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Hadoop in the Wild

This document outlines various practical applications of Hadoop in distributed computing, illustrating its value through diverse use cases. Examples include log processing, image identification, and recommendation engines, demonstrating how organizations like banks, mobile carriers, and online services utilize Hadoop to analyze data and improve customer insights. It also discusses architectural frameworks such as the Lambda architecture, highlighting the challenges and solutions encountered in data analysis and storage. Gain insights into effective strategies for leveraging Hadoop in modern data environments.

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Hadoop in the Wild

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  1. Hadoop in the Wild CMSC 491/691 Hadoop-Based Distributed Computing Spring 2014 Adam Shook

  2. Agenda • Check out some use cases • Discuss some architectures

  3. Use Cases

  4. Common Use Cases • Log Processing • Image Identification • Extract Transform Load • Recommendation Engines • Time-Series Storage and Processing • Building Search Indexes • Long-Term Archive • Audit Logging

  5. Non-Use Cases • Data processing handled by one large server • ACID Transactions

  6. A Bank • Problem • Need to analyze customer activity across multiple products to predict credit risk • Acquired a number of banks • Solution • Setup a single Hadoop cluster with data from multiple EDWs • Bank added new sources of customer service data to get a clear picture of a customer’s financial situation

  7. A Mobile Carrier • Problem • Why are our customers terminating their service contracts? • Solution • Combined transactional and event data with social network data • Combined coverage maps with account data

  8. An Online Dating Service • Problem • Surveys, demographic, and web activity to build a picture • Customers wanted better recommendations • Algorithms improved and number of users grew • Solution • Moved data and analysis to Hadoop • Able to size system to meet needs of customers

  9. Ad Targeting • Problem • Advertising is a special kind of recommendation • Need to select best ad for a particular visitor, but each advertiser is paying to have its ad seen • Solution • Collect stream of user activity with continuous analysis • Build sophisticated models of user behavior

  10. POS Transaction Analysis • Problem • Retailers able to collect much more data in stores and online • EDW do not generally support sophisticated analysis to provide better forecasting • Solution • Loaded 20 years of sales transactions and used Hive to do same analysis as before • Now able to use new algorithms with new data sets

  11. Sensor Data • Problem • Volume of sensor data from every generator across multiple grids is enormous • Clear picture depends on real-time and forensic analysis • Solution • Capture and store all streaming sensor data • Built continuous analysis system to watch performance of generators

  12. Threat Analysis • Problem • How do we detect threats and fraudulent activity in an online world? • Solution • Use of HBase to store virus signatures • Use of MapReduce to compare spam or malware • Lambda Architecture

  13. Trade Surveillance • Problem • Difficult to monitor trades for compliance, and impossible to catch rogue traders • Solution • Store trade data and trading party data • Continuously monitor activity and build connections • Provides cheap storage for law-required auditing

  14. Search • Problem • Indexing stuff is pretty easy, until go and index the Internet • User preferences make it harder • Solution • MapReduce was designed for indexing • Online retailers depend on search for users finding and buying products

  15. Data Sandbox • Problem • ??? • Solution • Simple storage mechanism with diverse tools for data analysis and exploration

  16. Architectures

  17. Building your Data Lake

  18. Building your Data Lake

  19. Building your Data Lake

  20. Building your Data Lake

  21. 1 2 3 4

  22. Lambda Architecture Hadoop All Data Precompute Views BATCH LAYER Batch recompute SERVING LAYER QFD N QFD 1 QFD 2 New Data Stream Query Batch views (HDFS/SQL) (Apache HBase) Real-time views QFD N QFD 1 QFD 2 Storm Process Stream Increment Views SPEED LAYER Real-Time Increment

  23. Facebook • EDW (Oracle) was unable to scale and perform • Investigated small Hadoop system • Engineers loved it • Began developing Hive

  24. Facebook • Time-series summaries • Ad hoc jobs over historical data • Long-term archival store for logs • Look up log events by specific attributes

  25. Facebook Architecture

  26. Facebook Messaging • Needed a short set of temporal data • A growing set of data that is rarely accessed • HBase fit their needs more than other open-source technologies

  27. Twitter Architecture

  28. LinkedIn Architecture

  29. LinkedIn Applications

  30. LinkedIn Applications

  31. LinkedIn Applications

  32. LinkedIn Future • MapReduce is not suited for large graph processing • Batch-oriented nature is not suited for “breaking news”

  33. References • Hadoop: The Definitive Guide, Chapter 16.2 • http://www.slideshare.net/s_shah/the-big-data-ecosystem-at-linkedin-23512853 • http://www.slideshare.net/Hadoop_Summit/hadoop-hardware-twitter-size-does-matter • http://www.forbes.com/sites/edddumbill/2014/01/14/the-data-lake-dream/ • http://www.slideshare.net/brocknoland/common-and-unique-use-cases-for-apache-hadoop • http://blog.cloudera.com/wp-content/uploads/2011/03/ten_common_hadoopable_problems_final.pdf

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