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Introduction to Hadoop

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What is Apache Hadoop?

Apache Hadoop was born to enhance the usage and solve major issues of big data. The web media was generating loads of information on a daily basis, and it was becoming very difficult to manage the data of around one billion pages of content. In order of revolutionary, Google invented a new methodology of processing data popularly known as MapReduce. Later after a year Google published a white paper of Map Reducing framework where Doug Cutting and Mike Cafarella, inspired by the white paper and thus created Hadoop to apply these concepts to an open-source software framework that supported the Nutch search engine project. Considering the original case study, Hadoop was designed with much simpler storage infrastructure facilities.

Apache Hadoop is the most important framework for working with Big Data. Hadoop biggest strength is scalability. It upgrades from working on a single node to thousands of nodes without any issue in a seamless manner.

The different domains of Big Data means we are able to manage the data’s are  from videos, text medium, transactional data, sensor information, statistical data, social media conversations, search engine queries, ecommerce data, financial information, weather data, news updates, forum discussions, executive reports, and so on

Google’s Doug Cutting and his team members developed an Open Source Project namely known as HADOOP which allows you to handle the very large amount of data. Hadoop runs the applications on the basis of MapReduce where the data is processed in parallel and accomplish the entire statistical analysis on large amount of data.

It is a framework which is based on java programming. It is intended to work upon from a single server to thousands of machines each offering local computation and storage. It supports the large collection of data set in a distributed computing environment.

The Apache Hadoop software library based framework that gives permissions to distribute huge amount of data sets processing across clusters of computers using easy programming models.

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History of Hadoop

Let’s discuss the History of Hadoop in the Tabular format:

Year Milestone
2003 Google publishes the MapReduce and Google File System (GFS) papers, outlining the principles of distributed computing and storage.
2004 Doug Cutting and Mike Cafarella created an open-source implementation of Google’s MapReduce, which later became Apache Hadoop.
2006 Yahoo started using Hadoop for its web search and other applications, contributing to its development and forming the Hadoop project.
2008 Apache Hadoop became an Apache Top-Level Project, signifying its maturity and widespread adoption in the industry.
2009 Hadoop 0.20.0, a significant release, introduces HDFS federation, security improvements, and support for HBase and Pig.
2011 Hadoop 0.23.0 brings the next-generation MapReduce (YARN) that separates resource management and job scheduling/monitoring.
2012 Hadoop 2.0.0 marks a major milestone with the general availability of YARN, enabling multiple applications to share a cluster.
2014 Hadoop 2.2.0 introduces Hadoop Federation, allowing multiple namespaces in a cluster, improving scalability and isolation.
2017 Hadoop 3.0.0 is released, featuring major improvements like erasure coding, support for more nodes, and performance enhancements.
2018 Hadoop 3.1.0 arrives with features such as container allocation, GPU support, and improved resource utilization for better efficiency.
2019 Hadoop 3.2.0 is released, focusing on stability, performance, and several key feature enhancements in areas like security and storage.

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The Hadoop High-level Architecture

Hadoop Architecture based on two main components namely MapReduce and HDFS

Different Hadoop Architectures based on the Parameters chosen

The Apache Hadoop Module

Hadoop Common: Includes the common utilities which supports the other Hadoop modules

HDFS: Hadoop Distributed File System provides unrestricted, high-speed access to the data application.

Hadoop YARN: This technology is basically used for scheduling of job and efficient management of the cluster resource.

MapReduce: This is a highly efficient methodology for parallel processing of huge volumes of data.

Then there are other projects included in the Hadoop module which are less used:

Apache Ambari: It is a tool for managing, monitoring and provisioning of the Hadoop clusters. Apache Ambari supports the HDFS and MapReduce programs. Major highlights of Ambari are:

  • Managing of the Hadoop framework is highly efficient, secure and consistent.
  • Management of cluster operations with an intuitive web UI and a robust API
  • The installation and configuration of Hadoop cluster are simplified effectively.
  • It is used to support automation, smart configuration and recommendations
  • Advanced cluster security set-up comes additional with this tool kit.
  • The entire cluster can be controlled using the metrics, heat maps, analysis and troubleshooting
  • Increased levels of customization and extension make this more valuable.

Cassandra: It is a distributed system to handle extremely huge amount of data which is stored across several commodity servers. The database management system (DBMS)is highly available  with no single point of failure.

HBaseit is a non-relational, distributed database management system that works efficiently on sparse data sets and it is highly scalable.

Apache Spark: This is highly agile, scalable and secure the Big Data compute engine, versatiles the sufficient work on a wide variety of applications like real-time processing, machine learning, ETL and so on.

Hive: It is a data warehouse tool basically used for analyzing, querying and summarizing of analyzed data concepts on top of the Hadoop framework.

Pig: Pig is a high-level framework which ensures us to work in coordination either with Apache Spark or MapReduce to analyze the data. The language used to code for the frameworks are known as Pig Latin.

Sqoop: This framework is used for transferring the data to Hadoop from relational databases. This application is based on a command-line interface.

Oozie: This is a scheduling system for workflow management, executing workflow routes for successful completion of the task in a Hadoop.

Zookeeper: Open source centralized service which is used to provide coordination between distributed applications of Hadoop. It offers the registry and synchronization service on a high level.

  • Hadoop Mapreduce (Processing/Computation layer) –MapReduce is a parallel programming model mainly used for writing large amount of data distribution applications devised from Google for efficient processing of large amounts of datasets, on large group of clusters.
  • Hadoop HDFS (Storage layer) – Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) which provides a distributed file system that is especially designed to run on commodity hardware. It reduces the faults or errors and helps incorporate low-cost hardware. It gives high level processing throughput access to application data and is suitable for applications with large datasets.
  • Hadoop YARN –Hadoop YARN is a framework  used for job scheduling and cluster resource management.
  • Hadoop Common –This includes Java libraries and utilities which provide those java files which are essential to start Hadoop.
  • Task Tracker –It is a node which is used to accept the tasks such as shuffle and Mapreduce form job tracker.
  • Job Tracker –It is a service provider which runs Mapreduce jobs on cluster.
  • Name Node –It is a node where Hadoop stores all file location information(data stored location) in Hadoop distributed file system.
  • Data Node – The data is stored in the Hadoop distributed file system.
  • Data Node –It stores data in the Hadoop distributed file system.

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Prerequisites for Hadoop

Here are the prerequisites for learning Hadoop:

Prerequisite Description
Basic Understanding of Programming Proficiency in a programming language (e.g., Java, Python) is beneficial for writing MapReduce jobs and working with Hadoop.
Knowledge of Linux/Unix Familiarity with Linux/Unix environments and basic command-line operations is important for Hadoop installation and management.
Understanding of Databases A grasp of database concepts, including SQL and relational databases, is useful for working with Hadoop’s distributed file system.
Familiarity with Java Knowledge of Java is advantageous for customizing Hadoop configurations and writing MapReduce programs.
Understanding of Networking Basic knowledge of networking concepts, such as IP addresses and ports, is helpful for configuring Hadoop clusters.
Basics of Data Storage and Retrieval Understanding how data is stored, retrieved, and processed is essential for effectively utilizing Hadoop’s capabilities.
Knowledge of XML and JSON Familiarity with data interchange formats like XML and JSON is useful for processing data in various formats with Hadoop.

These prerequisites will provide a solid foundation for learning and working with Hadoop.

How does Hadoop Work?

Hadoop helps to execute large amounts of processing where the user can connect together multiple commodity computers to a single, as a single functional distributed system and have a particular set of clustered machines that reads the dataset in parallel and provide intermediate, and after integration gets the desired output.

Hadoop runs code across a cluster of computers and performs the following tasks:

  • Data are initially divided into files and directories. Files are divided into consistent sized blocks ranging from 128M and 64M.
  • Then the files are distributed across various cluster nodes for further processing of data.
  • Job tracker starts its scheduling programs on individual nodes.
  • Once all the nodes are done with scheduling then the output is return back.

The Ultimate Goal

  • Apache Hadoop framework
  • Hadoop Distributed in File System
  • Visualizing of Data using MS Excel, Zoom data or also known as Zeppelin
  • Apache MapReduce program
  • Apache Spark ecosystem
  • Ambari administration management
  • Deploying Apache Hive and Pig, and Sqoop
  • Knowledge of the Hadoop 2.x Architecture
  • Data analytics based on Hadoop YARN
  • Deployment of MapReduce and HBase integration
  • Setup of Hadoop Cluster
  • Proficiency in Development of Hadoop
  • Working with Spark RDD
  • Job scheduling using Oozie

The above methodology guide you to become professional of Big Data and Hadoop and ensuring enough skills to work in an industrial environment and solve real world problems and gain solutions for the better progressions.

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The Challenges facing Data at Scale and the Scope of Hadoop

Big Data are categorized into:

  • Structured –which stores the data in rows and columns like relational data sets
  • Unstructured – here data cannot be stored in rows and columns like video, images, etc.
  • Semi-structured – data in format XML are readable by machines and human

There is a standardized methodology that Big Data follows highlighting usage methodology of ETL.

ETL – stands for Extract, Transform, and Load.

Extract –fetching the data from multiple sources

Transform – convert the existing data to fit into the analytical needs

Load –right systems to derive value in it.

Comparison to Existing Database Technologies

Most database management systems are not up to scratch for operating at such lofty levels of Big data exigencies either due to the sheer technical inefficient. When the data is totally unstructured, the volume of data is humongous, where the results are at high speeds, then finally only platform that can effectively stand up to the challenge is Apache Hadoop.

Hadoop majorly owes its success to a processing framework called as MapReduce that is central to its existence. The MapReduce technology gives opportunity to all programmers contributes their part where large data sets are divided and are independently processed in parallel. These coders doesn’t need to knew the high-performance computing and can work efficiently without worrying about intra-cluster complexities, monitoring of tasks, node failure management, and so on.

Hadoop also contributes it’s another platform namely known as Hadoop Distributed File System (HDFS). The main strength of HDFS is its ability to rapidly scale and work without a hitch irrespective of any fault with the nodes. HDFS in essence divides large file into smaller blocks or units ranging from 64 to 128MB later are copied onto a couple of nodes of the cluster. From this HDFS ensures no work would stop even when some nodes going out of service. HDFS owns APIs to ensure The MapReduce program is used for reading and writing data (contents) simultaneously at high speeds. When there is a need to speed up performance, and then add extra nodes in parallel to the cluster and the increased demand can be immediately met.

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Advantages of Hadoop

  • It give access to the user to rapidly write and test the distributed systems and then automatically distributes the data and works across the machines and in turn utilizes the primary parallelism of the CPU cores.
  • Hadoop library are developed to find/search and handle the failures at the application layer.
  • Servers can be added or removed from the cluster dynamically at any point of time.
  • It is open source based on Java applications and hence compatible on all the platforms.

Hadoop Features and Characteristics

Apache Hadoop is the most popular and powerful big data tool, which  provides world’s best reliable storage layer –HDFS(Hadoop Distributed File System), a batch Processing engine namely  MapReduce and a Resource Management Layer like YARN. Open-source – Apache Hadoop is an open source project. It means its code can be modified according to business requirements.

  • Distributed Processing– The data storage is maintained in a distributed manner in HDFS across the cluster, data is processed in parallel on cluster of nodes.
  • Fault Tolerance– By default the three replicas of each block is stored across the cluster in Hadoop and it’s changed only when required. Hadoop’s fault tolerant can be examined in such cases when any node goes down, the data on that node can be recovered easily from other nodes. Failures of a particular node or task are recovered automatically by the framework.
  • Reliability– Due to replication of data in the cluster, data can be reliable which is stored on the cluster of machine despite machine failures .Even if your machine goes down, and then also your data will be stored reliably.
  • High Availability– Data is  available and accessible even there occurs a hardware failure due to multiple copies of data. If any incidents occurred such as if your machine or few hardware crashes, then data will be accessed from other path.
  • Scalability– Hadoop is highly scalable and in a unique way hardware can be easily added to the nodes. It also provides horizontal scalability which means new nodes can be added on the top without any downtime.
  • Economic– Hadoop is not very expensive as it runs on cluster of commodity hardware. We do not require any specialized machine for it. Hadoop provides huge cost reduction since it is very easy to add more nodes on the top here. So if the requirement increases, then there is an increase of nodes, without any downtime and without any much of pre planning.
  • Easy to use– No need of client to deal with distributed computing, framework takes care of all the things. So it is easy to use.
  • Data Locality– Hadoop works on data locality principle which states that the movement of computation of data instead of data to computation. When client submits his algorithm, then the algorithm is moved to data in the cluster instead of bringing data to the location where algorithm is submitted and then processing it.

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Hadoop Assumptions

Hadoop is written with huge amount of clusters of computers in mind and is built upon the following assumptions:

  • Hardware may fail due to any external or technical malfunction where instead commodity hardware can be used.
  • Processing will be run in batches and there exits an emphasis on high throughput as opposed to low latency.
  • Applications which run on HDFS have large sets of data. A typical file in HDFS may be of gigabytes to terabytes in size.
  • Applications require a write-once-read-many access model.
  • Moving Computation is cheaper compared to the Moving Data.

Hadoop Design Principles

The following are the design principles on which Hadoop works:

  • System shall manage and heal itself as per the requirement occurred.
  • Fault Tolerant are automatically and transparently route are managed around failures speculatively execute redundant tasks if certain nodes are detected to be running of slower phase.
  • Performance is scaled based on linearity.
  • Proportional change in terms of capacity with resource been change (Scalability)
  • Compute must be moved to data.
  • Data Locality is termed as lower latency, lower bandwidth.
  • It is based on simple core, modular and extensible (Economical).

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