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Big data – a brief overview

  1. 1. Big Data – A Brief Overview Petabytes, Hadoop, Analytics, Collaborative business intelligence, Data scientists, In-Memory Databases, NoSQL platforms
  2. 2. Big Data • What is it? • Where does it come from? • How do we process it? • What do we do with it? • Who are the players? • What are the opportunities?
  3. 3. What Is Big Data? Like the term Cloud, it is a bit Nebulous
  4. 4. Attributes of Big Data • Volume • Velocity - streaming • Variety
  5. 5. Where Does It Come From? It Depends
  6. 6. Key Drivers Spread of cloud computing, mobile computing and social media technologies, financial transactions
  7. 7. Sources of Big Data • Chatter from social networks, • Web server logs, • Traffic flow sensors, • Satellite imagery, • Broadcast audio streams, • Banking transactions, • MP3s of rock music, • The content of web pages, • Scans of government documents, • GPS trails, • Telemetry from automobiles, • Financial market data • ….
  8. 8. How Do We Process It?
  9. 9. Process Pipeline Source: http://radar.oreilly.com
  10. 10. Hadoop A distributed processing Framework based on Map/Reduce
  11. 11. Pig A platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.
  12. 12. Mahout A machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are implemented on top of Apache Hadoop.
  13. 13. Hive Data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in distributed storage.
  14. 14. Pegasus A Peta-scale graph mining system that runs in parallel, distributed manner on top of Hadoop
  15. 15. Sqoop A tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases.
  16. 16. Flume A distributed service for collecting, aggregating, and moving large log data amounts to HDFS.
  17. 17. Yahoo S4 S4 is a general-purpose, distributed, scalable, partially fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data.
  18. 18. Twitter Storm Storm can be used to process a stream of new data and update databases in real time.
  19. 19. Trends Funding, Companies, Applications, Jo bs, IPOs
  20. 20. Funding & IPO • Cloudera, (Commerical Hadoop) more than $75 million • MapR (Cloudera competitor) has raised more than $25 million • 10Gen (Maker of the MongoDB) $32 million • DataStax (Products based on Apache Cassandra) $11 million • Splunk raised about $230 million through IPO
  21. 21. Big Data Application Domains • Healthcare • The public sector • Retail • Manufacturing • Personal-location data • Finance
  22. 22. A Few Examples
  23. 23. PayPal Tracking Architecture
  24. 24. Market and Market Segments Research Data and Predictions
  25. 25. http://wikibon.org/wiki/v/Big_Data_Market_Size_and_Vendor_Revenues
  26. 26. Market for big data tools will rise from $9 billion to $86 billion in 2020
  27. 27. http://wikibon.org/wiki/v/Big_Data_Market_Size_and_Vendor_Revenues
  28. 28. Future of Big Data • More Powerful and Expressive Tools for Analysis • Streaming Data Processing (Storm from Twitter and S4 from Yahoo) • Rise of Data Market Places (InfoChimps, Azure Marketplace) • Development of Data Science Workflows and Tools (Chorus, The Guardian, New York Times) • Increased Understanding of Analysis and Visualization http://www.evolven.com/blog/big-data-predictions.html
  29. 29. http://www.evolven.com/blog/big-data-predictions.html
  30. 30. Opportunities
  31. 31. Skills Gap • Statistics • Operations Research • Math • Programming • So-called "Data Hacking"

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