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Hadoop
Mayuri Agarwal
Data Management !!!!!!
Big Data-What does it mean?
Velocity:Often time
sensitive , big data must be used as it is
streaming in to the enterprise it
order to maximize its value to the
business.Batch ,Near time ,
Real-time ,streams
Volume:Big data comes in one size : large . Enterprises are awash with data ,easy amassing terabytes
and even petabytes of information.TB , Records , Transactions ,Tables , Files.
Variety:Big data extends beyond structured data ,
including semi-structured and unstructured data to all varieties :text , audio , video ,click
streams ,log files and more Structured , Unstructured , Semi-structured
Veracity:Quality and
provenance of received data.
Good , Undefined , bad ,
Inconsistency , Incompleteness ,
Ambiguity
Value
Big Data
90%
10%
Worldwide Data
Last 2 yearsSince the Beginnning of the Time
What is Hadoop?
Software project that enables the distributed processing of large data sets across clusters of commodity servers
Works with structured and unstructured data
Open source software + Hardware commodity = IT cost Reduction
It is designed to scale up from a single server to thousands of machines
Very high degree of fault tolerance software’s ability to detect and handle failures at the
application layer
The origin of the name Hadoop….
The name Hadoop is not an acronym; it’s a made-up name. The project’s creator, Doug Cutting, explains how the name came about:The name my kid gave a stuffed yellow elephant. Short, relatively easy to spell and pronounce, meaningless, and not used elsewhere: those are my naming criteria. Kids are good at generating such. Googol is a kid’s term.
Hadoop Sub-projects
HDFS
Map-Reduce
HDFS-Hadoop Distributed File System
Distributed, scalable, and portable file system
Each node in a Hadoop instance typically has a single Namenode : a cluster of Datanodes form the HDFS cluster
Asynchronous replication.
Data divided into 64mb (default) or 128mb blocks , each block replicated 3 times (default)
Namenode holds file system metadata.
Files are broken up and spread over Datanode .
HDFS- Read & Write
MapReduce
Software framework for distributed computation
Input | Map() | Copy/Sort | Reduce () | Output
JobTracker schedules and manages jobs.
Task tracker executes individual map() and reduce task on each cluster node.
Example : MapReduce
Master – Slave Model
Hadoop Ecosystem
HBase HBase is an open source , non-relational, distributed database A Key-value store
A value is identified by the key Both key and value are a byte array
The values are stored in key-order Thus access data by key is very fast
Users create table in HBase There is no schema of HBase table Very good for sparse data Takes lots of disk space
HBase Architecture
Master: Responsible for coordinating with region server.
Region server: Serves data for read and write
Zookeeper: Manages the HBase cluster
Low latency and random access to data
Hive
A system for managing and querying structured data built on Hadoop
SQL-Like query language called HQL
Main purpose is analysis and ad hoc querying
Database/table/partition –DDL operation
Not for :small data sets ,Low latency queries ,OLTP
Hadoop-Hive Architecture
HBase-Hive configuration
HBase as ETL data sink
HBase as Data Source
Low Latency warehouse
Hive and MySQL Database Structure
Hadoop Limitations Not a high-speed SQL database. Is not a particularly simple technology. Hadoop is not easy to connect to legacy systems. Hadoop is not a replacement for traditional data warehouses. It is an
adjunctive product to data warehouses. Normal DBAs will need to learn new skills before they can adopt
Hadoop tools. The architecture around the data - the way you store data, the way
you de-normalize data, the way you ingest data, the way you extract data - is different in Hadoop.
Linux and Java skills are critical for making a Hadoop environment a reality.
Hadoop’s Capability Hadoop is a super-powerful environment that can transform your
understanding of data.
Hadoop can store vast amounts of data.
Hadoop can run queries on huge data sets.
You can archive data on Hadoop and still query it.
Hadoop allows you to ingest data at incredible speeds and analyze it and report on it in near real-time.
Hadoop massively reduces the latency of data.
Hadoop: Hot skill to acquire on IT job circuit
The market for data technologies, such as databases, is a multi-billion dollar industry.
Many start-ups are working on technology extensions to Hadoop to make it both analytical and transactional. That would be big.
Major companies have a big data strategy and want to build their businesses on top of this
Google, the originator of Hadoop, has already moved on – suggesting that within a decade either the Hadoop framework will have to be developed beyond all recognition or that something newer could be on the way to supplant it.
Every major internet company - be it Google, Twitter, Linkedin or Facebook - uses some form of Hadoop .
Thanks