Components Of Hadoop

What is Hadoop?

Hadoop is an open-source platform for storing, processing and analyzing enormous volumes of data across distributed computing clusters. It is one of the most widely used technologies for big data processing and has emerged as a vital tool for businesses working with big data sets. The core components of Hadoop work together to enable distributed storage and processing capabilities.

Hadoop Through an Analogy

Consider yourself in charge of organizing, analyzing, and processing information from a vast library containing billions of books. But it would take an eternity if you did it all by yourself. In this situation, Hadoop is useful.

Hadoop can be compared to a well-organized group of librarians working side by side in a huge library. Each librarian (cluster node) is responsible for managing a certain collection of books. They store and arrange the books using a smart filing system (HDFS), which makes sure that copies of important books are preserved in different locations for security.

The distribution of work and resources among librarians is overseen by the head librarian (YARN). The head librarian receives requests for specific information and breaks them down into smaller tasks before distributing them to the appropriate librarians for faster processing.

The librarians currently use an innovative strategy called "MapReduce". When someone asks a question (a query), they break it down into smaller sections (Map phase), allocate those parts to the proper librarians, and those librarians swiftly search for the required material in their books. They then collate (Reduce phase) their findings before giving them back to the head librarian, who assembles the information into a thorough response.

The Rise of Big Data

The emergence of Big Data is a reflection of the huge amounts of structured and unstructured data and their growing significance. It has changed how we gather and use information, creating tremendous breakthroughs and opening up new opportunities across a variety of industries.

Today, there are more than 4 billion Internet users. Here's how much data is roughly generated:

• 9,176 Tweets per second
• 1,023 Instagram pictures posted per second
• 5,036 calls on Skype every second
• Google searches happen at 86,497 queries per second.
• 86,302 YouTube videos are viewed every second.
• 2,957,983 emails are sent per second.

Principal Causes of the Rise of Big Data:

• Technological Advancements: Improvements in compute and storage capability make it possible to process large datasets effectively.
• Internet and connectivity: The widespread use of the Internet and the IoT create enormous volumes of data.
• Digitization: Data processing and collecting are facilitated by digitization, the conversion of analog data into digital representations.
• User-Generated Content and Social Media: Social media usage and content creation generate large amounts of data.
• Mobile Devices and Apps: Smartphones and mobile apps create a steady stream of data about user behavior.
• Data-Driven Decision Making: Organizations use data-driven decision-making to optimize operations and enhance products.
• Scientific Research: In order to achieve major findings, research areas rely on large-scale data analysis.
• Real-time data and streaming: Data streaming technologies are driven by the demand for real-time data analysis and response.
• Personalization and Customer Experience: Big Data enables customized experiences and services through personalization.
• Open Data Initiatives: The sharing of datasets encourages the creation of new applications.

Big Data and Its Challenges

Big Data presents enormous opportunities for businesses and organizations to learn insightful things, make data-driven choices, and innovate in many different areas. To fully realize its potential, however, there are a number of issues that must be resolved.

Among the major challenges presented by big data are:

• Volume: Managing and processing vast amounts of data.
• Velocity: Analyzing real-time data streams quickly.
• Variety: Handling diverse data types and formats.
• Veracity: Ensuring data accuracy and quality.
• Complexity: Dealing with data relationships and structures.
• Privacy and Security: Protecting sensitive data.
• Governance: Establishing data management policies.
• Scalability: Adapting to data growth demands.
• Skill Shortage: Lack of qualified data professionals.
• Cost Management: Balancing infrastructure expenses.
• Ethical Concerns: Addressing biases and transparency.
• Legal Compliance: Adhering to data protection laws.

Why Is Hadoop Important?

Hadoop is important for several reasons, as it addresses some of the most critical challenges in modern data processing and analysis. Here are the key reasons why Hadoop holds significance:

• Scalability: Hadoop can manage enormous volumes of data by adding more commodity hardware to the cluster, thanks to its distributed architecture, which enables it to scale horizontally.
• Fault Tolerance: The Hadoop Distributed File System (HDFS) copies data across numerous cluster nodes. Due to this redundancy, the danger of data loss is reduced and system reliability is increased even in case one node fails.
• Cost-Effectiveness: Hadoop uses commodity hardware, which is less expensive. Hadoop is a desirable alternative for businesses looking to handle and store enormous amounts of data without breaking the bank because of its cost-effectiveness.
• Flexibility: Hadoop is adaptable to a variety of data types since it can handle structured, semi-structured, and unstructured data.
• Processing power: Hadoop's MapReduce framework enables parallel data processing across numerous nodes, facilitating quick and effective data analysis. The time required for difficult computations is considerably decreased by this parallel processing capacity.
• Real-time and batch processing: To meet the diverse data processing requirements of enterprises and organizations, Hadoop provides both real-time (via technologies like Apache Spark) and batch processing capabilities.

Components of Hadoop

a. Hadoop HDFS

1. Features of HDFS The Hadoop Distributed File System (HDFS) is a powerful and reliable distributed file system for processing large amounts of data. The following are the primary characteristics of HDFS:

   • Distributed Storage: In a Hadoop cluster, many nodes store a copy each of the data blocks that HDFS has split up into smaller units. Even if some nodes die, this distributed storage guarantees fault tolerance and high availability of data.
   • Scalability: HDFS is made to scale horizontally, making it simple for businesses to add more nodes to their Hadoop clusters in order to handle growing data volumes.
   • Data Replication: HDFS replicates data blocks among nodes to guarantee the accuracy of the data. Each block is kept in three copies by default, one on the node where the data is written and the other two on other nodes in the cluster.
   • Data Compression: Data compression is supported by HDFS, which lowers the amount of storage needed and speeds up data processing.
   • Rack Awareness: To reduce the risk of data loss in the event of rack failures, HDFS attempts to arrange replicas of data blocks on several racks within a data center.

2. Master and Slave Nodes

   • Master Node: The key element of the distributed system is the master node, also referred to as the NameNode in Hadoop's HDFS or the ResourceManager in YARN. Its key features include managing metadata, resources, scheduling, and ensuring fault tolerance and cluster coordination.
   • Slave Node: A slave node is a worker node in the distributed system, sometimes referred to as a DataNode in Hadoop's HDFS or a NodeManager in YARN. According to instructions from the master node, it is in charge of carrying out tasks and storing data, and also reports resources and health status to the master node.

b. Hadoop MapReduce

Hadoop MapReduce is a programming framework and processing engine for distributed data processing on massive clusters. It is an essential part of the Apache Hadoop ecosystem and is a key tool for managing large amounts of data. The MapReduce approach makes it easier to process large datasets by partitioning difficult tasks into more manageable, parallelisable actions.

The main steps of MapReduce are described below:

• Map: Data is separated into smaller splits during the Map phase, and each split is handled independently by several nodes concurrently. In this step, each input split is subjected to a user-defined Map function, which converts it into intermediate key-value pairs.
• Shuffle and Sort: The intermediate key-value pairs are shuffled and sorted based on their keys after the Map phase. In order to prepare the data for the Reduce phase, this step brings together the values mapped to the same key.
• Reduce: The intermediate key-value pairs with the same key are merged during the Reduce phase, and the grouped pairs are then subjected to a user-defined Reduce function to generate the output. This function aggregates the data or conducts calculations on it to produce the required results.

c. Hadoop YARN

A crucial part of the Apache Hadoop ecosystem that was introduced with Hadoop 2.0 is Hadoop YARN (Yet Another Resource Negotiator). The Hadoop Distributed File System (HDFS) and the MapReduce processing engine are separated from one another by the resource management layer known as YARN. It makes the Hadoop ecosystem more effective by enabling Hadoop clusters to handle data processing workloads outside of MapReduce.

The main features and benefits of Hadoop YARN are as follows:

• Resource Management: YARN effectively manages and distributes cluster resources to the applications running on the cluster, including CPU and memory. It enables dynamic resource sharing across different applications, assuring best use.
• Scalability: YARN makes it possible for Hadoop clusters to be expanded horizontally by adding more nodes to handle growing data volumes and processing requirements.
• Utilization of the cluster: With YARN, clusters may be used more efficiently by executing a variety of workloads concurrently, including MapReduce, Apache Spark, Apache Hive, Apache HBase, and others.
• Flexibility: YARN supports a number of different processing engines, which makes it appropriate for a variety of data processing jobs and workloads outside of MapReduce, such as real-time processing and interactive queries.
• Fault Tolerance: YARN keeps track of cluster node health and looks for malfunctions. To maintain data processing, it automatically restarts crashed programs or containers on the working nodes.
• Compatibility: Backward compatibility with MapReduce applications is maintained by YARN. Applications already written for MapReduce can run on YARN without needing to be modified.

Hadoop Distributed File System

a. Name Node

As the master node of HDFS, NameNode is responsible for overseeing the metadata and data distribution of the file system throughout the cluster.

Key responsibilities include:

• Manages metadata and file system namespace.
• Keeps track of data block locations.
• Coordinates data distribution and replication.
• Single point of failure; fault tolerance through HA (High Availability) and Federation mechanisms.

b. Data Node

DataNodes are the worker nodes of HDFS and are responsible for storing and managing the actual data in HDFS. Key characteristics include:

• Stores data blocks and replicates them for fault tolerance.
• Sends heartbeats to the NameNode for health status.
• Performs read and write operations for data blocks.
• Rack-aware for improved data availability.
• Allows horizontal scalability by adding more DataNodes.

c. Job Tracker

• Prior to Hadoop 2.0, the JobTracker served as the main organizing body for the MapReduce processing system.
• MapReduce jobs sent to the Hadoop cluster were managed and scheduled by it.
• Accountable for assigning tasks to TaskTracker nodes (worker nodes) and keeping track of their development.
• By reassigning tasks in the event of TaskTracker errors, fault tolerance was ensured.
• The ResourceManager and ApplicationMaster components of YARN, which offer more effective and scalable resource management and task scheduling for data processing in Hadoop clusters, have superseded the JobTracker's functionality in Hadoop 2.0 and later versions.

d. Task Tracker

• In the Hadoop MapReduce system, the TaskTracker was a worker node component.
• It operated on every node in the Hadoop cluster and carried out the Map and Reduce operations that the JobTracker had delegated to it.
• TaskTrackers were in constant communication with the JobTracker, sending heartbeat messages to indicate their availability and progress.
• In addition, the TaskTracker had to inform the JobTracker of changes to a task's status, such as completion or failure.
• In order to reduce data transfer across the network, it managed the local execution of jobs and ensured data locality by running tasks on nodes where the data was stored.
• It's crucial to remember that the MapReduce framework was updated with Hadoop 2.0 to use YARN for resource management and task scheduling. As a result, in Hadoop 2.0 and later versions, the TaskTracker's job was given to NodeManager.

How Hadoop Improves on Traditional Databases?

Hadoop improves on traditional databases in several ways, making it a powerful solution for handling large-scale data processing and storage challenges. Here are the key ways in which Hadoop surpasses traditional databases:

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