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Big data analytics: Technology's bleeding edge
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Big data analytics: Technology's bleeding edge


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There can be data without information , but there can not be information without data. …

There can be data without information , but there can not be information without data.
Companies without Big Data Analytics are deaf and dumb , mere wanderers on web.

Published in: Technology

  • Thanks alot Charan for the motivation comming in . I am glad you liked it :)
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  • Hey Bhavya, I reviewed your presentation and want to congratulate you on your effort. These are good intro slides to share with your batch mates who may want to understand all the fuss around Big data :-)...
    Good job..
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  • 1.  Big Data refers to massive, often unstructured data that is beyond the processing capabilities of traditional data management tools.  Big Data can take up terabytes and petabytes of storage space in diverse formats including text, video, sound, images etc.  Traditional relational database management systems cannot deal with such large masses of data.  Examples : User updates over fb. Clicks over the internet.
  • 2.  Volume refers to huge amount of data being generated every minute.  90% of the data we have now is created in just past 2 years.  IP traffic by 2015 would turn 4X than what it is now.  3 billion people would be online by 2015 .
  • 3.  Velocity refers to SPEED at which new data is being generated and moves around.  It includes Real time working systems such as Online banking.  Need of low response time.  Technology “In-Memory Analytics” is employed to deal with data in motion.
  • 4.  Variety refers to various datatypes which we can now use.  Earlier focus was on neat and structured data kept in form of tables in RDBMS.  80% of data available now is unstructured data  Datatypes are anomalous varying from text to videos to audios to pictures.
  • 5. Transform problems into possibilities
  • 6.  It is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other real- time insights.  Use of Big Data Analytics – Google Search recommendations, Satyamev jayte, Genes reading Data Mining Big data Analytics Data constraints like data must be neat and clean  Big data can not be neat as it is unstructured  Elaborate ETL required thus have to wait for completion of ETL cycle for insights.  Big data analytics provide real – time insights.
  • 7.  Descriptive  Diagnostic  Predictive  Prescriptive
  • 8.  Relational databases failed to store and process Big Data.  As a result, a new class of big data technology has emerged and is being used in many big data analytics environments.  The technologies associated with big data analytics include  Hadoop  Mapreduce  NoSQL
  • 9.  Hadoop is a open source framework  Java-based programming framework  Processing and storing of large data sets  Distributed computing environment.  Components of hadoop  HDFS( hadoop distributed file system)  Mapreduce
  • 10.  HDFS stores data in DISTRIBUTED,SCALABLE and FAULT- TOLERANT WAY.  Name node have metadata about data on DataNodes  DataNodes actually have data on them in form of blocks and they are capable of communicating
  • 11. Hadoop SQL  Data is stored in form of compressed files across n number of commodity servers  Data is stored in form of tables and columns with relation in them  Fault tolerant – if one node fails ,system still work  If any one node crashes ,it gives error so as to maintain consistency Any questions ???...
  • 12.  Copying same file over all (thousands) of nodes ? doesn’t it seem like wastage of space !  It actually is not a waste memory, because of 2 reasons:  If one node failed ,System would still work as data is never lost.  The query is scaled over nodes so it bring about faster results due to parallel processing eg- Select the count of word ‘happy’ on twitter. The query is split across multiple servers with a criteria (here months), and the results are consolidated.
  • 13.  MapReduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. as in previous example twitter data was processed on different servers on basis of months .  Hadoop is the physical implementation of Mapreduce .  It is combination of 2 java functions : Mapper() and Reducer()  example: to check popularity of text. use of word-count..
  • 14.  Mapper function maps the split files and provide input to reducer  Mapper ( filename , file –contents): for each word in file-contents: emit (word , 1)  Reducer function clubs the input provided by mapper and produce output  Reducer ( word , values): sum=0; for each value in values: sum=sum + value emit(word , sum) can anyone think of any disadvantages??..
  • 15.  There were 2 major disadvantages when hadoop was developed which now have been dissolved  HDFS dependency on single Namenode solution: A secondary Namenode is attached to Primary Namenode  MapReduce is a java fraamework and did not support sql queries solution: Facebook developed HIVE which allowed scientists work with sql on distributed database.
  • 16.  Not only SQL  Non- relational database management system  Used where no fix schemas are required and data is scaled horizontally.  4 Categories of Nosql databases:  Key-value pair  Columnar database  Graph databases  Document databases
  • 17.  KEY-VALUE PAIR  keys used to get Value from opaque Data blocks  Hash map  Tremendously fast Drawback: No provision for content based queries .
  • 18.  DOCUMENT DATABASE • Again a key value store but value is in form of document. • Documents are not of fixed schemas • documents can be nested • Queries based on content as well as keys • Use cases: blogging websites
  • 19.  COLUMNAR DATABASE  Works on attributes rather than tuples  Key here is column name and value is contiguous column values  Best for aggregation queries  Trend : select (1 or 2 column’s values ) where ( same or the other column value ) = some value.
  • 20.  GRAPH DATABASES • Is a collection of nodes and edges • Nodes represent data while edge represent link between them • Most dynamic and flexible
  • 21.  Websites : • Experts sound off on big data , Analytics and its tools • Big data and analytics hub • fundamentals-i-version-3/ Hadoop fundamentals Research papers : •MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat Appeared in: OSDI'04: Sixth Symposium on Operating System Design San Francisco, CA, December, 2004.
  • 22. Data is the new oil Without Big data analysis companies are deaf and dumb , mere wanderers on web ... Like a cattle on the highway ! Thank you ! Keep dreaming BIG :D