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Big Data Use Cases in the cloud. Peter Sirota, GM Elastic MapReduce @ petersirota. What is Big Data?. Computer generated data Application server logs (web sites, games) Sensor data (weather, water, smart grids) Images/videos (traffic, security cameras). Human generated data - PowerPoint PPT Presentation
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Big Data Use Cases in the cloudPeter Sirota, GM Elastic MapReduce @petersirota
What is Big Data?
Computer generated data Application server logs (web sites, games) Sensor data (weather, water, smart grids) Images/videos (traffic, security cameras)
Human generated data Twitter “Firehose” (50 mil tweets/day 1,400% growth
per year) Blogs/Reviews/Emails/Pictures
Social graphs Facebook, linked-in, contacts
Big Data is full of valuable, unanswered questions!
Why is Big Data Hard (and Getting Harder)?
Data Volume Unconstrained growth Current systems don’t scale
Why is Big Data Hard (and Getting Harder)?
Why is Big Data Hard (and Getting Harder)?
Data Structure Need to consolidate data from multiple data sources
in multiple formats across multiple businesses
Why is Big Data Hard (and Getting Harder)?
Changing Data Requirements Faster response time of fresher data Sampling is not good enough and history is important Increasing complexity of analytics Users demand inexpensive experimentation
We need tools built specifically for Big Data!
Innovation #1:
Apache HadoopThe MapReduce computational paradigm Open source, scalable, fault‐tolerant, distributed system
Hadoop lowers the cost of developing a distributed system for data processing
Innovation #2:
Amazon Elastic Compute Cloud (EC2) “provides resizable compute capacity in the cloud.”
Amazon EC2 lowers the cost of operating a distributed system for data processing
Amazon Elastic MapReduce =
Amazon EC2 + Hadoop
Elastic MapReduce applications
Targeted advertising / Clickstream analysisSecurity: anti-virus, fraud detection, image recognitionPattern matching / Recommendations Data warehousing / BIBio-informatics (Genome analysis) Financial simulation (Monte Carlo simulation)File processing (resize jpegs, video encoding)Web indexing
Clickstream Analysis –
Big Box Retailer came to Razorfish 3.5 billion records 71 million unique cookies 1.7 million targeted ads required per day
Problem: Improve Return on Ad Spend (ROAS)
Clickstream Analysis –
Targeted Ad
User recently purchased a sports movie and is searching for video games (1.7 Million per day)
Clickstream Analysis –
Lots of experimentation but final design: 100 node on-demand Elastic MapReduce cluster running Hadoop
Clickstream Analysis –
Processing time dropped from 2+ days to 8 hours (with lots more data)
Clickstream Analysis –
Increased Return On Ad Spend by 500%
World’s largest handmade marketplace 8.9 million items 1 billion page view per month $320MM 2010 GMS
• Easy to ‘backfill’ and run experiments just boot up a cluster with 100, 500, or 1000 nodes
Production DB snapshots
Production DB snapshots
Web event logs
Web event logs ETL – Step
1ETL – Step
1ETL – Step
2ETL – Step
2
JobJob
JobJob
JobJob
Recommendations
etsy.com/gifts
Gift Ideas for Facebook Friends
•
• Yelp generates close to 400GB of logs per day
Yelp
• Yelp does not have a physical MapReduce cluster
• Running 250 production clusters per week
• All of those run on Elastic MapReduce
MapReduce at Yelp
Features driven by MapReduce
Features driven by MapReduce
• Analyze ad stats (reporting, billing, algorithm inputs)
• Analyze A/B test results
• Detect duplicate business listings
• Email bounce processing
• Identify bots based on traffic patterns
More MapReduce uses
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Big Data @ foursquare
9/23/2011 Amazon EMR Strata Justin Moore - @injust
How do we use EMR?
• Map-Reduce– Run algorithms on our entire dataset– Streaming jobs, complex analyses
• Hive– Business intelligence– Exploratory analyses– Infographics!
9/23/2011 Amazon EMR Strata Justin Moore - @injust
How big is our data?
• Global reach (North Pole, Space)• Native app for almost every smartphone, SMS,
web, mobile-web• 10M+ users, 15M+ venues, ~1B check-ins• Terabytes of log data
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Our Stack
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Computing venue-to-venue similarity
• Spin up 40 node cluster• Submit Ruby streaming job
– Invert User x Venue matrix– Grab Co-occurrences– Compute similarity
• Spin down cluster• Load data to app server
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Who is checking in?
9/23/2011 Amazon EMR Strata Justin Moore - @injust
What are people doing?
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Where are our users?
9/23/2011 Amazon EMR Strata Justin Moore - @injust
When do people go to a place?
Thursday Friday Saturday Sunday
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Why are people checking in?
• Explore their city, discover new places• Find friends, meet up• Save with local deals• Get insider tips on venues• Personal analytics, diary• Follow brands and celebrities• Earn points, badges, gamification of life• The list grows…
9/23/2011 Amazon EMR Strata Justin Moore - @injust
How can we leverage these insights?
9/23/2011 Amazon EMR Strata Justin Moore - @injust
Join us!
foursquare is hiringwww.foursquare.com/jobs
Justin Moore@injust
http://aws.amazon.com/elasticmapreduce/