Globant and Big Data on AWS
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Big Data at Globant provides solutions leveraging big data technologies. Three examples summarized: 1) Metamarkets developed a console supporting drill-downs and roll-ups of billions of events in real-time, storing 10GB daily in a 15TB Hadoop cluster scanning 1 billion rows in under a second. 2) Gree's Hadoop architecture stores gaming events to analyze player profiles, revenue, and game performance. 3) Kantar Media's solution analyzes addressable TV ads, customizing ads to each user and obtaining reliable viewership measurements.
Globant and Big Data on AWS
- 1. Big Data at Globant Success Cases in AWS Sabina A. Schneider
- 2. What is Big Data?
- 3. What is Data Science?
- 4. Data Architecture Enterprise High Information Availability Strategy and Performance NoSQL Distributed Mission Solutions Critical Product Positioning in the Market Deeper insight about your Customers Analytics and Alerts on KPIs Cross-reference data with different sources
- 7. Scalable Architecture in the Cloud Mobile Devices in the cars Third Party Web App Web App Web App Integration Elastic Load Mobile Devices Balancer Auto scaling singly Web Client NoSQL DB S3 Bucket Cloudfront EMR Cluster Storm Real Time processing Hadoop Analytics Dashboard Trends Web Client Pig BigData – storage and processing
- 8. Metamarkets has developed a web-based analytics console that supports drill-downs and roll-ups of high dimensional data sets (real-time bidding), comprising billions of events, in real-time. Data store collects 10 GB of information every day, and has over 15 TB. Reports using Hadoop and Hive on AWS Infrastructure. The 40-instance cluster can scan, filter, and aggregate 1 billion rows in 950 milliseconds.
- 9. Gree is a leading casual game development company. Globant developed a Hadoop based architecture to store gaming events and generate telemetry information. These metrics are used to analyze, segment gamer profiles, estimate revenue and perform predictive analysis on game performance.
- 10. Products Positioning in the Market • Tweets recollection on specific events (eg: elections), integrated with a set of MapReduce based queries • Data stored in a 20- node Hadoop cluster • Google Visualization tools for widget based Dashboard
- 11. What? • Innovation to the Financial Market • Sentiment Analytics to what’s happening now and what can happen next in the Market • Predictions one week in advance according to comments on Tweeter Challenges • Aggresive Real Time analysis on Social Networks • Dashboarding comparing with real values from Yahoo Finances • Sentiment Analysis and Languague filtering • Analytics Predictions
- 12. Data Science Recommend ation Classification Sophisticated Mathematical algorithm Statistical Clustering Algorithm Predictions on KPIs Predictions on Metrics
- 13. Moneygram Transaction Scoring Analysis of Moneygram historical transactional data labeled as Fraudulent/Non Fraudulent • 8 years of transactional data to analyze Training using Support Vector Machines of historical data • Classification achieved by using only a subset of data using soft margins (by use of slack variables) to construct dividing hyperplane • Possible use of kernel principal components to preprocess data and reduce dimensionality of training dataset • Avoid high computation times (sparse solution) Benefits • Detect fraudulent transactions with a higher level of accuracy • Increase in customer service satisfaction (less false-positives)
- 14. Shopping cart suggestion engine Generate suggestions based on client shopping history • Cluster a large dataset representing clients' shopping history using unsupervised learning algorithms. • Use information from new/existing client to classify into the clusterized shopping history from ALL clients. • Generate suggestions based on the cluster's shopping preferences • Use of Hadoop and Mahout for clustering and posterior classification
- 15. • Metadata word clustering using Solr • Content management and information sorting/ categorization classified by location. Enhance the performance at a view level. • Indexing of jwt content coming from different sources (internal and external) developed with Solr on Lucene. Integration with myJwt.com: internal social network. • organize the content storage: service running in the Cloud that receives content, generate different assets (snapshot, thumbnails), extract metadata to be centralized in one place • myIdeas: collect ideas from different creative designers from different location and share a bonus between the bright ideas
- 16. Data Visualization Our data visualization practice allows our customers to understand the evolution of key business drivers, trends, and drill down into the root causes of deviations. Our HTML5 data visualization solution, allows us to combine the flexibility of a custom made solution with a fast time to market. It’s based in standard Widgets, allowing each user to customize the dashboard as required, and visualize it on every device.
- 17. Big Data Visualization Framework
- 18. Cloud server Browser User input Video streaming
- 20. Kantar Media manages TV Advertisement displayed on DirecTV US. We developed the addressable advertisement reporting solution, used by advertisers to plan and analyze the performance of addressable advertisement. Advertisement displayed on TV is customized to each user profile. The solution allows obtaining reliable measurements from TV, analyzes the structure of the audience that has watched each advertisement, and allows evaluating the ROI of the marketing campaign.
- 22. Touch screen based scorecard, used by the top management to analyze and compare results from different countries and products.
- 23. Thank you!