Big Data Use Cases in the cloud Peter Sirota, GM Elastic

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/ s/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)?

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 Hadoop  The 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 analysis Security: anti-virus, fraud detection, image recognition Pattern matching / Recommendations Data warehousing / BI Bio-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 Web event logs ETL – Step 1 ETL – Step 2 Job

Recommendations The Taste Test

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

Analyze ad stats (reporting, billing, algorithm inputs) Analyze A/B test results Detect duplicate business listings bounce processing Identify bots based on traffic patterns More MapReduce uses

9/23/2011 Amazon EMR Strata Justin Moore Big foursquare

9/23/2011 Amazon EMR Strata Justin Moore 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 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 Our Stack

9/23/2011 Amazon EMR Strata Justin Moore 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 Who is checking in?

9/23/2011 Amazon EMR Strata Justin Moore What are people doing?

9/23/2011 Amazon EMR Strata Justin Moore Where are our users?

9/23/2011 Amazon EMR Strata Justin Moore When do people go to a place? Thursday Friday SaturdaySunday

9/23/2011 Amazon EMR Strata Justin Moore 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 How can we leverage these insights?

9/23/2011 Amazon EMR Strata Justin Moore Join us! foursquare is hiring Justin