The Internet has seeped into our personal, social, as well as professional lives so profoundly that we hardly spend even an hour without it. This way, a tremendous amount of information is shared and uploaded over the Internet. Traditional storage systems such as RDBMS and SQL are not competent in handling even the chunks of this amount of information. This enlarged the breakdown time with every new user login.
The data shared is unstructured, very high in volume, and so complex that it is really difficult to handle. Moreover, traditional systems are not capable of capturing, curating, analyzing, sharing, securing, visualizing, and storing this voluminous data.
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It was here that the concept of Hadoop was conceived, which stands for High-Availability Distributed Object-Oriented Platform.
This article will let you go through an overview of Hadoop and the reasons for taking the Hadoop course in 2022.
What Is Hadoop?
Apache Hadoop is an open-source framework that allows for distributed processing of massive data sets across a bundle of computers making use of simple programming models.
It is simply a database system that utilizes every node, cluster, or system connected to a network and considers it as a server itself. This way, the processing is distributed, and the system becomes more efficient. The capability of identifying and repairing failures is inherently built into this system, making it reliable and secure against expected downtime.
There are four modules in a Hadoop ecosystem. They are:
1. Hadoop Common
It consists of the common utilities and libraries to support other modules of Hadoop.
2. HDFS Or Hadoop Distributed File System
The core of Hadoop, HDFS is a fault-tolerant, distributed, and auto-replicating file system that is capable of storing data in the form of clusters and also provides easy and quick access to the stored data.
3. Hadoop YARN
It is a processing layer that looks after job scheduling and resource management and also handles a variety of processing requirements.
4. Hadoop MapReduce
MapReduce is considered the ‘heart of Hadoop’ by IBM. Hadoop MapReduce is a batch-oriented programming model that enables the system to process huge data sets across a cluster of machines.
The data is processed in two phases, namely Mapping and Reducing (hence the name MapReduce). In the Maooing phase, the data is divided into small chunks and is spread across the cluster by using a Mapper function. Then the Reducing phase, with the help of a Reducer function, aggregates the data.
Advantages Of Using Apache Hadoop
Big Data is all around us, generated by us. Hadoop is typically a Big Data processing paradigm that is capable of handling this massive amount of data. Some of the special features of Hadoop include distributed storage, parallel processing, scalability, fault tolerance, and more. These features are discussed below:
Scalable
Hadoop is highly scalable as it works on clusters of machines or nodes. It allows you to add the nodes without any downtime. It allows for both horizontal (adding new nodes to the cluster) and vertical(increasing RAM or hard disk) scaling.
This way, businesses can process petabytes of data stored in the HDFS.
Open-Source
The source code of Hadoop is available free of cost, even the proprietary versions such as Horton and Cloudera also.
Flexible
Hadoop allows for easy and quick access to various data types and data sources.
High Throughput And Low Latency
The amount of work done per unit time is referred to as throughput. Low latency refers to the processing of data without or with minimal delay. With the concept of distributed storage of data and parallel processing, Hadoop achieves high throughput along with low latency.
Adaptability
Apache Hadoop is mostly developed in Java, but it is known for its excellent support for different coding languages such as Python, Java, Groovy, C++, and more.
Cost-Effective
A Hadoop cluster can be built with common commodity hardware, eliminating hardware costs. Moreover, the data management expenses are also minimal, making Hadoop a cost-effective solution for the storage and processing of massive amounts of data.
Is It Worth Learning Apache Hadoop In 2022?
Definitely!!
Let’s look at the top reasons that make Hadoop worth learning.
1. Big Data And Hadoop Are Suitable For Professionals From Different Backgrounds
The tools and infrastructure of the Hadoop ecosystem can be utilized by professionals from varying backgrounds. The exponential growth of Big Data analytics continues to open up opportunities for professionals with a background in analytics as well as IT.
The professionals who can work with Hadoop and benefit from this growth include:
- Hadoop Engineers
- Hadoop Tester
- Database Administrators
- Business Analysts
- Software Architects
- Software Developers
- Data Warehousing Professionals
For instance, you can use Apache Hive to execute queries with SQL as an analyst or a data scientist. Moreover, you can write MapReduce code and create scripts using Apache Pig as a programmer.
2. Hadoop Professionals Are Highly In Demand And Earn Huge Salaries
Apache Hadoop is known for its speed, scalability, adaptability, and cost-effectiveness. The Hadoop ecosystem and the suite of packages, including Spark, Hive, Kafka, and Pig, are leveraged in numerous use cases across many sectors, making Hadoop a prominent tool.
Some of the most in-demand and highest-paying skills in the field of analytics and data science include Apache Hadoop, Apache Hive, Pig, and MapReduce, according to a report by Burning Glass Technologies and IBM. These skills improve your earning capability and opportunities for career advancements as well.
The average annual salary of a Hadoop Developer in India is around INR 6 lakhs and may go as high as INR 28 lakhs and even more. The demand for skilled and trained Hadoop Developers is consistently driven by the lack of professionals with relevant skills.
3. Hadoop Is On A Consistent Growth Path
With the growing landscape of Big Data, a majority of well-established companies have adopted Hadoop technologies to handle Big Data analytics. Since the Hadoop ecosystem consists of various technologies that are required for building strong strategies regarding Big Data.
The data from Google Trends states that Big Data and Hadoop are growing with almost the same growth patterns. This means that Hadoop is extensively adopted by companies for making smarter data-driven decisions.
Conclusion
That said, you might have realized the importance of learning Hadoop technologies as they may provide you with massive growth opportunities and also increase your earning potential.
To learn how to use Hadoop tools and techniques to come up with impressive data-driven solutions that can impact business strategies, you can take up an online training course. An online training course helps you acquire the required knowledge and skills and also makes you go hands-on with all the techniques involved in the Hadoop ecosystem.
Enroll Yourself Now!
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