Buzz word

AGENDA

Big Data refers to massive, often unstructured data that is beyond the processing capabilities of traditional database 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.

3 V’s of big data ?..

Structured vs unstructured

What is BIG DATA ?

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 .

2.7 zetabytes , hydron exp.

Volume

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.

90k youtube, 45k google/sec

Velocity

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 etc

How we gain? Video.. Portable devices, sensors n Social media

Variety

Transform problems into possibilities

Big data analytics ..

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 jayate.

Types of Analytics..

Future scope – Genes reading for curing deadly diseases like cancer .

Big Data Analytics

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.

Leading Technologies

Hadoop is an open source framework.

Generally is Java-based programming framework .

Processing and storing of large data sets.

Distributed computing environment.

Components of hadoop HDFS( hadoop distributed

file system). Mapreduce.

Hadoop

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 .

HDFS (Hadoop Distributed File System)

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 ???...

Hadoop SQL

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- Count all words of my twitter history to check what i talk about the most. The query is split across multiple servers with a criteria (here months), and the results are consolidated.

Benefits of Hadoop

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 hosts 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..

Map-Reduce Algorithm

MR - Word count example

Mapper function maps the split files and provide input to reducer.

Mapper ( split_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 + valueemit(word , sum)

can anyone think of any disadvantages??..

Mapper() and Reducer()

There were 2 major disadvantages when hadoop was developed which now its strengths.

HDFS dependency on single Namenode solution: A secondary Namenode is attached to Primary

Namenode. MapReduce is a java framework and did not support sql queries

solution: Facebook developed HIVE which allowed scientists to work with sql on distributed database.

Disadvantages of hadoop

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

NoSQL

KEY-VALUE PAIR

Keys used to get Value from opaque Data blocks.

Hash map.

Tremendously fast.

Drawback:No provision for content based queries .

NoSQL Categories

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.

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.

GRAPH DATABASES

• Is a collection of nodes and edges.

• Nodes represent datawhile edges represent link between them.

• Most dynamic and flexible.

Base Vs Acid properties ..

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

CONCLUSION

Websites :• http://searchbusinessanalytics.techtarget.com/

Experts sound off on big data , Analytics and its tools• http://www.ibmbigdatahub.com/infographic/four-vs-big-data

Big data and analytics hub• https://bigdatauniversity.com/bdu-wp/bdu-course/hadoop-fundamentals-i-version-3/

Hadoop fundamentals

Research papers : Dean J. and Ghemawat S., “MapReduce: Simplified Data Processing on Large Clusters ”,“OSDI: Sixth Symposium on Operating System Design San Francisco, CA”, “2004”.

References