Types of Hadoop and Hadoop Architecture
Types of Hadoop:
Apache Hadoop: Apache Hadoop is the core open-source framework that provides the fundamental components for distributed storage and processing of big data. It includes HDFS (Hadoop Distributed File System) for data storage and MapReduce for data processing. Apache Hadoop is highly customizable and widely used across industries.
Cloudera Hadoop: Cloudera is a well-known company that offers its distribution of Hadoop with additional features and services. Cloudera's distribution includes management tools, security features, and performance enhancements, making it easier for enterprises to deploy and manage Hadoop clusters.
Hortonworks Hadoop: Hortonworks, now merged with Cloudera, was another major player in the Hadoop distribution space. Like Cloudera, Hortonworks provided its version of Hadoop with additional tools and services to simplify big data processing for organizations.
MapR Hadoop: MapR was a Hadoop distribution known for its focus on performance, scalability, and reliability. It included its file system, MapR-FS, which offered some advantages over the traditional HDFS.
IBM InfoSphere BigInsights: IBM's Hadoop distribution, BigInsights, included value-added features like text analytics, Big SQL for querying data in Hadoop using SQL, and integration with other IBM products.
Amazon EMR: While not a traditional Hadoop distribution, Amazon Elastic MapReduce (EMR) is a cloud-based service provided by Amazon Web Services (AWS). It allows users to create Hadoop clusters on-demand without the need for manual setup, making it easy to process large amounts of data in the cloud.
Hadoop Architecture:
The Hadoop architecture consists of several key components that work together to enable the storage and processing of big data in a distributed manner. The primary components of Hadoop architecture are as follows:
Hadoop Distributed File System (HDFS): HDFS is the storage layer of Hadoop. It is a distributed file system that divides large files into smaller blocks and stores them across multiple nodes in the Hadoop cluster. Each block is replicated across several nodes for fault tolerance. HDFS is designed for high-throughput data access, making it ideal for big data processing.
NameNode: The NameNode is the master node in the Hadoop cluster responsible for managing the file system metadata, such as the directory tree and file-to-block mappings. It does not store the actual data but keeps track of where data blocks are located in the cluster.
DataNodes: DataNodes are worker nodes in the Hadoop cluster that store the actual data blocks. They communicate with the NameNode and follow its instructions for block replication and data storage.
YARN (Yet Another Resource Negotiator): YARN is the resource management layer in Hadoop. It manages and allocates resources (CPU and memory) across various applications running in the cluster. YARN enables the execution of MapReduce jobs and other data processing frameworks like Apache Spark and Apache Flink.
MapReduce: MapReduce is the processing engine in Hadoop for distributed data processing. It processes data in parallel across the cluster by dividing the input data into smaller chunks, performing map and reduce operations on those chunks, and then aggregating the results.
JobTracker (Obsolete): In older versions of Hadoop, the JobTracker was responsible for managing MapReduce jobs. However, with the introduction of YARN, JobTracker was replaced by the ResourceManager.
ResourceManager: The ResourceManager is the central authority in YARN that manages job scheduling and resource allocation for different applications.
TaskTracker (Obsolete): Similar to JobTracker, TaskTracker was used in older Hadoop versions to manage individual tasks within a MapReduce job. With YARN, TaskTracker was replaced by NodeManager.
NodeManager: NodeManager runs on each DataNode and is responsible for managing resources on that node and executing tasks assigned by the ResourceManager.
Hadoop Clients: Hadoop clients are machines or applications that interact with the Hadoop cluster. They submit jobs, read data, and interact with Hadoop services through APIs or command-line utilities.
In summary, the Hadoop architecture is a distributed and fault-tolerant system that stores and processes big data efficiently. It leverages HDFS for storage and YARN for resource management, allowing various data processing engines like MapReduce, Spark, and Flink to execute on the cluster, making it a versatile and powerful solution for big data analytics and processing.
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