What Is Hadoop?

Corporations across the US have increased their use and analysis of big data to make effective decisions impacting their businesses. These companies have turned to generating more data to produce key insights to enhance their operational efficiency. 

Big data represents large quantities of varied information used for analysis. These data sets come in structured or unstructured forms and can expand at an increasing pace or velocity. There is a divide between the demand for skilled professionals capable of handling big data tasks and the supply of qualified workers in the marketplace today. Building your skills in related technologies is vital to taking advantage of potential job opportunities and landing a role as a professional in big data. 

Hadoop is popular and widely used for big data purposes today. As an open-source software managed by the Apache Software Foundation, Hadoop provides a framework and suite of technologies capable of handling many jobs related to data storage and data processing. 

Before building your skills and pursuing a career that may rely on Hadoop, explore a bit about the history of Hadoop, its various uses in diverse industries, its associated pros and cons, and how you can begin your journey of learning Hadoop today.

What is Hadoop?

Hadoop is an open-source, trustworthy software framework that allows you to efficiently process mass quantities of information or data in a scalable fashion. As a platform, Hadoop promotes fast processing and complete management of data storage tailored for big data solutions. It achieves these impressive computation results by splitting the workload between multiple nodes in a cluster that performs tasks at the same time.

A key feature of Hadoop is its ability to scale quickly by adding computing nodes in a cluster of commodity hardware to increase processing power. Instead of performing tasks on one computer, Hadoop links many computers together in a cluster that can compute and store data. The programming in Hadoop runs on the Java programming language.

The development of Hadoop occurred to manage the explosive growth of the web. As the number of web pages reached billions, companies began building open-source frameworks to tackle the difficult task of handling searches and providing accurate results. New technology companies at the time, such as Google and Yahoo, worked to create and develop frameworks capable of automating web searches. Hadoop is one open-source framework created during this time at the Apache Software Foundation. In 2008, Yahoo officially open-sourced Hadoop [1].

Hadoop modules 

Four key modules in Hadoop enable the software to process large quantities of data and perform efficiently despite various challenges and obstacles. The four modules are:

• Hadoop Distributed File System (HDFS):  The HDFS holds your data, similar to a local file on your desktop, and enables Hadoop to have storage capabilities utilizing commodity hardware. Other key characteristics of the HDFS are its ability to handle large quantities of data, its high tolerance for errors or failures, and its scalability features. 

• Yet another resource negotiator (YARN): As a resource negotiator, this module schedules jobs and monitors the overall usage of resources in the system. YARN also works to allocate resources efficiently by properly managing cluster resources.

• MapReduce: The MapReduce module in Hadoop enables the system's parallel processing component. MapReduce breaks down your data set into smaller sub-parts and provides each node with a task for processing. 

• Hadoop Common: Often referred to as Hadoop Core, this module contains the basic libraries, commonly in the Java programming language, and tools frequently used to support the other modules. 

What is Hadoop used for?

Hadoop is relevant in a wide variety of situations today related to processing and storing data. These applications are far-reaching and represent the power of Hadoop beyond its original purpose of handling searches and providing accurate results. Three of the primary tasks you can complete using Hadoop include big data processing, parallel processing, and diverse data storage.

Big data processing

As mentioned, Hadoop can handle large data sets, sometimes reaching the size of petabytes. It accomplishes this by scaling its computing cluster and incorporating many nodes. This gives Hadoop the required amount of processing power to handle big data sets.

Parallel processing

As covered in the description of MapReduce, Hadoop can complete multiple tasks or jobs in parallel. It breaks your data into smaller pieces and divides the workload between various nodes.

Diverse data storage

Hadoop performs with many types of data, whether text, images, or another form. This ability also means it stores structured or unstructured data effectively, giving you maximum flexibility in deciding how you use Hadoop.

Who uses Hadoop?

The benefits of Hadoop as an open-source, low-cost software have caught the attention of many companies in various industries dealing with data analytics and big data challenges.  Any company tasked with processing mass quantities of data simultaneously leverages Hadoop for its big data capabilities. Uses include parallel processing of data, data storage features, handling preprocessed datasets alongside data lakes, and even researching functions. Types of industries utilizing Hadoop to tackle big data and analytics challenges include:

• Banking and finance: These companies deploy Hadoop to build various risk and management analysis models. 

• Insurance: Insurance companies may use Hadoop for risk management and model creation, similar to those in the banking and finance industry. 

• Marketing: Marketing teams focus on handling and performing analyses of customer relationship management information and can use Hadoop to complete this task efficiently.

• AI and machine learning: Due to the large amount of data required to train and develop effective machine learning algorithms, companies focused on this sector leverage Hadoop to process mass quantities of information to build advanced models and other applications. 

• Public cloud: Public cloud providers, such as Google Cloud and Amazon Web Services, utilize Hadoop to build big data tools and applications for their customers on the cloud. 

Pros and cons of using Hadoop

Hadoop has many benefits due to its impressive processing abilities, storage capabilities, and overall effectiveness for big data problems. However, a list of cons have developed as Hadoop has grown older and new applications have been released. 

Pros

You can see the importance of Hadoop through its various advantages, including:

• Scalability: Hadoop enables scalability through its distributed model by allowing you to increase the number of nodes you use to process data.

• Flexibility: Hadoop eliminates the requirement to preprocess your data as a precursor to placing it in data storage. This flexibility allows you to store and use your data based on your circumstances when needed.

• Computing power: Hadoop enables you to handle and process large quantities of data quickly by allowing you to increase the number of nodes in use at a given time.

• Fault tolerance: If an error occurs within your hardware, such as a specific node failing, Hadoop automatically sends the tasks handled by that node to a different node to ensure the completion of all processing. Additionally, Hadoop automates saving additional versions of your data in multiple places for you.

• Low cost: Hadoop is a low-cost software capable of handling big data applications because it uses commodity hardware and its open-source nature. 

Cons

The current challenges and cons of Hadoop include:

• Complexity: You may face complications with MapReduce while trying to tackle difficult tasks because it requires coding in Java. You may also find it challenging to determine what tools to deploy for your jobs because MapReduce features many different tools and functions as part of its expansive ecosystem. 

• Data management and data governance limitations: Many aspects of data management and governance need improvement within Hadoop, including data cleansing, metadata management, and specific tools geared toward ensuring the quality and standardization of data. 

• Security concerns: While Hadoop does have various tools configured to aid with data authentication, auditing, encryption, and other security-related tasks, data protection remains a concern because of the mass quantities of data processed in Hadoop.

• Lack of qualified professionals: A talent gap exists in the industry where companies face challenges finding qualified programmers with the proper technical skills in Java to handle MapReduce for entry-level roles. 

How to start learning Hadoop

To progress your professional career in data and analytics, learning Hadoop and building specialized skills in this big data technology can boost your chances of landing a desired, high-level job and increase your expected earnings. Before you begin learning specific Hadoop concepts, it can help you identify the related skills you can develop.

Applicable general skills include knowledge of the Linux operating system, programming skills in Java or Python, and experience with SQL. While these skill sets may not be necessary to master before learning Hadoop, they can provide a foundational introduction to Hadoop-related technical skills. 

Progressing beyond general technical skills, essential steps to take to learn Hadoop include:

• Understand Hadoop basics: Begin by learning the fundamental concepts behind the Hadoop software and understanding how its framework can enable you to solve complex problems. These key concepts include the main modules in Hadoop, such as HDFS, YARN, and MapReduce.

• Experiment with Hadoop: Once you build foundational knowledge, downloading and experimenting with Hadoop is the next step to increase your confidence and learn critical skills. Since Hadoop is open-source, you can access and interact with training resources to begin your journey from the very start.

• Familiarize yourself with technology updates: Staying up-to-date on new Hadoop updates allows you to ensure that you are learning relevant topics and following best practices. Various resources such as blogs, online forums, and the Apache Hadoop website are excellent references for following current Hadoop and related technology trends. 

• Complete a Hadoop course: Courses in Hadoop can aid your learning experience if you are a complete beginner or have some prior experience and expose you to a wide variety of Hadoop topics.

• Gain a Hadoop certification: Completing a Hadoop certification helps you hone your skills and demonstrably highlight them for clients and potential employers. Many online sources and companies offer Hadoop certification programs. 

You may also deepen your knowledge with an online Professional Certificate, a credential that can help strengthen your resume. Some excellent Hadoop-related courses are on Coursera, such as the IBM Data Engineering Professional Certificate.

Getting started on Coursera

Learn more about Hadoop and other tools utilized in big data by completing a course or receiving a relevant certificate on Coursera. Consider checking out Introduction to Big Data by UC San Diego. This course covers various topics across the big data landscape, including the characteristics of big data, dimensions of scalability, programming, big data systems, and how to use Hadoop. 

Another relevant course worth checking out is the IBM Data Engineering Professional Certificate. Taught at a beginner level, this course focuses on teaching practical skills used by data engineers today. Topics include building and managing relational databases, implementing data pipelines, creating interactive dashboards, and gaining experience with MongoDB and Hadoop.