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Hadoop Introduction - PowerPoint PPT Presentation
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DataFlair's Big Data Hadoop Tutorial PPT for Beginners takes you through various concepts of Hadoop:This Hadoop tutorial PPT covers: 1. Introduction to Hadoop 2. What is Hadoop 3. Hadoop History 4. Why Hadoop 5. Hadoop Nodes 6. Hadoop Architecture 7. Hadoop data flow 8. Hadoop components – HDFS, MapReduce, Yarn 9. Hadoop Daemons 10. Hadoop characteristics & features Related Blogs: Hadoop Introduction – A Comprehensive Guide: https://goo.gl/QadBS4 Wish to Learn Hadoop & Carve your career in Big Data, Contact us: info@data-flair.training +91-7718877477, +91-9111133369 Or visit our website https://data-flair.training/
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Presentation Transcript
Agenda
- Introduction to Hadoop
- Hadoop nodes & daemons
- Hadoop Architecture
- Characteristics
- Hadoop Features
What is Hadoop?
An Open Source framework that allows distributed processing of large data-sets across the cluster of commodity hardware
What is Hadoop?
An Open Sourceframework that allows distributed processing of large data-sets across the cluster of commodity hardware
Open Source
- Source code is freely available
- It may be redistributed and modified
What is Hadoop?
An open source framework that allows Distributed Processingof large data-sets across the cluster of commodity hardware
Distributed Processing
- Data is processed distributedlyon multiple nodes / servers
- Multiple machines processes the data independently
What is Hadoop?
An open source framework that allows distributed processing of large data-sets across the Clusterof commodity hardware
Cluster
- Multiple machines connected together
- Nodes are connected via LAN
What is Hadoop?
An open source framework that allows distributed processing of large data-sets across the cluster of CommodityHardware
Commodity Hardware
- Economic / affordable machines
- Typically low performance hardware
What is Hadoop?
- Open source framework written in Java
- Inspired by Google's Map-Reduce programming model as well as its file system (GFS)
Doug Cutting added
DFS & MapReduce
in
Hadoop defeated
Super computer
converted 4TB of
image archives over
100 EC2 instances
Doug Cutting started
working on
Doug Cutting
joined Cloudera
2002
2003
2004
2005
2006
2007
2008
2009
published GFS &
MapReduce papers
Hadoop became
top-level project
Development of
started as Lucenesub-project
- launched Hive,
- SQL Support for Hadoop
Basic Hadoop Architecture
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Work
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USER
Master(s)
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100 SLAVES
Hadoop Characteristics
Distributed Processing
Open Source
Fault Tolerance
Easy to use
Reliability
Economic
High Availability
Scalability
Open Source
- Source code is freely available
- Can be redistributed
- Can be modified
Free
Transparent
Affordable
Inter-operable
Open
Source
No vendor lock
Community
Distributed Processing
- Data is processed distributedly on cluster
- Multiple nodes in the cluster process data independently
Centralized Processing
Distributed Processing
Fault Tolerance
- Failure of nodes are recovered automatically
- Framework takes care of failure of hardware as well tasks
Reliability
- Data is reliably stored on the cluster of machines despite machine failures
- Failure of nodes doesn’t cause data loss
High Availability
- Data is highly available and accessible despite hardware failure
- There will be no downtime for end user application due to data
USER
Scalability
- Vertical Scalability – New hardware can be added to the nodes
- Horizontal Scalability – New nodes can be added on the fly
Economic
- No need to purchase costly license
- No need to purchase costly hardware
Economic
Open Source
Commodity Hardware
=
+
Easy to Use
- Distributed computing challenges are handled by framework
- Client just need to concentrate on business logic
Data Locality
- Move computation to data instead of data to computation
- Data is processed on the nodes where it is stored
Data
Data
Data
Data
App Servers
Storage Servers
Algo
Algo
Data
Data
Algorithm
Algo
Algo
Data
Data
Servers
Summary
- Everyday we generate 2.3 trillion GBs of data
- Hadoop handles huge volumes of data efficiently
- Hadoop uses the power of distributed computing
- HDFS & Yarn are two main components of Hadoop
- It is highly fault tolerant, reliable & available
