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Blind Spots in BIG DATA: Detecting Coverage and Monitoring Challenges

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Ido Shilon
Ido Shilon

1) The document discusses challenges with big data analysis including ensuring complete data coverage from all relevant sources like devices, platforms and browser configurations. 2) It also discusses the challenge of effective monitoring to detect issues that could corrupt alerting data, giving examples of how the company Forter addresses these challenges through techniques like API monitoring and machine learning anomaly detection. 3) The key takeaways are to understand all parts of the data pipeline, log errors from both client and server, and flag any incidents affecting input data for data scientists.

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Blind Spots in BIG DATA Erez Koren Forter
About myself - Erez Koren In the computer business since 2nd grade Love building products and hacking stuff Currently in my 3rd startup adventure Working at Forter from before day one 2017
About Forter We catch Fraudsters & protect E-commerce merchants Founded 3.5 years ago ~80 employees worldwide Backed by 2017
We detect fraud, give a real-time decision (approve/decline) every time and guarantee it (chargeback protection). Covering the whole customer lifecycle We collect data from browsers (JS) and mobile apps (through SDK ) We also receive order/account data S2S into our API and reply with our decision in real-time Our stack: Forter - What & How We Do It 2017 Compliance: And more...
The big data infrastructure... 2017
But you have a feeling that something is wrong Are you sure the data contains everything you need? How do you ensure the quality of your data? 2017
The COVERAGE challenge In some cases the data you are analyzing is only partial
Today’s internet is a jungle. There are thousands of devices, platforms, browsers and configurations. Are you sure you are collecting data from all / most of the relevant sources? 2017 The COVERAGE Challenge
Demo time This is how we do it 2017
11 MULTIPLE DEVICES & PLATFORMS
12 MULTIPLE VERSIONS, INCLUDING DEV. VERSIONS
13 SENDING EVENTS FROM 25 DIFFERENT CONFIGS
14 SELENIUM TESTS COVERS FULL CHECKOUT EXPERIENCE
15 IN REAL WORLD SOME OF THE TESTS ALWAYS FAIL
16 EXAMPLE FOR UNEXPECTED DATA IN REAL WORLD Chorme Safari Mobile Safari Firefox IE Android Browser Edge Chrome WebView PhantomJS undefined Opera WebKit
17 Detect exceptions that occurs on client side Browsers (JS), Mobile SDKs and any other client integrations CLIENT SIDE CODE MONITORING
18 JS SCRIPT TIMEOUTS Merchant checked the website with a browser that is not supporting javascript Detect gaps between script request from server and script events received
Compare the data segments of the general population versus the data segment spread in your data Test it as if you were a real user Even if everything is working now, in the future it will not Takeaways 2017
The MONITORING Challenge
2017 The MONITORING Challenge Is “measuring everything” good enough? How often are you checking the graphs? Do you have enough alerts or too many? There are always technical issues that can corrupt the alerting data
Demo 2 time This is how we do it 2017
23 API AVAILABILITY CHECK External monitoring (watch the watcher), including round-trip Pingdom and StatusCake
24 DEPENDENCIES MONITORING (RSS) e.g. AWS, GitHub Reported to our #production room in slack
25 API RESPONSES ANOMALY DETECTION Detect decline increase from X% to Y% in a given time window
26 1. Making sure we don’t slow the site down, or impact checkout funnel via automated Selenium tests (with & without our script, multiple browsers) 2. Incremental deployment support for JS SCRIPT MONITORING
27 ML FEATURES ANOMALY DETECTION Monitoring system’s health by measuring our Machine Learning features distribution over time
28 VULNERABILITIES MONITORING (RSS) OS, databases, libraries etc.
29 ALERTS DAILY SUMMARY Alerts summary of in the last 24h + ability to drill the graphs
2017 Takeaways Make sure every alert can be drilled down into a graph and relate to the raw metric Know how to investigate - leave breadcrumbs to raw data (even when the data is aggregated) Differentiate between critical alerts and other alerts (that can be fixed the next morning) Measure low values as well as the high ones - alerts for low values (e.g. CPU) is something that most systems are missing
2017 Takeaways Understand the pipes and filters make sure there are no hidden blockages in the data pipelines Log errors both from client side and server side when possible and analyze together Make sure incidents that affect input data are shared with your data scientists by using “dirty” or “partial” flag
Thank you ! 2017

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Blind Spots in BIG DATA: Detecting Coverage and Monitoring Challenges

  • 1. Blind Spots in BIG DATA Erez Koren Forter
  • 2. About myself - Erez Koren In the computer business since 2nd grade Love building products and hacking stuff Currently in my 3rd startup adventure Working at Forter from before day one 2017
  • 3. About Forter We catch Fraudsters & protect E-commerce merchants Founded 3.5 years ago ~80 employees worldwide Backed by 2017
  • 4.
  • 5. We detect fraud, give a real-time decision (approve/decline) every time and guarantee it (chargeback protection). Covering the whole customer lifecycle We collect data from browsers (JS) and mobile apps (through SDK ) We also receive order/account data S2S into our API and reply with our decision in real-time Our stack: Forter - What & How We Do It 2017 Compliance: And more...
  • 6. The big data infrastructure... 2017
  • 7. But you have a feeling that something is wrong Are you sure the data contains everything you need? How do you ensure the quality of your data? 2017
  • 8. The COVERAGE challenge In some cases the data you are analyzing is only partial
  • 9. Today’s internet is a jungle. There are thousands of devices, platforms, browsers and configurations. Are you sure you are collecting data from all / most of the relevant sources? 2017 The COVERAGE Challenge
  • 10. Demo time This is how we do it 2017
  • 13. 13 SENDING EVENTS FROM 25 DIFFERENT CONFIGS
  • 14. 14 SELENIUM TESTS COVERS FULL CHECKOUT EXPERIENCE
  • 15. 15 IN REAL WORLD SOME OF THE TESTS ALWAYS FAIL
  • 16. 16 EXAMPLE FOR UNEXPECTED DATA IN REAL WORLD Chorme Safari Mobile Safari Firefox IE Android Browser Edge Chrome WebView PhantomJS undefined Opera WebKit
  • 17. 17 Detect exceptions that occurs on client side Browsers (JS), Mobile SDKs and any other client integrations CLIENT SIDE CODE MONITORING
  • 18. 18 JS SCRIPT TIMEOUTS Merchant checked the website with a browser that is not supporting javascript Detect gaps between script request from server and script events received
  • 19. Compare the data segments of the general population versus the data segment spread in your data Test it as if you were a real user Even if everything is working now, in the future it will not Takeaways 2017
  • 21. 2017 The MONITORING Challenge Is “measuring everything” good enough? How often are you checking the graphs? Do you have enough alerts or too many? There are always technical issues that can corrupt the alerting data
  • 22. Demo 2 time This is how we do it 2017
  • 23. 23 API AVAILABILITY CHECK External monitoring (watch the watcher), including round-trip Pingdom and StatusCake
  • 24. 24 DEPENDENCIES MONITORING (RSS) e.g. AWS, GitHub Reported to our #production room in slack
  • 25. 25 API RESPONSES ANOMALY DETECTION Detect decline increase from X% to Y% in a given time window
  • 26. 26 1. Making sure we don’t slow the site down, or impact checkout funnel via automated Selenium tests (with & without our script, multiple browsers) 2. Incremental deployment support for JS SCRIPT MONITORING
  • 27. 27 ML FEATURES ANOMALY DETECTION Monitoring system’s health by measuring our Machine Learning features distribution over time
  • 28. 28 VULNERABILITIES MONITORING (RSS) OS, databases, libraries etc.
  • 29. 29 ALERTS DAILY SUMMARY Alerts summary of in the last 24h + ability to drill the graphs
  • 30. 2017 Takeaways Make sure every alert can be drilled down into a graph and relate to the raw metric Know how to investigate - leave breadcrumbs to raw data (even when the data is aggregated) Differentiate between critical alerts and other alerts (that can be fixed the next morning) Measure low values as well as the high ones - alerts for low values (e.g. CPU) is something that most systems are missing
  • 31. 2017 Takeaways Understand the pipes and filters make sure there are no hidden blockages in the data pipelines Log errors both from client side and server side when possible and analyze together Make sure incidents that affect input data are shared with your data scientists by using “dirty” or “partial” flag