What Is Hadoop | Hadoop Tutorial For Beginners | Edureka
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( Hadoop Training: https://www.edureka.co/hadoop ) This Edureka "What is Hadoop" tutorial ( Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand how Big Data emerged as a problem and how Hadoop solved that problem. This tutorial will be discussing about Hadoop Architecture, HDFS & it's architecture, YARN and MapReduce in detail. Below are the topics covered in this tutorial: 1) 5 V’s of Big Data 2) Problems with Big Data 3) Hadoop-as-a solution 4) What is Hadoop? 5) HDFS 6) YARN 7) MapReduce 8) Hadoop Ecosystem
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Hadoop Ecosystem | Hadoop Ecosystem Tutorial | Hadoop Tutorial For Beginners ...Simplilearn This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
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7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
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Hadoop technologytipanagiriharika Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
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** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
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** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
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Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Certification: https://www.edureka.co/kubernetes-certification
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DevOps Training: https://www.edureka.co/devops-certification-training
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✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrs Labs Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
Into The Box Conference Keynote Day 1 (ITB2025)
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Semantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AIartmondano By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
What Is Hadoop | Hadoop Tutorial For Beginners | Edureka
- 2. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Agenda 1. 5 V’s of Big Data 2. Problems with Big Data 3. Hadoop-as-a solution 4. What is Hadoop? 5. HDFS 6. YARN 7. MapReduce 8. Hadoop Ecosystem
- 3. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING 5V’s of Big Data
- 4. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Value? Different kinds of data is being generated from various sources Data is being generated at an alarming rate Mechanism to bring the correct meaning out of the data Uncertainty and inconsistencies in the data Data is being generated at an accelerating speed 5 V’s of Big Data Volume Variety Velocity Value Veracity
- 5. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Problems with Big Data Processing
- 6. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Problems with Big Data Highly Scalable Storing huge and exponentially growing datasets Processing data having complex structure (structured, un-structured, semi- structured) Bringing huge amount of data to computation unit becomes a bottleneck
- 7. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING So for Big Data problem statement, Hadoop emerged as a solution…. What is Hadoop?
- 8. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Hadoop is a framework that allows us to store and process large data sets in parallel and distributed fashion Allows to dump any kind of data across the cluster Allows parallel processing of the data stored in HDFS HDFS (Storage) MapReduce (Processing)
- 9. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop 1 hr. HDFS ReadWrite Storing exponentially growing huge datasets Storing unstructured data Processing data faster Allows to store any kind of data, be it structured, semi- structured or unstructured Provides parallel processing of data present in HDFS Allows to process data locally i.e. each node works with a part of data which is stored on it HDFS, storage unit of Hadoop is a Distributed File System 2 31
- 10. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Distributed File System (HDFS)
- 11. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING HDFS NameNode DataNode DataNodeDataNode Slave Node Master NodeHDFS ▪ Storage unit of Hadoop ▪ Distributed File System ▪ Divide files (input data) into smaller chunks and stores it across the cluster ▪ Horizontal Scaling as per requirement ▪ Stores any kind of data ▪ No schema validation is done while dumping data
- 12. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING HDFS Block file.xml 128 MB 128 MB 128 MB 128 MB HDFS Cluster HDFS Blocks moving to HDFS • HDFS stores the data in form of blocks • Block size can be configured base on requirements Note: The default Block Size is 128 MB
- 13. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING NameNode • Master daemon • Maintains and Manages DataNodes • Records metadata e.g. location of blocks stored, the size of the files, permissions, hierarchy, etc. • Receives heartbeat and block report from all the DataNodes NameNode NameNode DataNode DataNodeDataNode Secondary NameNode
- 14. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING DataNode DataNode ▪ Slave daemons ▪ Stores actual data ▪ Serves read and write requests NameNode DataNode DataNodeDataNode Secondary NameNode
- 15. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Secondary NameNode Secondary NameNode • Checkpointing is a process of combining edit logs with FsImage • Allows faster Failover as we have a back up of the metadata • Checkpointing happens periodically (default: 1 hour) NameNode DataNode DataNodeDataNode Secondary NameNode
- 16. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Distributed File System NameNode DataNode DataNodeDataNode Secondary NameNode Secondary NameNode NameNode editLog editLog fsImage fsImage editLog (new) FsImage (final) First time copy Temporary During checkpoint
- 17. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING YARN (Yet Another Resource Negotiator)
- 18. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING YARN Resource Manager Node Manager Node Manager Node Manager ResourceManager • Receives the processing requests • Passes the parts of requests to corresponding NodeManagers NodeManagers • Installed on every DataNode • Responsible for execution of task on every single DataNode
- 19. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING YARN Architecture Resource Manager Node Manager Node Manager container App Master App Master container Node Manager App Master container Client Node Status Resource Request MapReduce Status App Manager ResourceManager has two components: Schedulers & ApplicationsManager NodeManager has two components: ApplicationMaster & Container
- 20. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING YARN Architecture Resource Manager Node Manager container App Master NodeStatus ResourceRequest App Manager ApplicationsManager • ApplicationsManager accepts the job submission • Negotiates to containers for executing the application specific ApplicationMaster and monitoring the progress ApplicationsMaster • ApplicationMasters are the deamons which reside on DataNode • Communicates to containers for execution of tasks on each DataNode
- 21. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Architecture Bigger Picture
- 22. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Architecture Secondary NameNode NameNode ResourceManager DataNode DataNode NodeManger NodeManager container App Master container App Master NodeManger NodeManager container App Master container App Master DataNode DataNode JobHistory Server HDFS YARN
- 23. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING MapReduce
- 24. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING MapReduce MapReduce is a software framework which helps in writing applications that processes large data sets using distributed and parallel algorithms inside Hadoop environment.
- 25. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING MapReduce Job Workflow IND ENG AUS NZ NZ ENG AUS IND AUS IND SL NZ IND, ENG, AUS, NZ AUS, IND, SL, NZ NZ, ENG, AUS, IND IND, 1 ENG, 1 AUS, 1 NZ, 1 AUS, 1 IND, 1 SL, 1 NZ, 1 NZ, 1 ENG, 1 AUS, 1 IND, 1 IND, (1,1,1) ENG, (1,1) AUS, (1,1,1) NZ, (1,1,1) SL, (1) IND, 3 ENG, 2 AUS, 3 NZ, 3 SL, 1 INPUT SPLITTING MAPPING SHUFFLING FINAL RESULT IND, 3 ENG, 2 AUS, 3 NZ, 3 SL, 1 REDUCING
- 26. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Ecosystem
- 27. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Hadoop Ecosystem