What is Hadoop?

Problems Leading to Evolution of Hadoop

Handling the heterogeneity of data i.e., semi-structured, structured, and unstructured, has been the main issue for analysis of data. RDBMS mainly focuses on structured data like banking transactions, operational data, and more, whereas Hadoop specializes in semi-structured or unstructured data like audios, videos, text, Facebook posts, and more. RDBMS technology is proven, highly consistent, mature, and many organizations support it. Hadoop is high in demand because of Big data, which mostly consists of unstructured data in different formats.

Enlisted are the problems with the Big data that lead to the evolution of the Hadoop:

1. Exponentially Large Data Sets: Earlier, storing a colossal amount of data was not feasible as the storage was limited to only one system, and data rose at a tremendous rate.
2. Storing of Heterogeneous Data: The data is not only enormous but is also available in various formats: semi-structured, structured, and unstructured. Therefore, the available system should store varieties of data generated from multiple sources.
3. Speed: Accessing and processing speed is a bigger problem than Big data as hard disk capacity is increasing, but the data transfer speed is not increasing at a similar rate.

Scientists Mike Cafarella and Doug Cutting developed a platform called Hadoop 1.0 and launched it in 2006 to support the distribution of the Nutch search engine and also overcome the mentioned problems. They were inspired by Google’s MapReduce, which splits an application into small fractions to run on different nodes,

In November 2012, Apache Hadoop was made available for the public by Apache Software Foundation. The framework is managed by Apache Software Foundation and is licensed under the Apache License 2.0.

What is Hadoop?

Based on the Java framework, Hadoop is an open-source software used for processing and storing Big data. Hadoop allows the user to store Big Data in a distributed environment, so that, they can process it parallelly. Hadoop helps in making a better business decision by providing a history of data and various records of the company. So by using this technology company can improve its business. Hadoop does lots of processing over collected data from the company to deduce the result, which can help to make a future decision.

Components of Hadoop

1. HDFS

Hadoop Distributed File System allows the user to store data of various formats across the cluster. HDFS creates an abstraction i.e., HDFS, similar to virtualization, which is a single unit for storing Big Data, but data storage is across multiple nodes in a distributed fashion. HDFS follows a master-slave architecture. In HDFS, the Namenodes act as master, and Data Nodes act as slaves. The metadata about the data stored in Data nodes which contains within Namenodes, such as where are the replication of the data block kept, which data block is stored in which data node, and more. Data nodes store the actual data.

HDFS MASTER-SLAVE ARCHITECTURE

2. Yarn

Yarn performs all the processing activities by allocating resources and scheduling tasks. It consists of two components ResourceManager and NodeManager. The resource manager again acts as the master node; it receives the processing request. It then accordingly passes the parts of requests to corresponding NodeManagers, where the actual processing takes place. NodeManagers are installed on every DataNode. It is responsible for the execution of the task on every single DataNode.

YARN ARCHITECTURE

Other Components:

• Hadoop Common: Set of standard utilities and libraries which contain other Hadoop modules
• MapReduce: Executes tasks in a parallel fashion by distributing the data as small blocks
• Ambari: A web-based interface for managing, configuring, and testing Big Data clusters to support its components such as HDFS, MapReduce, and more. The interface provides a console for monitoring the health status of the clusters and also allows evaluates the performance of individual components such as MapReduce, Pig, Hive, and more in a user-friendly way.
• Cassandra: It is a highly scalable and open-source distributed database system based on NoSQL dedicated to handling a massive amount of data across multiple commodity servers, thereby contributing to high availability.
• Flume: Flume is a distributed and reliable service for collecting, aggregating, and moving the bulk of streaming data into centralized data stores.
• HBase: HBase is a non-relational distributed database that runs on Big Data Hadoop cluster, which stores large amounts of structured data.
• HCatalog: It is a layer of storage management and table, allowing developers to share and access data.
• Hive: Hive is a data warehouse platform built for providing data query and analysis.
• Oozie: Oozie is a server-based system that manages and schedules the Hadoop jobs.
• Pig: Pig is responsible for manipulating data stored in HDFS with the help of a compiler for MapReduce and a language called Pig Latin.
• Solr: A highly scalable search tool, that enables indexing, central configuration, failovers, and recovery.
• Spark: A fast open-source engine responsible for Hadoop streaming and supporting SQL, machine learning, and processing graphs.
• Sqoop: It is a mechanism to transfer vast amounts of data between Hadoop and structured databases.
• ZooKeeper: An open-source application, ZooKeeper configures and synchronizes the distributed system

The Ultimate Hands-On Hadoop: Tame your Big Data!

How Does Hadoop Serve as a Solution to Big Data Problems?

1. Storage of Big Data

HDFS stores Big data in a distributed system. The data is stored in blocks across the DataNodes, and the user specifies the size of the blocks. For instance, if the user has 512MB of data and he has configured HDFS to create 128 MB of data blocks. So data is divided by HDFS into four blocks i.e., 512/128=4 and stored across different DataNodes. It also replicates the data blocks on different DataNodes. Now, using commodity hardware solves the storage challenge.

The scaling problem is solved by focusing on horizontal scaling instead of vertical scaling. Some extra data nodes to the HDFS cluster can also be added by the user as and when required, instead of scaling up the resources of the DataNodes.

2. Storing Structured, Unstructured and Semi-Structured Data

HDFS allows the user to store all kinds of data, whether it is structured, semi-structured, or unstructured since, in HDFS, there is no pre-dumping schema validation. And it also follows "write once and read many" model. Due to this, you can write the data once, and you can read it multiple times for finding insights.

3. Processing and Accessing Data

One of the significant challenges with Big Data is processing and accessing data efficiently. Solving the problem involves moving processing to data and not data to processing. What does this mean? In MapReduce, the processing logic is sent to different slave nodes, and then the data is processed parallelly across different nodes. The processed results are then sent back to the master node, where the results are merged, and the response is sent back to the client.

Download and Installation

Hardware Requirements:

Hadoop is feasible to work on any ordinary hardware cluster. Commodity hardware is required to run the service.

OS Requirement

Hadoop is developed to run on Windows and UNIX platforms.

Browser Requirement

Hadoop supports mostly all popular browsers. These browsers include Google Chrome, Microsoft Internet Explorer, Mozilla Firefox, Safari for Windows, and Linux and Macintosh system, depending on the requirement.

Software Requirement

Hadoop service requires Java software as the Hadoop framework is written in Java programming language. The minimum version for Java required is the Java 1.6 version.

Database Requirement

Hive or HCatalog requires a MySQL database for successfully running the Hadoop framework.

Installation:

Discussed below are a few ways in which Hadoop can be installed.

1. Standalone Mode

We can install Hadoop on a single node in a standalone instance. Thus Hadoop platform works like a system that executes Java; this is mostly used for debugging processes. It also helps to check the MapReduce applications on a single node before running them on a massive cluster of Hadoop.

2. Fully Distributed Mode

Several nodes of commodity hardware are connected to this distributed node to form the Hadoop cluster. The NameNode, Secondary NameNode, and JobTracker all work on the master node, whereas the Secondary DataNode and the DataNode work on the slave node. TaskTracker works on the slave node, as well.

3. Pseudo-distributed Mode

A single-node Java system, pseudo-distributed node runs the entire Hadoop cluster. So, various daemons such as NameNode, DataNode, TaskTracker, and JobTracker runs on the single instance of the Java machine to form the distributed Hadoop cluster.

Features of Hadoop

1. Appropriate for Big Data

With a massive amount of data and with a variety of it being structured, semi-structured, and unstructured, there is an excellent possibility of a loss of data and failure of data. Hadoop clusters are best suited for this job, as they can process large and complex heterogeneous data.

2. Computational Ability

Hadoop offers distributed computational ability enabling fast processing of big data with multiple nodes running in parallel.

3. Fault Tolerance

The Hadoop ecosystem offers the service to replicate the input data on other cluster nodes, thus leading to a lesser number of failures. As in the scenario of a clustered node failure, the jobs are redirected automatically to other nodes; therefore, the system responds to the real-time without failures.

4. No Preprocessing Required

An enormous amount of structured or unstructured data retrieves at once without having to preprocess them before storing them into the database.

5. Scalability

The Hadoop tool is entirely scalable as it allows us to raise the cluster size from a single machine to thousands of servers without having to administer extensively.

6. Cost-effective

Since it is an open-source service, it is affordable.

Applications of Hadoop

1. Healthcare

Big data in healthcare enhances the quality of human life by preventing deaths by reducing cost, curing diseases, improving profits, and predicting epidemics. Big data analytics is leveraged by various medical institutions, research labs, and hospitals to reduce healthcare costs by changing the models of the line of treatment. Big Data in healthcare is a great concept due to the different data types and the pace at which healthcare data management needs management. Hadoop is successful in the healthcare industry to manage the massive healthcare data as the MapReduce engine, and HDFS is capable of processing thousands of terabytes of data. Hadoop makes use of cheap commodity hardware, making it a pocket-friendly investment for the healthcare industry.

2. Understanding Customer Requirement

Understanding the customer’s requirement is one of the significant areas which uses Hadoop. Many domains like telecom use this technology to find out the customer’s requirement by analyzing substantial amounts of data and extracting the information from the data. The technology is also used by social media; the service posts an advertisement on various social media sites repeatedly to the target customer. Credit card companies use Hadoop to find out the appropriate customer for their product; they contact the customers through various ways to sell their products. Many online booking sites track users of their previous surfing according to this information, and based on customer interest, provide flight suggestions to them. Many e-commerce companies also keep track of the product bought together. Based on this, they propose to customers to purchase other products when a customer tries to buy one of the relevant products from that group — for example, mobile back cover, screen guard suggestions on buying a smartphone.

3. Improvement of Cities and Countries

The development of towns and cities can be improved by analyzing the data using Hadoop, for example, traffic jams can be controlled by using Hadoop. Hadoop also serves its purposes for the development of smart cities and to improve transport by giving proper route guidelines for the buses, trains, and other vehicles.

4. Financial Trading

The trading field uses Hadoop. It consists of a complex algorithm that scans markets with predefined conditions to discover trading opportunities. It is designed to work without human interaction in case nobody is present to monitor. High-frequency trading also uses Hadoop. The algorithm takes most of the trading decisions.

5. Optimizing Business Processes

A business process requires Hadoop as it optimizes the performance of the company in various ways. It allows retailers to customize their stocks by the prediction that comes from multiple sources like social media, google search, and other platforms. Based on this company can make the best decision to improve their business and maximize their profits. Many companies use this technology to enhance their workspace by monitoring the behavior of their employees. Sensors can be embedded into the badge of an employee to observe their interaction with other people of the organization. This helps a lot in taking HR decisions in case of any issue between the employees.

6. Performance Optimization

Improving personal life by monitoring the sleep pattern, morning walk, and daily routine of healthy people also involves using Hadoop. Many dating sites also use Hadoop to find out people with common interests.

7. Optimizing Machine Performance

The mechanical field uses Hadoop on a large scale. It is used to develop self-driving cars by automation. By using GPS, cameras, and powerful sensors, a car can be driven without human interference. Uses of Hadoop are playing a massive role in this field, which is going to change the driving experience for good.

Summary

We can adopt Hadoop in our organizations as per our business requirements. In just a decade, Hadoop has marked its presence in a big way in the computing industry. It has made the possibility of data analytics real. It is widely used in analyzing site visits, fraud detection, banking applications, and the healthcare industry.