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What is Hadoop?

Apache Hadoop is a free and open-source software framework for managing data processing and storage in large-scale applications written in the Java programming language. The platform does this by partitioning Hadoop’s large data processing and analytics tasks into smaller workloads that can be handled in parallel on the various nodes of a computer cluster.

History of Hadoop:

Apache Hadoop was created when companies like Yahoo and Google were just getting their start and they needed a way to handle ever-increasing amounts of huge data and provide online results more quickly.

Hadoop was created by Doug Cutting and Mike Cafarella in 2002 when they were working on the Apache Nutch project. They were inspired by Google’s MapReduce, a programming technique that splits an application into tiny pieces to execute on separate nodes. An elephant toy that Doug’s kid had inspired the term Hadoop, according to a New York Times story.

Hadoop was initially based on Nutch, however it was eventually split off. While Hadoop took over the data processing and distributed computing duties, Nutch concentrated on web crawling. In 2008, Yahoo published Hadoop as an open-source project. Apache Hadoop was released to the public in November 2012 by the Apache Software Foundation (ASF).

Components of Hadoop:

Apache Hadoop is a platform for storing and managing Big Data that relies on distributed storage and parallel computing. It is the most used tool for managing massive amounts of data. Hadoop is made up of three parts.

  1. Hadoop Distributed File System – HDFS: Hadoop’s data is stored in the Hadoop Distributed File System.
  2. MapReduce or Hadoop: Hadoop’s central processing unit is called MapReduce, or Hadoop.
  3. Hadoop YARN, or Hadoop Yarn: Hadoop YARN is a component of Hadoop that handles resource management.

How significant is Hadoop’s effect?

The introduction of Hadoop was a watershed moment for the big data industry. Of a large extent, it is considered the precursor to today’s cloud-based data lakes. Hadoop has levelled the playing field when it comes to computing resources, allowing businesses to analyze and query massive data sets on a massive scale with nothing more than open-source software and commodity hardware.

This was a major shift since it provided an option to the dominant proprietary data warehouse (DW) systems and closed data formats.

Apache Hadoop’s rapid adoption has allowed businesses to more easily store and handle massive volumes of data, as well as benefit from improved computational power, fault tolerance, flexibility in data management, decreased costs compared to DWs, and more scalability. Hadoop ultimately set the path for further advances in big data analytics, such as the launch of Apache Spark.

What is Hadoop Programming?

Although Java is the language of choice for Hadoop MapReduce, users may build map and reduce functions in whatever language they wish with the help of a module such as Hadoop streaming. Hadoop’s framework is mostly C-based with some Java-based native code. In addition, shell scripts are often used to create command-line tools.

What is Hadoop Database?

Data storage and relational databases are not problems that can be solved using Hadoop. Instead, it is an open-source framework’s intended use to handle massive volumes of data in parallel and in near-real time.

The HDFS serves as a repository for information, but it is not a relational database because of its lack of structure. Actually, Hadoop supports storing data in any of these three formats: unstructured, semi-structured, and structured. Consequently, businesses have more leeway to analyse big data in ways that serve their own purposes.

Uses of Hadoop:

  1. Retail: Hadoop is used by retailers to boost their sales. Hadoop also assisted in keeping track of the things purchased by consumers. Hadoop also assists merchants in predicting the product price range. Hadoop also assists merchants with e-commerce development. These Hadoop benefits are very beneficial to the retail business.
  2. Finance: Hadoop is used to identify financial industry fraud. Credit card firms mostly use it to identify specific users for their products. Hadoop is also utilized for fraud pattern analysis.
  3. Health Care: Hadoop is used for the analysis of large datasets, such as those generated by medical equipment, clinical data, medical reports, etc. Hadoop performs in-depth analyses and scans of the reports in order to lessen the amount of time spent on human labour.
  4. Security and Law Enforcement: Hadoop is used by the United States National Security Agency to thwart terrorist plots. Law enforcement agencies utilize data analysis techniques to track down offenders and anticipate their next moves. Hadoop is utilized in the military, in cyber security, etc.
  5. Advertisements: Hadoop is employed by the advertising industry as well. Hadoop is used in video capture, financial data analysis, and social media management. Social media sites like Facebook, Instagram, etc. are the source of the analyzed data. Product marketing also makes use of Hadoop.

Hadoop’s benefits extend well beyond the realm of the software industry.

Importance of Hadoop:

Data analysts may benefit from using Hadoop as a useful tool. Hadoop’s numerous useful features are a large part of what contributes to the software’s widespread applicability and popularity.

  • The system stores and processes massive amounts of data quickly and concurrently. Data organization distinguishes semi-structured, organized, and unstructured collections.
  • By providing real-time analytics, an improvement may be made to operational decision-making as well as batch workloads for historical analysis.
  • Organizations may save data, and, if necessary, processors can apply filters to it so that it can be used for specialized analytical purposes.
  • Because Hadoop is scalable, organizations will have the ability to collect more data because they will be able to add a huge number of nodes to it.
  • A method protects applications and data processing against hardware failures. Traffic is immediately rerouted to other nodes, allowing applications to continue running.

Benefits of Hadoop:

Hadoop’s scalability, robustness, and adaptability are three of its most notable features along with the below mentioned benefits:

  • Fast: In HDFS, data are mapped and dispersed throughout the cluster, allowing for speedier retrieval. It can handle terabytes of information in minutes and petabytes in hours. Even the tools used to handle the data are often located on the same servers, saving processing time.
  • Diversity: The Hadoop Distributed File System (HDFS) is capable of storing a variety of data forms, including structured, semi-structured, and unstructured data.
  • Cost-Effective: Hadoop is a platform for handling data that is available for free.
  • Resilient: The data that is stored in one node is duplicated in the other nodes of the cluster to ensure that there is no downtime.
  • Scalable: Because Hadoop operates in a distributed setting, it is simple to include more servers into the system.

Challenges of Hadoop:

The greatness of Hadoop is not without its drawbacks, however. There are a few challenges with Hadoop, such as:

  • The learning curve is really high. Hadoop’s file system requires non-intuitive MapReduce routines written in Java if you wish to conduct a query. In addition, there are many parts to the ecosystem as a whole.
  • To apply a universal method to all datasets would be absurd. There is no “one size fits all” benefit while using Hadoop. It takes expertise to filter through the myriad of components, each of which operates things in its own unique way.
  • The capabilities of MapReduce are restricted. Though a fantastic programming paradigm overall, MapReduce’s file-intensive methodology makes it less than optimal for asynchronous interactive iterative tasks like data analytics.
  • Safety is a concern with Hadoop. Hadoop is missing essential security features like strong authentication, encrypted data, careful provisioning, and regular audits. Much of the information currently available is very confidential.

List of Top Companies Hadoop Users:

Hadoop is used by many companies globally. Here is the list of well-known companies using this big data tool:

  1. Adobe
  2. Netflix
  3. Uber
  4. Twitter
  5. eBay
  6. Facebook
  7. LinkedIn
  8. British Airways
  9. Expedia
  10. The National Security Agency (NSA) of the United States, and many more.


Companies may now improve their decision-making abilities thanks to the information they can get from big data analytics. Hadoop is a popular choice for storing, processing, and analyzing Big Data because of its versatility. After reading this article, you should fully grasp Hadoop’s basic concepts.

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