Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Real-world Applications of Streaming Analytics- StreamAnalytix Webinar

On- demand webcast ‘Real-world Applications of Streaming Analytics’ available at http://bit.ly/1AeGdxM

Ähnliche Bücher

Kostenlos mit einer 30-tägigen Testversion von Scribd

Alle anzeigen

Ähnliche Hörbücher

Kostenlos mit einer 30-tägigen Testversion von Scribd

Alle anzeigen
  • Als Erste(r) kommentieren

Real-world Applications of Streaming Analytics- StreamAnalytix Webinar

  1. 1. WEBINAR Real World Applications of Streaming Analytics Recorded version available at http://bit.ly/1AeGdxM 1 © 2014 Impetus Technologies
  2. 2. Recent Webcast Recap– Archived on the Website Recorded version available at http://bit.ly/1AeGdxM 2 © 2014 Impetus Technologies Real-time Streaming Analytics for Enterprises based on Apache Storm Real-time Streaming Analytics: Business Value, Use Cases, and Architectural Considerations
  3. 3. Why rapid growth and demand for real-time analytics Q&A Recorded version available at http://bit.ly/1AeGdxM Agenda 3 © 2014 Impetus Technologies StreamAnalytix – Product Overview Real World Case Studies Business Problem, Solution Architecture and Outcomes
  4. 4. • Big Data Solutions & Services company • Unique in depth, expertise – started implementing in 2008 • Proven with customer success • IP and Products • We deliver - Business Impact from Big Data Solutions • Technology expertise • Data Science • Business Analytics • Serving Fortune 1000 companies since 1996 • Large-scale and mission critical software platforms • HQ: Los Gatos, CA; 1500 people • Offshore operations in 3 cities in India Recorded version available at http://bit.ly/1AeGdxM Brief Intro 4 © 2014 Impetus Technologies
  5. 5. Drivers for Real-time Streaming Analytics Fleet Operations & Logistics Security Mobile Devices and Apps Energy Industry IT Operations Recorded version available at http://bit.ly/1AeGdxM 5 © 2014 Impetus Technologies
  6. 6. Drivers for Real-time Streaming Analytics You and I : The ‘CUSTOMER’ Recorded version available at http://bit.ly/1AeGdxM 6 © 2014 Impetus Technologies
  7. 7. Drivers for Real-time Streaming Analytics Recorded version available at http://bit.ly/1AeGdxM 7 © 2014 Impetus Technologies
  8. 8. Drivers for Real-time Streaming Analytics Context Sensitive service Recorded version available at http://bit.ly/1AeGdxM Multi-channel engagement in real-time 8 © 2014 Impetus Technologies Happy customers, Loyalty, Revenue, Profits, Growth
  9. 9. Drivers for Real-time Streaming Analytics Recorded version available at http://bit.ly/1AeGdxM Business Operations 9 © 2014 Impetus Technologies Business Analytics Real-time Streaming Analytics
  10. 10. Real-time Business Analytics – The “Batch Gap” The batch workflow is too slow Views are out of date Not yet absorbed. Data absorbed into Batch Views Recorded version available at http://bit.ly/1AeGdxM 10 © 2014 Impetus Technologies Now Time Just a few hours of data.
  11. 11. Blended View – Historical and NOW Recorded version available at http://bit.ly/1AeGdxM t 11 © 2014 Impetus Technologies now Hadoop works great back here Storm works here Blleennddeedd Vviieew
  12. 12. Big Data and Fast Data Combined Batch Layer Pre-computed information Batch re-compute Speed Layer Pre-computed information Real time increment Recorded version available at http://bit.ly/1AeGdxM All data All data 12 © 2014 Impetus Technologies Serving Layer Batch view Batch view Merge Real time view Real time view Incoming Data Query
  13. 13. Poll Where are you in the process of implementing real-time streaming analytics? Recorded version available at http://bit.ly/1AeGdxM 13 © 2014 Impetus Technologies
  14. 14. Enterprise Class Real time Streaming Analytics Platform A Product developed and offered by Recorded version available at http://bit.ly/1AeGdxM 14 © 2014 Impetus Technologies
  15. 15. StreamAnalytix is a software platform that enables enterprises to analyze and respond to events in real-time at Big Data scale. It is designed to rapidly build and deploy streaming analytics applications for any industry vertical, any data format, and any use-case Recorded version available at http://bit.ly/1AeGdxM At a Glance 15 © 2014 Impetus Technologies
  16. 16. StreamAnalytix Block Diagram Recorded version available at http://bit.ly/1AeGdxM 16 © 2014 Impetus Technologies
  17. 17. Case Studies - Real World Applications Recorded version available at http://bit.ly/1AeGdxM 17 © 2014 Impetus Technologies
  18. 18. Case Study 1 – Intelligence Solutions Company Basic Schematic Architecture Recorded version available at http://bit.ly/1AeGdxM Problem: 18 © 2014 Impetus Technologies Numerous " non-voice " communications
  19. 19. Case Study 1 – Intelligence Solutions Company • Classify streaming text in real-time based on topic • Sentiment Analysis on the stream in real-time • 250 million messages a day • Variety: weblogs, chats, emails, tweets etc. Recorded version available at http://bit.ly/1AeGdxM • Accuracy Classification - 99.99% Sentiment analysis - 80% 19 © 2014 Impetus Technologies 20 Predefined Categories "Arts_culture_entertainment " "law_crime_justice" "disaster_accident" "economy_finance" "education" "environment_weather" "health" "lifestyle" "politics" "religion" "science" "society" "sports" "conflict_war" "literature" "computing" "labor" "travel" "governance_government" "human_interest" Problem statement
  20. 20. Case Study 1 – Intelligence Solutions Company Problem statement • English and Arabic content • Other languages = “other” (no metadata) Had CSS and JavaScript files To be categorized as “scripts” • Ingest, Store, Index, Query Metadata and Raw binary data Petabytes • Query SLA – On any 4 hour window "cold data" 4 to 5 seconds ETSI compliant encryption Recorded version available at http://bit.ly/1AeGdxM • Data very raw 20 © 2014 Impetus Technologies
  21. 21. Case Study 1 – Intelligence Solutions Company Classification Recorded version available at http://bit.ly/1AeGdxM Content Extraction and Preprocessing 22 © 2014 Impetus Technologies Sentiment Analysis Tokenization of words based on delimiters (space) Elimination of all “Stop Words”, non-contributory words Removal of non-ASCII and Non UTF-8 Models built offline and scoring online
  22. 22. Case Study 1 – Intelligence Solutions Company Real-time Classification • 20 categories; Multiple labels if applicable • Semantic similarity approach based on matrix • Language independent (with caveats) • Low Latency achieved by two step process Recorded version available at http://bit.ly/1AeGdxM decomposition -Pre-processing -Numerical computation 23 © 2014 Impetus Technologies
  23. 23. Case Study 1 – Intelligence Solutions Company Sentiment Analysis • Dictionary or Lexicon approach; Unsupervised model • Prepared offline with matrix decomposition • Polarities assigned to adjectives (+ - 0 ) -Surrounding words could negate, amplify etc. -Clusters of words treated separately -Feature extraction possible for distinct sentiment Recorded version available at http://bit.ly/1AeGdxM 24 © 2014 Impetus Technologies
  24. 24. Case Study 1 – Intelligence Solutions Company Learnings - Analytics • Language independent technique worked well • 50-60 documents per topic was sufficient -Is not 100% tokenizable – no spaces -Did not hamper accuracy significantly -Needed language expert to test model (for any foreign language) Recorded version available at http://bit.ly/1AeGdxM • Arabic content 25 © 2014 Impetus Technologies
  25. 25. Case Study 1 – Intelligence Solutions Company Learnings - Architecture • Task lends well to parallelization and scale out • StreamAnalytix is a good fit – linear scale out • Event size/ throughput – trade off • Unique sharding and indexing for query optimization • Many more types of use-cases possible Recorded version available at http://bit.ly/1AeGdxM • Flexible topology 26 © 2014 Impetus Technologies
  26. 26. Case Study 2 – Hosted Contact Center Solution Recorded version available at http://bit.ly/1AeGdxM 27 © 2014 Impetus Technologies
  27. 27. Case Study 2 – Hosted Contact Center Solution IVR Agent Queue Recorded version available at http://bit.ly/1AeGdxM 28 © 2014 Impetus Technologies
  28. 28. Case Study 2 – Hosted Contact Center Solution • Proactive – Business teams want to understand dominant call paths • Lower “Queue” time Recorded version available at http://bit.ly/1AeGdxM Problem Statement • Reactive – Customer service complaints on “What happened to my call ?” Diagnostics - Easier - Faster 29 © 2014 Impetus Technologies • Proactive - Abandoned call analysis Hang up on IVR/hold
  29. 29. Case Study 2 – Hosted Contact Center Solution Technical Requirements Log Aggregation • Stream raw log events from multiple remote servers • Filter incoming log events – before storage • Index/search of log events Real-Time Dashboard and Alerts Auto Correlate Logs in Real-time • Correlate log events arriving at different time intervals based on System ID, Channel ID, Call ID • Visualize the complete call path for a particular id • Ability to show counters on the existing Log Monitoring dashboard. For eg. #of inbound calls per tenant • SLA based alarms – ability to generate alarms based on SLA threshold values over a moving time window per Recorded version available at http://bit.ly/1AeGdxM tenant. 30 © 2014 Impetus Technologies IVR Dominant Path
  30. 30. Case Study 2 – Hosted Contact Center Solution Recorded version available at http://bit.ly/1AeGdxM 31 © 2014 Impetus Technologies
  31. 31. Case Study 2 – Hosted Contact Center Solution Recorded version available at http://bit.ly/1AeGdxM 32 © 2014 Impetus Technologies
  32. 32. Case Study 2 – Hosted Contact Center Solution Recorded version available at http://bit.ly/1AeGdxM 33 © 2014 Impetus Technologies
  33. 33. Case Study 2 – Hosted Contact Center Solution Outcome, Next steps • Next steps Recorded version available at http://bit.ly/1AeGdxM 34 © 2014 Impetus Technologies – Sentiment analysis in real-time (chat) – Audio to text: Sentiment analytics on transcript – Rich real-time dash-boarding and live counters • Successfully solved key problems – Call log aggregation, indexing and search – Real-time call path picture – Dominant path analytics
  34. 34. Case Study 3 – Digital Content Provider • Scholarly journals, educational, research content • Institutional Subscribers – 1000s of users each • Business wants real-time visibility and analytics of customer behavior patterns Recorded version available at http://bit.ly/1AeGdxM 35 © 2014 Impetus Technologies
  35. 35. Case Study 3 – Digital Content Provider • 10s of millions of events per day – Clickstream data – complex XML events – Complex XML events parsed, filtered in real-time • Recommendation engine Recorded version available at http://bit.ly/1AeGdxM Problem Statement • Real-time ETL • Clickstream-Analytics: – Double click detection – BOT detection – Upsell/ cross-sell 36 © 2014 Impetus Technologies
  36. 36. Case Study 3 – Digital Content Provider Data Flow and Real-time Pipeline Design Recorded version available at http://bit.ly/1AeGdxM 37 © 2014 Impetus Technologies
  37. 37. Case Study 4 – Web Application SLA Monitoring • Healthcare insurance exchange software platform • Server response time to front end application is a key • Complaints from key customers (potential revenue impact) • Triggered need for aggressive monitoring and alerting Recorded version available at http://bit.ly/1AeGdxM metric system 38 © 2014 Impetus Technologies
  38. 38. Case Study 4 – Web Application SLA Monitoring Recorded version available at http://bit.ly/1AeGdxM 39 © 2014 Impetus Technologies • Alert if response breaches 4 second threshold • Real-time counters/ dashboard for a variety of metrics • Monthly report Problem Statement
  39. 39. Case Study 4 – Web Application SLA Monitoring Syslog Server Kafka Server StreamAnalytix Agent Features • The agent can publish to multiple destinations • The agent can send encrypted data (optional) StreamAnalytix Real-Time Pipeline Recorded version available at http://bit.ly/1AeGdxM Remote Node StreamAnalyti x Agent Syslog Kafka via TCP 40 © 2014 Impetus Technologies Index Store Down Stream System SLA Events Report generation SLA Alerts Real-Time Counters Data Flow and Real-time Pipeline Design
  40. 40. Successful outcomes with all early customers A few others in process • Tier1 Healthcare Insurance Carrier – variety of use-cases • Major Credit Card Brand and Bank – variety of use-cases • End-point Security Application – On-prem and SaaS • Mobile Field Devices – Real-time monitoring, predictive analytics Recorded version available at http://bit.ly/1AeGdxM 41 © 2014 Impetus Technologies
  41. 41. Q&A ? Email us at inquiry@streamanalytix.com www.StreamAnalytix.com Request: On-premise and Cloud based trial and/or Proof of concept Recorded version available at http://bit.ly/1AeGdxM 42 © 2014 Impetus Technologies
  42. 42. StreamAnalytix Product Highlights An “App Server” for real-time apps – on-premise and cloud Focus on your business logic - leave infra to us Handle all the 3V’s of Big Data on one platform Recorded version available at http://bit.ly/1AeGdxM 43 © 2014 Impetus Technologies Significant time to market acceleration Seamless integration with Hadoop and NoSQL
  43. 43. Data Parsing - Variety Recorded version available at http://bit.ly/1AeGdxM Key Features High Speed Data Ingestion Elastic Scaling – Volume, Velocity 44 © 2014 Impetus Technologies Pluggable Persistence Real-time Index and Search Dynamic Message Routing Rule Based Alert Pluggable Workflow Management Fault Tolerance and Data Integrity Optimized for High Performance

    Als Erste(r) kommentieren

    Loggen Sie sich ein, um Kommentare anzuzeigen.

  • diggjam

    Jan. 28, 2015
  • JordiCosta3

    Nov. 12, 2015
  • burakcorekcioglu

    Jun. 19, 2018

On- demand webcast ‘Real-world Applications of Streaming Analytics’ available at http://bit.ly/1AeGdxM

Aufrufe

Aufrufe insgesamt

1.342

Auf Slideshare

0

Aus Einbettungen

0

Anzahl der Einbettungen

2

Befehle

Downloads

49

Geteilt

0

Kommentare

0

Likes

3

×