Big data and hadoop ecosystem tools

 Introduction to Big Data
 Properties of Big Data
 Introduction to Hadoop
 Core components in Hadoop
 MapReduce
 Hadoop Ecosystem tools
 Conclusion

A data which is beyond storage capacity and beyond
processing power
Properties of Big Data
According to IBM
Volume
Velocity
variety

1. Structured Data
RDBMS
2. Semi Structured Data
Log Files
3. Unstructured Data
text, audio, video, image etc..

Name Node
 Master of the system
 Maintains and manages the blocks
of data nodes
Data Node
 salves and provides actual storage
 responsible for read and write operations

 Highly fault-tolerant
 High Throughput
 Suitable for applications with large data dets
 Write once and read many times
 Can be built by commodity hardware
 Replicating data across different data nodes

 Low latency data access(quickly access small data)
 Lots of small files
 Multiple writes, arbitrary file modifications

 Familiar with SQL use
 Initially given by Facebook
 Internally runs with MapReduce
 HiveQL-Hive Query Language act as interpreter
 Can load thousands of rows at a time

Importing data from RDBMS to HDFS
Exporting data from HDFS to RBMS
Used to Store data in Hbase
Used to upload data to Hive

 No need of lot of knowledge in programming and
SQL
 Simplifies the work done by mapreduce programs
 Initially given by Yahoo
 Own language “Pig Latin Scripting”

 Works as a server
 Coordinating more than one job at a time

 No SQL
 Column Oriented Format
 Data can be stored and processed

 Hadoop can handle any type of data
 Open Source from Apache
 Fault Tolerant
 Provides tools for various domain knowledge
 Works very fast compared to others

Big data and hadoop ecosystem tools

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