike Spicer - Lead Architect, IBM Streams
  • 2. Traditional Processing Stream Processing Data Repository Data Query request response Real-Time Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics Historical fact finding Find and analyze information stored on disk Batch paradigm, pull model Query-driven: submits queries to static data
  • 4. What Makes Kafka ideal for Stream Processing FAST – •  A single Streams Kafka Source/Sink can Consume/Produce 100,000’s msgs/sec SCALABLE – •  Partitioned Kafka Topics work with parallel Streams Kafka Sources •  Parallel sources in the same Consumer group can consume 1,000,000’s msgs/sec DURABLE – •  Kafka is distributed and replicated •  Messages are logged and replayable for a configured period •  Streams Kafka connectors support Guaranteed Processing •  Source supports exactly once (& at least once) semantics •  Sink supports at least once semantics A UNIVERSAL HUB – •  Hub connecting all applications and data sources •  Isolation between Producer and Consumer
  • 5. Streaming Analytics Can Handle Many Use Cases IBM Streams is being applied in many use cases – •  Market and Customer Intelligence •  Revenue, Upsell / Cross Sell •  Personalized Customer Experience •  Network Analytics •  IoT, Connected Car and Telematics •  National / Cyber Security, PII & PCI Data Leakage •  Health and Improved Patient Outcomes •  Operational Optimization Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Watch the video Insight Presentation Insight Presentation Insight PresentationRead the Case Study Read the Case Study Read the Case Study Read the Case Study Read the Case StudyRead the Case Study Read the Case Study Insight Presentation Read the Case StudyInsight Presentation Read the Press ReleaseRead the Abstract
  • 6. Example Real-Time Analytics Use Cases North American Telco Real Time Advertising – •  Click thru rate and Revenue up 50% •  ~30M in memory profiles, 500 SPSS models •  Purchases, Web click stream, CDRs, IPTV viewing, Behavioral events •  Total events ~1.2B per day, 210K per second •  Average Latency 8ms Thompson Reuters Eikon – •  News Ingest and Analytics •  News, Market Data & Meta Data Streams to HBase •  Signal App: Real time Technical Analysis •  Bollinger Band, Simple moving average, etc. •  VolSurf: Real time volatility surfaces •  200k instruments, 100k msgs/sec Multichannel @ Website Predictive Models Scoring, Segmentation, Analysis, Association Target Advertising Platform (Campaign Management) Transactions from all customers Descriptive •  Age •  Gender •  Family situation •  Zip code Transactions from this customer •  Cardholder since YYYYMM •  Average transaction value •  Monthly transaction value •  Categories purchased •  Brands purchased Interactions •  Web registration •  Web visits •  Customer service contacts •  Channel preference Attitudes •  Satisfaction scores •  Shopper type •  Eco score Customers Capture: Search keywords Page content Cookies IP addresses Device info Actions within a window of time In-Motion Behavior Analysis Match with Global Id Map keywords to attributes and classification hierarchy Invoke behavior models/ scores Advertisers IBM Streams Inges&on   Technology   SDI  Data   (Metadata)   Elektron   (Market  data)   News   Others…   IBM Streams
  • 7. Real-Time Analytics from the Center to the Edge with Quarks for edge analytics on device or gateway – •  Lightweight embedded streaming analytics runtime •  Analyze events locally on the edge •  Reduce communication costs by only sending relevant events Device Hub – •  Device management •  Message broker (including MQTT & Kafka) •  Public device hub API supports custom device hub IBM Streams for streaming analytics – •  High performance, full featured streaming analytics •  Build windows of state and correlate across devices •  Have access to data-of-record systems, e.g. medical history •  Control edge device based upon analytics •  Central job management/health summary •  Automatic application connectivity Cluster Gateway Edge DeviceEdge Device Messaging (MQTT, Kafka etc.)
  • 8. Real-Time Analytics – What Are You Waiting For? The World is real-time, analyze it in real-time – • Acquire events as they happen • Analyze in real-time to detect and predict insights • Act immediately to change outcomes Forrester Research described the following key takeaway in their recent Wave report – • All Data Is Born Fast “All data originates in a flash, whether it is from Internet-of- Things (IoT) devices, web clicks, transactions, or mobile app usage. But traditional analytics is done much, much later. Why wait? AD&D pros can use streaming analytics embedded in applications to get actionable value tout de suite. So what are you waiting for? Streaming analytics solutions can capture perishable insights on real-time data to bring immediate context to all IoT, mobile, web, and enterprise apps.” The Forrester Wave™: Big Data Streaming Analytics Platforms, Q1 2016 The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
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