What is Hadoop and What is it Used For?

What is Hadoop?

So, what is Hadoop? It's an open source framework that's based on Java. It manages the storage as well as processing of gigantic data amounts for apps. It uses parallel processing and distributed storage for handling big data and analytics jobs. It breaks down workloads into smaller workload sections that can be run easily at the same time.

It handles different forms of structured, unstructured and semistructured data for more flexibility during collection, management and analysis of data. This ability around processing and storing different data types makes it highly popular for big data environments. They involve only huge data amounts. It mixes internet clickstream records, transaction data, web server and mobile application logs, customer emails, social media posts, sensor data from IoT, etc.

What is Hadoop Used For?

Another important question in addition to 'what is Hadoop' is 'what is Hadoop used for'. There are many things it accomplishes and this list tracks down a few of them-

• Analytics and Big Data - It's a great framework for production data processing, researching and analytics that need processing terabytes or even petabytes of big data. It also stores different datasets and data parallel processing.

• Data Lakes- It complements data lakes since it stores data without any preprocessing. This is where gigantic quantities of unrefined data are stored.

• Data Storage and Archiving- It's useful for mass storage on commodity hardware as a low-cost storage option for different data kinds. This includes click streams, sensor and machine data or transactions.

• Risk Management- Insurance companies, banks and many other financial services companies use it for building risk analysis and management models.

• Marketing Analytics- Marketing departments use it for storing and analyzing CRM (customer relationship management) data.

• AI and Machine Learning- Hadoop ecosystems process data and model training operations for Machine Learning apps.

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How Does Hadoop Work for Big Data Management and Analytics?

Finding answers to key questions is very important. One of these is - how does Hadoop work for big data management and analytics. There are a lot of aspects to this working and this section discusses it.

It works on commodity servers and scales up to support a multitude of hardware nodes. Its file system is designed in such a way that rapid data access is provided across the nodes in a cluster. It also has many fault-tolerant capabilities so apps can run continuously even if any individual nodes fail. These features led to Hadoop becoming an important data management platform for big data analytics.

It processes and stores many data types and even sets up data lakes in the form of expansive reservoirs for continued streams of information. Raw data is generally stored as it is in a Hadoop data lake. Data scientists and other analysts then access complete data sets when needed. Data is filtered and prepared by data management or analytics teams to support different apps.

Data lakes serve different purposes. Big data analytics has become more important in any business' decision making process. It has made effective data security processes and data governance a priority in Hadoop deployments. This framework is useful in data lake houses.

Components of Hadoop

There are several components of Hadoop that must be studied. The four key components are discussed here to make it more feasible to get started towards learning this framework better.

• Hadoop Distributed File System (HDFS)

It's a critical component of the architecture. HDFS is the main storage system and is designed for scaling to petabytes of data and even runs on commodity hardware. It maintains humongous data sets across different nodes in a distributed computing environment.

It operates on the main principle of storing huge files across different machines. It segregates huge data into smaller blocks for high output and then manages them through distinct nodes in the network.

• Yet Another Resource Negotiator (YARN)

YARN manages resources in the scheduling and cluster tasks for users. It is considered to be a key element in its architecture. Multiple data processing engines like graph processing, batch processing and interactive processing can handle data stored in this component.

YARN segregates the functionalities of job scheduling and resource management into distinct daemons. This design makes space for a scalable and flexible architecture to accommodate many more processing approaches and applications.

• MapReduce Programming Model

MapReduce is an integral programming model in its architecture. It's designed for processing huge data volumes in parallel by segregating the work into different sets of independent tasks. This model simplifies vast data set processing to make it an indispensable part of this framework.

This programming model is characterized by two main tasks - Map and Reduce. The former one takes a set of data to convert it into a different set of data where every individual element is parted into tuples. The latter one takes the Map' output as its input and mixes those tuples to form a smaller set of tuples.

• Hadoop Common

Hadoop Common is also more commonly called the glue that keeps the entire architecture together. It consists of utilities and libraries that are needed by other modules here. It contains all the important Java scripts and files needed to begin Hadoop. This component works to make sure hardware failures are managed by the framework itself for a high degree of reliability and resilience.

Related Article- What Are The Benefits of Splunk Analytics For Hadoop?

5 Advantages of Hadoop for Big Data

The benefits of using this framework are plenty but there are the 5 advantages of Hadoop for Big Data that are a must know.

• High Scalability- Many nodes can be added to improve the performance dramatically.

• Economic- It runs on a group of commodity hardware that is not considered very expensive.

• Resilience- It allows for system resilience and fault tolerance. This means that jobs get redirected to another node if a hardware node fails.

• Flexibility- Data storage is flexible as data doesn't need any preprocessing before being stored. As much data as is needed can be stored and then used later.

• High Support- It supports real-time analytics apps for better operational decision making.

Related Article - Hadoop Tutorial For Beginners

Wrap-Up

Hadoop makes it simpler to use the processing and storage capacity in cluster servers. It also executes distributed processes against gigantic quantities of data. Its building blocks make the basis for building services and applications. This blog covers 'what is Hadoop' along with its components, advantages, uses and working for in-depth understanding of this framework.

FAQs

Q1. Is Hadoop a software or hardware?

It's an open source software framework. It stores data and runs apps on commodity hardware clusters.

Q2. Which language is used in this tool?

The framework is written mostly in Java programming language. Some native code is also written in C as well as command line utilities in shell scripts.

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