Big Data : Bits of History, Words of Advice
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Big Data: Bits of History, Words of Advice by Venu Vasudevan discusses the history and challenges of big data. The document provides examples from Iridium satellite communications and an industrial IoT project to illustrate issues with fast, unpredictable data streams and the challenges of integrating IT and OT systems. It advocates for hybrid human-machine approaches and emphasizes starting simply before over-engineering architectures to fit extreme needs. The key lessons are to focus on the core problem, allow for data variability, and consider lightweight data augmentation when starting with limited real-world datasets.
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Big Data : Bits of History, Words of Advice
- 1. Big Data : Bits of History, Words of Advice Venu Vasudevan GLSEC Big Data Meetup
- 2. Big Data : Bits of History, Words of Advice
- 4. Big Data : Behavioral Big Data - The ‘V’ view of Big Data challenges - Number of V’s up for debate
- 5. Big Data : Architectural untidy data firehose clean analytics fast & good slower & much better Lambda architecture Lake architecture Stream architecture
- 6. Technical
- 7. Technical
- 8. This Talk Behavioral View Technology Solution Stack ‘Middleware’ (benefit of hindsight) some more some governance culture (gap) data economics ownership foodfights dataeconomics
- 10. Iridium • mobile routers (10K mph), fixed people • no repeated patterns • satellites N-S movement • earth E-W movement • regular topology, irregular exceptions • solar flares • military satellite presence
- 11. Fast Data Problem • cellular frequency allocation (graph coloring problem) • frequent fast recalculations (fast routers + semi-fast earth) • transmit-no transmit (solar flares, military satellite presence) • moving ‘seam’ seam irregularities
- 12. Fast Data Problem • cellular frequency allocation (graph coloring problem) • frequent fast recalculations (fast routers + semi-fast earth) • transmit-no transmit (solar flares, military satellite presence) • moving ‘seam’ • + ‘France’ seam irregularities broadcast = +$$$ broadcast = -$$$ (lawsuit)
- 13. Fast Data Problem • quest for (OO)DB technology to address ‘France’ as make-or- break use case • query expressive power • complex constraint satisfaction • query handling throughput • 3-4 month benchmarking effort seam broadcast = +$$$ broadcast = -$$$ (lawsuit)
- 14. Fast Data Problem • quest for (OO)DB technology to address ‘France’ • query expressive power • query handling throughput • 3-4 month benchmarking effort • France solved ‘out-of- band’ (legally) seam broadcast = +$$$ broadcast = -$$$ (lawsuit)don’t overfit your architecture to an extreme requirement unless it’s from an extreme (paying) user
- 15. Big Data Problem • systems management • manage 66 ‘nodes’ • nodes moving at 10K mph • ‘seam’ moving of 20K mph • sounds harder than trivial, but not too hard
- 16. ‘Pre’ Lambda Solution • Dumb edge | smart core approach • 15K events/sec/satellite • 1M events/sec • Fast & Approximate - FMEA: ’compiled’ lookup table for failure modes • Slow & Precise - Model-based reasoning on satellite models untidy satellite firehose (1M events/sec) actionable insights ‘Pre’ Lambda architecture Model-Based Reasoning FMEA
- 17. ‘Pre’ Lambda Solution • Dumb edge | smart core approach • 15K events/sec/satellite • Fast & Approximate - FMEA: ’compiled’ lookup table for failure modes • Slow & Precise - Model-based reasoning on satellite models • Simple, straightforward & wrong. untidy satellite firehose (1M events/sec) actionable insights ‘Pre’ Lambda architecture Model-Based Reasoning real-time expert system FMEA Yet, an architecture that is ‘rinsed and repeated’ over the years
- 18. why does dumb edge smart cloud endure? • edges are expensive ($2B) • when edges go wrong (break/blow up /collide) , they make headlines $ $$$$$
- 19. why dumb edge smart cloud • edges are expensive ($2B) • when edges go wrong (break/blow up /collide) and make headlines • nobody messes with an ‘edge’ once it works • clouds don’t make for good news headlines $ T-0 $$$$$ T-30 yrs
- 20. why dumb edge smart cloud • edges are expensive ($2B) • when edges go wrong (break/blow up /collide) and make news headlines • nobody messes with an ‘edge’ once it works • thus, implementing an end- to-end architecture causes culture clashes over my dead body iterate & refine
- 21. an almost repeat (Industrial IoT) • edges are messy & domain specific • creating them means dealing with culture clashes • but .. an ounce of edge is worth a pound of cloud $$$$$ T-30 yrs $ T-0
- 22. Things to consider • Problem statement. What’s your ‘France’? • colorful sub-problem. strategy overfit. • Architecture. small fixes to IT/OT gap can go a long way to a simpler problem • Technology Choices. best practices & the risk of ‘rewardless risk’ • right - make average programmers productive with new tech • frequent - turn great programmers into average
- 23. Big Data to Deep Metadata streaming video(TV) ~ 1 petabyte/day second minute hour day/week epochal detect & replace ads Create Playlists by Player, Play, Sentiment Identify minor characters with rabid fan following rejuvenate old content derivenewcontent ‘chapterize’ by Player, Play, Sentiment
- 24. Platform Triage Challenge new Product, new market • one core technology, many markets • platform triaging challenge. what drives the platform? • highest (but uncertain) $ potential? • ‘extreme’ requirement? • sparsest competition? • use case outlier is your biggest customer deep metadata technology SaaS data platform Advertising Search Video concept maps
- 25. ad replacement use case • speed • few days (on-demand content) • few seconds (real-time rebroadcast with new ads) • precision • low - best effort, for low cost international content for niche audiences • high - frame level for expensive content. e.g. Sports/$10M/episode programming • errors • 90% accuracy - ok for long tail content • ‘five nines’ for premium content precision accuracy speed ad replacement opportunity space largest customer
- 26. occam’s razor works (again) • build to simplicity • loose coupling between data engg & equipment engg • modularize complexity • ‘differentiate your product’ changes • ‘necessary evil’ changes data-only approach +1st party integration (dynamically configure ad splicers) 3rd party knobs (dynamically refresh CDN)
- 27. Architecture
- 28. but, what if .. • Data is untidy • Interpretation is subjective/cultural • Automation is aspirational but quixotic
- 29. human-powered analytics • some analytics tasks are too ‘slippery’ for machines • data hard to characterize • uneven video quality of ‘old’ archives • untidy • insights are subjective
- 30. human-powered analytics • some analytics tasks are too ‘slippery’ for machines • need for human augmentation • humans generate ‘training’ sets to bootstrap m/c learning • humans completely take over some tasks
- 31. machines vs humans • crowdsourcing & human- powered computing • has been the ‘next big thing’ for a while • checkered history: • uneven output • fraud • uneven throughput Machines Humans fast slow brittle malleable objective subjective clear nuanced
- 32. machines vs humans • much of that has changed • Amazon Mech Turk • 500K active users • the ‘human machine’ can return substantial jobs in under 30 mins • quantifiable as a machine for many media tasks - latency, quality, error rate, thruput
- 34. Things to consider • Beware ‘France’ in other forms: • customer with loudest voice & ‘holy grail’ hairball • Dealing with data quality & variability • crowdsourcing has come a long way as credible ‘engine’ • If big data the answer, what is the question? (have strong opinion held weakly) • decision rationalization • process automation • human ‘power tool’ (e.g. compelling visualization) vs imperfect automation
- 35. startup data jiu-jitsu • How to create a data- driven strategy before the data shows up? • rationalize future SaaS revenue models • justify product decisions in a data- driven manner need data for product need product for data
- 36. startup data jiu-jitsu • How to create a data- driven strategy before the data shows up? • how ‘intelligent’ can lighting control be with 50-100K users? • how do people use dimmers (continuous or quantized) — UX implications
- 37. data set dilemma • standard sources (e.g. Kaggle & UCI) insufficient • few ‘physical world’ datasets • expensive to collect • may be specialized (vendor-specific) • dataset proxies for IoT actuation may not work • energy utilization != switch usage
- 38. big data, small start • physical world data likely to be smaller (1-10 homes, few months) • setup costs limit size of public datasets • e.g. UMass Smart* light switch dataset
- 39. big data, small start • consider data ‘augmentation’ • standard practice in AI (deep learning) - horizontally flipping, random crops … • under-used in data space • may need some thought on perturbation models for your domain real synthesized https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
- 40. In short .. • big data success - equal parts tech & non-tech • solving right problem, not just problem right • revisit problem, and what success means