Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and more
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Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and more
- 1. Big Data & Analytics Use Cases in Mobile, E-commerce, Media and more Russell Nash AWS Solutions Architect
- 3. Product? Do we have a product? Can we ship? How to develop faster? Better? Cheaper? Market? Can we scale? What do people do & why? How do we optimize?
- 5. • 10 million guests • 550,000 properties listed • Massive growth on AWS • $776.4M from top investors • $10B valuation – more than Hyatt
- 6. “At Airbnb, we look into all possible ways to improve our product and user experience. Often times this involves lots of analytics behind the http://nerds.airbnb.com/redshift-performance-cost/ scene.” Henry Cai 蔡明航 Software Engineer, Growth at Airbnb
- 7. The best startups use AWS for analytics…
- 8. Agenda • Big Data Overview • MapReduce / Hadoop • Case Study: Yelp • Data Warehousing • Case Study: Foursquare • NoSQL • Case Study: AdRoll • Streaming • Case Study: Supercell
- 9. STREAMING Hadoop MPP NoSQL
- 10. KINESIS EMR Redshift DynamoDB
- 11. Structure High Low Large Size Small Traditional Database Hadoop NoSQL MPP DW
- 12. Hadoop MPP NoSQL Structure Latency Interfaces
- 13. Background • 2004 – Map Reduce • 2006 – Hadoop
- 14. Input File Functions Output Hadoop cluster 1. Very Flexible 2. Very Scalable 3. Often Transient
- 15. Big Data Verticals and Use cases Media/Advertising Targeted Advertising Image and Video Processing Oil & Gas Seismic Analysis Retail Recommendation s Transactions Analysis Life Sciences Genome Analysis Financial Services Monte Carlo Simulations Risk Analysis Security Anti-virus Fraud Detection Image Recognition Social Network/Gaming User Demographics Usage analysis In-game metrics
- 16. Deployment Options On-premise Cloud Managed on Cloud
- 17. Amazon Elastic MapReduce Manageability Scalability Cost
- 18. Case Study
- 19. 400 GB of logs per day ~12 Terabytes per month
- 21. 1) Load log file data for six months of user search history into Amazon S3 Amazon S3 Search ID Search Text Final Selection 12423451 westen Westin 14235235 wisten Westin 54332232 westenn Westin 12423451 14235235 54332232 12423451 14235235 54332232 12423451 14235235 54332232 12423451 14235235 54332232 12423451
- 22. Amazon S3 Amazon EMR Log Files 2) Spin up a 200 node cluster Hadoop Cluster
- 23. 3) 200 nodes simultaneously analyze this data looking for common misspellings … this takes a few hours Hadoop Cluster Amazon S3 Amazon EMR
- 24. Amazon S3 Amazon EMR 4) New common misspellings and suggestions loaded back into S3 Hadoop Cluster Log Files
- 25. Amazon S3 Amazon EMR 5) When the job is done, the cluster is shut down. Log Files
- 26. E-Commerce Case Study • Online Marketplace • EMR –Weblog analysis – Recommendations • Link logs with production database in EMR “Enables us to focus on developing our…analysis stack without worrying about the underlying infrastructure”
- 28. Trends SQL on Hadoop Spark
- 29. Hadoop MPP NoSQL Structure Latency Interfaces Any Mins-Hours Programming SQL-Like Tools
- 30. Background MPP = Massively Parallel Processing SQL Databases for analytical workloads Performance Scalability Ease of Use Cost
- 31. 1. SQL 2. High Performance 3. Broad Toolset
- 32. Deployment Options On-premise Cloud Managed on Cloud
- 33. Amazon Redshift Manageability Scalability Cost
- 34. Mobile Case Study • Location based social app • 40 Million users • 4.5 Billion check-ins • Multi-terabytes of log data
- 35. Who is checking in? 0.6 0.5 0.4 0.3 0.2 0.1 0 Gender Female Male Age 0 20 40 60 80
- 36. When do people go to a place? Gorilla Coffee Gray's Papaya Amorino Thursday Friday Saturday Sunday
- 37. “Using Amazon Redshift has enabled the company to perform more agile analytics while saving costs.”
- 38. Media Case Study • Placeshifting and media streaming • Collect terabytes of event logs • Viewership, devices etc • Hadoop for transformation • Redshift for analysis “Redshift allows us to turn on a dime”
- 39. Performance Evaluation on 2B Rows Traditional SQL Database Amazon Redshift Aggregate by month 02:08:35 00:35:46 00:00:12
- 40. Hadoop MPP NoSQL Structure Latency Interfaces Any Full Mins-Hours Seconds-Minutes Programming SQL-Like Tools SQL BI Tools
- 41. Background Databases for webscale transactions Performance Flexibility
- 42. ID Age State 123 20 CA 345 25 WA 678 40 FL Relational Table ID Attributes 123 Age:20, State:CA 345 Age:25, Country: Australia, Gender: F, Smoker: No 678 Age:40 Non-Relational Table
- 43. Deployment Options On-premise Cloud Managed on Cloud
- 44. Amazon DynamoDB Manageability Scalability Cost
- 45. Low Latency
- 47. Pixel “fires”
- 48. Pixel “fires” Serve ad?
- 49. Pixel “fires” Serve ad? Ad served
- 50. If you can’t reply in 100ms… It doesn’t matter anymore! Network 40 Buffer 20 Processing 40
- 51. Snacks DynamoDB
- 52. Hadoop MPP NoSQL Structure Latency Interfaces Any Full Semi Mins-Hours Seconds-Minutes Sub-second Programming SQL-Like Tools SQL Programming Tools
- 54. Use Cases • Gaming analytics • Sensor networks analytics • Ad network analytics • Log centralization • Click stream analysis • Hardware and software appliance metrics • …more…
- 55. Data Sources App.4 [Machine Learning] AWS Endpoint App.1 [Aggregate & De-Duplicate] Data Sources Data Sources Data Sources App.2 [Metric Extraction] S3 DynamoDB Redshift App.3 [Sliding Window Analysis] Data Sources Availability Zone Availability Zone Shard 1 Shard 2 Shard N Availability Zone Amazon Kinesis EMR
- 56. “Amazon Kinesis enables our business-critical analytics and dashboard applications to reliably get the data streams they need, without delays. Amazon Kinesis also offloads a lot of developer burden in building a real-time, streaming data ingestion platform, and enables Supercell to focus on delivering games that delight players worldwide.” Sami Yliharju, Supercell Services Lead
- 57. Big Data Tutorials aws.amazon.com/big-data Redshift Free Trial aws.amazon.com/redshift/free-trial
- 58. Big Data & Analytics Use Cases in Mobile, e-commerce, media and more Russell Nash AWS Solutions Architect
Editor's Notes
- Put something in users hands (doesn’t need to be code), and get feedback asap
- depending on your data structure, its size and access patterns you will need to pick the right solution * S3 is ideal for large unstructured objects such as files, pictures, binary data, etc. * Dynamo dB or other no SQL alternatives such as Cassandra is ideal for small object that you have to read or write at a high speed. It is great for data powering web or mobile applications * Amazon RDS (or other relational databases) are great for structured schema and standard SQL access but the size of data is typically limited to a single server. Of course it is possible to shard data across many RDS instances but this requires substantial development and ops work. * Hbase – ideal for analytics use case ▪ Optimized for append-heavy, light read workloads And so there is a variety of ways you can store your data on the cloud based on particular needs of your application.
- Hadoop and cloud marriage Shared nothing
- Yelp – Autocomplete, spelling suggestions S&P Capital IQ – Recommendations for investors based on behaviour Australian company – uses it to calculate which ad space it should buy.
- Let’s look at another company – Yelp.
- As you can see this company is growing rapidly and with more than 50 million of monthly visitors and 18 million or reviews the company generates about 400GB of data a day. That data needs to be processes and analyzed.
- The more searches you collect from your customers, the better recommendations you can provide. Using Hadoop on Amazon Elastic MapReduce Yelp analyses customer search results to deliver features such as hotel or restaurants recommendations. Yelp processes all customer reviews with natural language processing technologies to provide customers review highlights. From this example we can see that companies such as Yelp can use data generated by their customers on their web site to develop more innovative data products.
- By looking at typical queries, yelp can list common suggestions for a query even before you finish typing. Both of these products are possible because Yelp analyses all the web logs from their websites
- Map Reduce – Programming model for Hadoop Flume – Open source Log collection tool Mahout – Machine learning project Nutch – web search engine Cascading – Software abstraction layer Hbase – Columnar NoSQL database Cassandra – NoSQL database Sqoop – Data transfer between Hadoop and relational db’s Hive – SQL like language for Hadoop Chukwa – Log collection
- Approaching 50/50 male female
- You can see that some places are best for lunch during work hours others are dinner joints.
- Use Case – IMDB uses it for new applications. i.e, movie rating system
- Let’s look at another company – Yelp.
- Use Case – IMDB uses it for new applications. i.e, movie rating system
- Use Case – IMDB uses it for new applications. i.e, movie rating system
- [2 minutes] KINESIS is a new service that scales elastically for near realtime processing of streaming big data. The service will store large streams of data in durable, consistent storage, reliably, for near realtime processing of data by an elastically scalable fleet of data processing servers. Large streams means millions of records per second, GBs of data per second and near real-time means order of a few seconds Streaming data processing has two layers: a storage layer and a processing layer. The storage layer needs to support specialized ordering and consistency semantics that enable fast, inexpensive, and replayable reads and writes of large streams of data. Kinesis is the storage layer in Kinesis / Kinesis. The processing layer is responsible for reading data from the storage layer, processing that data, and notifying the storage layer to delete data that is no longer needed. Kinesis supports the processing layer. Customers compile the Kinesis library into their data processing application. Kinesis notifies the application (the Kinesis Worker) when there is new data to process. The Kinesis / Kinesis control plane works with Kinesis Workers to solve scalability and fault tolerance problems in the processing layer.
- Supercell is using Amazon Kinesis for real-time delivery of game insight data sent by hundreds of game engine servers.
- TALKING POINTS AWS Training and Certification is an organization dedicated to expanding and deepening knowledge of AWS, as well as driving proliferation in the usage of AWS services. Our programs are designed for customers, partners and AWS employees. Over the past several months, we have rolled out several new courses, training labs, and certifications to our customers and partners Go and visit the training team at the training booth to receive your 30% discount voucher for a certification exam.