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Nagarjuna K. HIVE. Why and What Hadoop ?. A tool to process big data . What is BIG Data ?. Facebook, Google+ etc., Machines too generate lots of data We are having a online discussion now , certainly how many of us are in this conference will also be recorded as data.

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HIVE

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  1. Nagarjuna K HIVE

  2. Why and What Hadoop ? • A tool to process big data

  3. What is BIG Data ? • Facebook, Google+ etc., • Machines too generate lots of data • We are having a online discussion now , certainly how many of us are in this conference will also be recorded as data.

  4. What is BIG Data ? ..continued • Exponential growth of data  challenges to Google, Yahoo, Microsoft, Amazon • Need to go through TBs and PBs of data ? • Which websites and books were popular ? • What kind of Ads appeal to them ? • Existing tools became inadequate to process such large data sets.

  5. Why is the data so BIG ? • Till Couple of decade back  Floppy disks • From then on  CD/DVD Drives • Half a decade back  Hard drives (500 GB) • Now  Hard Drives(I TB) are available in abundance

  6. Why is the data so BIG ? • So WHAT ? • Even the technology to read has taken a leap.

  7. Why is the data so BIG ?

  8. How to handle such BIG ? • BIG elephant • Numerous small chicken ?

  9. How to handle such BIG ? • Concept of Torrents • Reduce time to read by reading it from multiple sources simultaneously. • Imagine if we had 100 drives, each holding one hundredth of the data. Working in parallel, we could read the data in less than two minutes.

  10. How to handle such BIG ? -- Issues • How to handle a system up and downs ? • How to combine the data from all the systems ?

  11. Problem1 : System’s Ups and Downs • Commodity hard ware for data storage and analysis • Chances of failure are very high • So, have a redundant copy of the same data across some machines • In case of eventuality of one machine, you have the other • Google came up with a file system  GFS (Google File System) which implemented all these details.

  12. GFS • Divides data into chunks and stores in the file System • Can store data in ranges of PBs also

  13. Problem 2 : How to combine the data ? • Analyze data across different machines , But how do we merge them to get a meaningful outcome ? • Yes, all (some) of the data has to travel across network. Then only merging of the data can occur. • Doing this is notoriously challenging • Again Google  Map—Reduce

  14. Map Reduce • Provides a programming model  abstracts the problem of disk reads and writes transforming in to a computation of keys and values. • Two phases • Map • Reduce

  15. So what is Hadoop ? • An operating system ? • Provides • A reliable shared storage system • Analysis system

  16. History of Hadoop • Google was the first to launch GFS and MapReduce • They published a paper in 2004 announcing the world a brand new technology • This technology was well proven in Google by 2004 itself MapReduce paper by Google

  17. History of Hadoop • Doug Cutting saw an opportunity and led the charge to develop an open sourceversion of this MapReduce system called Hadoop . • Soon after, Yahoo and othersrallied around to support this effort. • Now Hadoop is core part in : • Facebook, Yahoo, LinkedIn, Twitter …

  18. History of Hadoop • GFS  HDFS • MapReduce  MapReduce

  19. HDFS -- A Brief Design  Streaming very large files on commodity cluster. • Very Large Files • MBs to PBs • Streaming • Write once read many approach • After huge data being placed  We tend to use the data not modify it • Time to read the whole data is more important • Commodity Cluster • No High end Servers • Yes, high chance of failure (But HDFS is tolerant enoguh) • Replication is done

  20. MapReduce -- A Brief • Large scale data processing in parallel. • MapReduce provides: • Automatic parallelization and distribution • Fault-tolerance • I/O scheduling • Status and monitoring • Two phases in MapReduce • Map • Reduce

  21. Hadoop Cluster

  22. Hadoop Ecosystems

  23. So What is Hive

  24. History • Built by Jeff’s team at FaceBook • A tool built for data warehousing on top of hadoop

  25. Why HIVE • huge volumes of data FB producing • burgeoning Social Network • How to analyze the data ?

  26. Who are using HIVE

  27. Version of Hadoop • We will deal with either of • Apache hadoop-0.20 • Clouderahadoop - cdh3

  28. Version of Hive • 0.8.1 • 0.9.*

  29. Pre-Requisites • Core-Java • Acquaintance with LINUX will help. • For better experience :- Linux installation on your machines.

  30. Thank you  • Your feedback is highly important to improve our course material and teaching methodologies. • Please email your suggestions to nagarjuna@outlook.com

  31. Disclaimer • Excel Online classes acknowledges the proprietary rights of the trademarks and product names of other companies mentioned in any of the training material including but not limited to the handouts, written material, videos, power point presentations, etc. All such training materials are provided to our students for learning purposes only. Students shall not use such materials for their private gain nor can they sell any such materials to a third party. Some of the examples provided in any such training materials may not be owned by us and as such we does not claim any proprietary rights for the same. We does not guarantee nor is it responsible for such products and projects. We acknowledges that any such information or product that has been lawfully received from any third party source is free from restriction and without any breach or violation of law whatsoever.

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