Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.

1. Big Data
Analytics

2.  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.

3.  Descriptive
 Diagnostic
 Predictive

Prescriptive

4.  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

5.  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

6.  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

7.  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.

8.  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)

9.  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

10.  KEY-VALUE PAIR
 keys used to get
Value from opaque
Data blocks
 Hash map
 Tremendously fast
Drawback:
No provision for content based queries .

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