This webinar follows the process of evaluating different big data platforms based on varying use cases and business requirements, and explains how big data professionals can choose the right technology to transform their business. During this session, Ooyala CTO, Sean Knapp will discuss why Ooyala selected DataStax as the big data platform powering their business, and how they provide real-time video analytics that help media companies create deeply personalized viewing experiences for more than 1/4 of all Internet video viewers each month.

1. Data Points += 2 billion
... daily
July 24th, 2013

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ABOUT ME
Sean Knapp
(@seanknapp)
•Co-Founder, EVP & Chief Product Officer
(formerly CTO)
•Senior Software Engineer @ Google
•Built & launched iGoogle
•Led Google’s Frontend Web Search and Ads
UX teams, who drove a $1B increase in
revenue for Google in 18 months
•B.S. & M.S. in Computer Science from
Stanford University

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Suite of products and services providing
white-label management, hosting, and
distribution of video online
Hundreds of customers including ESPN,
Bloomberg, Disney, Miramax, Univision,
Dell, Pac-12 Networks, and more
100M+ unique users streaming more
than 1B videos monthly, generating
more than 2B analytics events daily
280 employees located in Silicon Valley,
NYC, London, Tokyo, Sydney, Singapore,
Seoul & Guadalajara
OOYALA OVERVIEW

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EVOLVING INSIGHTS
• Insights circa ’07
• How many videos did I show this week?
• What were my monthly uniques?
• Insights circa ’09
• How many ad impressions did I receive from
users in each Designated Market Area
(DMA)?
• Insights circa ’11
• How many users do I have right now?
• Insights circa ’13
• How does the revenue from iPad users age
25-34 compare to those on XBox?
Weekly
Instant
Summary
Detailed
Complex

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BIG DATA @ OOYALA
• 1st Gen (circa ’07)
• Process: Hadoop MapReduce
• Language: Ruby
• Store: MySQL
• 2nd Gen (circa ’09)
• Process: Hadoop MapReduce
• Language: Ruby
• Store: Cassandra 0.5+
• 3rd Gen (circa ’11)
• Process: MapReduce, Storm
• Language: Ruby, Scala
• Store: DataStax Enterprise (300TB disk, 1TB RAM)
• 4th Gen (circa ’13)
• Process: MapReduce, Storm, Spark, Hive
• Language: Scala
• Store: DataStax Enterprise (1.5PB disk, 14TB RAM)
Batch
Realtime
Summary
Granular
Queryable

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OUR GOALS
• Evolve our Analytics product from a time-delayed,
static reporting system to a realtime, granular, and
dynamic query engine
• Launch our Content Recommendation engine, an
entirely new product offering
• Scale to billions of user events on a daily basis
• Support an ever expanding set of global customers
• Deliver a 5-9’s platform

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OUR CHALLENGES
• Very small ops team supporting global infrastructure
• Not enough capacity for performance tuning
• Routinely fell behind the latest releases
• Didn’t know which releases were stable enough
• Unforeseen product requirements beyond the next 12
months
• Existing solution would have cost nearly $1M to scale
to just 100TB

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SELECTION PROCESS
• Key Criteria
• Scalability: PB+, 100k+ operations per second
• Cost / price-performance
• Availability: 5-9’s
• Flexibility: schemaless
• Alternative Technologies
• Other RDMS systems
• HBase
• Voldemort

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WHY CASSANDRA
• First learned about C* in Nov 2008
• First deployed C* in Sep 2009
• Compelling Features
• Scalability: PB+, high ops/sec, billions of rows and
columns
• Performance: designed specifically for heavy
workloads similar to Ooyala’s
• Cost: could run on commodity hardware
• Availability: multi-datacenter with no single point of
failure
• Community: strong, unified direction

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RESULTS
• Business
• Launched the next-gen of our Analytics in ’09 that
solidified Ooyala as the leader in our industry
• Launched our Content Recommendation engine in
’12 that again separated us from the industry
• Technical
• 1,000x the scale of just 5 years ago
• Much higher ROI: 1PB+ for < $500k in hardware
• No more 3am pager alerts

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Q&A

12. THANK YOU