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Apache Hadoop Architecture Explained (With Diagrams)
Apache Hadoop is a popular big data framework that allows organizations to store, process, and analyze vast amounts of data. architecture of Hadoop is designed to handle large amounts of data by using distributed storage and processing. In this article, we will explain architecture of Apache Hadoop and its various components with diagrams.
Introduction to Apache Hadoop
Apache Hadoop is an open-source software framework used for storing and processing large amounts of data in a distributed environment. It was created by Doug Cutting and Mike Cafarella in 2006 and is currently maintained by Apache Software Foundation.
Hadoop is widely used in various industries, including finance, healthcare, retail, and government, for tasks such as data warehousing, data mining, and machine learning. It allows organizations to store and process data on a massive scale, making it easier to gain insights and make better business decisions.
The architecture of Hadoop is designed to handle large amounts of data by using distributed storage and processing. Hadoop consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce.
Hadoop Distributed File System (HDFS)
HDFS is primary storage component of Hadoop. It is designed to store large files and data sets across a cluster of commodity hardware. HDFS is based on a master/slave architecture, where a single NameNode manages file system namespace and regulates access to files, while DataNodes store data blocks and perform read and write operations.
In HDFS, data is split into smaller blocks and distributed across multiple DataNodes. Each block is replicated multiple times for fault tolerance. default replication factor in Hadoop is three, which means that each block is replicated three times. This ensures that even if one or two DataNodes fail, data is still available for processing.
HDFS provides high-throughput access to data and is optimized for large files and data sets. It is not suitable for small files and is not designed for real-time data access.
MapReduce is processing component of Hadoop. It is a programming model and framework for processing large data sets in parallel across a cluster of commodity hardware. MapReduce works by dividing a large data set into smaller subsets and processing each subset in parallel across multiple nodes in cluster.
MapReduce consists of two main functions: Map and Reduce. Map function takes an input data set and transforms it into a set of key/value pairs. Reduce function takes output of Map function and combines values with same key. MapReduce jobs can be written in various programming languages, including Java, Python, and C++.
The Hadoop ecosystem is a collection of open-source tools and frameworks that extend functionality of Hadoop. Some of most popular tools in Hadoop ecosystem are −
Hive − Hive is a data warehouse system that provides SQL-like queries and data analysis for Hadoop. It allows users to query data stored in Hadoop using a familiar SQL-like syntax.
Pig − Pig is a high-level platform for creating MapReduce programs used to analyze large data sets. Pig provides a scripting language called Pig Latin, which simplifies creation of MapReduce jobs.
HBase − HBase is a NoSQL database that provides real-time access to data stored in Hadoop. It is designed for use with large data sets and provides random, real-time read/write access to data.
ZooKeeper − ZooKeeper is a distributed coordination service that provides a centralized repository for configuration information. It is used to manage Hadoop clusters and provides reliable distributed coordination.
Oozie − Oozie is a workflow scheduler system used to manage Hadoop jobs. It allows users to define and execute workflows that consist of multiple Hadoop jobs.
Apache Hadoop Architecture Diagram
As shown in diagram, architecture of Hadoop consists of several layers −
This layer represents end-users who interact with Hadoop using various tools such as Hive, Pig, and Oozie.
This layer provides a set of APIs that are used by user layer to interact with Hadoop. Some of popular APIs in Hadoop ecosystem are Hadoop Common, HDFS, and YARN.
This layer represents MapReduce processing framework, which is used to process large data sets in parallel across a cluster of nodes.
This layer represents HDFS storage system, which is used to store large files and data sets across a cluster of nodes.
Resource Management Layer
This layer represents YARN resource management system, which is used to manage resources such as CPU, memory, and disk across a cluster of nodes.
This layer represents physical cluster of nodes that make up Hadoop cluster.
In addition to core components of Hadoop, there are other components that are commonly used in Hadoop ecosystem. Some of these components are −
Apache Spark is a fast and general-purpose engine for large-scale data processing. It provides APIs for programming in Java, Scala, and Python, and supports various data sources such as Hadoop Distributed File System (HDFS), Cassandra, and HBase.
Apache Kafka is a distributed streaming platform that can be used for building real-time data pipelines and streaming applications. It provides high-throughput and low-latency data delivery and supports various data sources such as HDFS, HBase, and Elasticsearch.
Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of log data from various sources to a centralized data store.
Apache Sqoop is a tool used for transferring data between Hadoop and structured data stores such as relational databases. It provides a command-line interface and supports various databases such as MySQL, Oracle, and SQL Server.
Apache Mahout is a machine learning library that provides algorithms for clustering, classification, and collaborative filtering. It supports various data sources such as Hadoop Distributed File System (HDFS) and Apache HBase.
These components and others in Hadoop ecosystem work together to provide a comprehensive big data processing platform that can be customized to meet specific business needs.
Advantages of Hadoop Architecture
The architecture of Hadoop provides several advantages for organizations that need to process large amounts of data. Some of these advantages are −
Hadoop can scale horizontally by adding more nodes to cluster, which allows organizations to handle larger data sets and processing workloads.
Hadoop is designed to handle hardware failures by replicating data across multiple nodes. This ensures that data is not lost and processing can continue even if some nodes fail.
Hadoop uses commodity hardware and open-source software, which makes it more cost-effective than traditional data processing solutions.
Hadoop can be customized to meet specific business needs by using various components in Hadoop ecosystem. This allows organizations to build solutions that are tailored to their specific requirements.
Apache Hadoop is a powerful big data framework that allows organizations to store, process, and analyze large amounts of data. Its architecture is designed to handle large amounts of data by using distributed storage and processing. Hadoop consists of two main components: HDFS and MapReduce. Hadoop ecosystem provides a collection of open-source tools and frameworks that extend functionality of Hadoop. Understanding architecture of Hadoop is essential for anyone working with big data and can help organizations make better business decisions.
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