There are many questions when you hear the word Hadoop for the first time. It is used by many international companies like Amazon Web Services, Facebook, ScienceSoft, Yahoo, Hortonworks, IBM, Microsoft, Hadapt, Datameer. These are the top 10 companies that are using Hadoop for managing their database.
Let’s start understanding what Hadoop is. We will first understand the issues we face with Big Data and the traditional processing system. We discuss all the essential points like its use, How it is used to manage Big Data, and how much a person can earn if they know how to use and collect the Big data using Hadoop.
In this article, we are going to talk about the most common questions which the bingers ask. We will discuss the problems with the Traditional Approach, Evolution of Hadoop, Hadoop as an answer for the issues of Big Data. When should we use Hadoop and when not to use it?. What will be the salary for the Big Data Analyzer?
What is the problems with Traditional Approach?
If we talk about the company, Nowadays they have so much data to store, and at the same time, they want their data to be accessible quickly. They can’t rely on the old ways because they want their data store and accessed quickly.
Many companies are using the RDBMS to store their data. But the RDBMS is only capable of structured data that are use full for the bank transaction, operational data, and many more. RDBMS is proper to store the data, but it doesn’t have a few other facilities which nowadays companies need, like structured, semi-structured, and unstructured.
Now, if we talk about Hadoop, this helps us in semi-structured and unstructured data like videos, text, images, logos, and audio. Still, some companies prefer RDBMS to store their data. But on the other hand, Hadoop is getting in demand due to the semi-structured and unstructured formats of Big Data.
What is the problem with Evolution of Hadoop?
All this started in the year 2003 when Doug Cutting decided to start a project Nutch. This project was build to handle billions of searches and index web pages. In October 2003, Google also launched GFS, which is known as Google File System. Then in December 2004, google also found MapReduce, both of this software was then used in the Nutch project to operate in the year 2015.
Then in the year 2006, Yahoo created and launched the Hadoop in which the GFS and MapReduce both were used, and Doug Cutting made it. In the year 2007, Yahoo started using Hadoop for more than 1000 node clusters.
Then Yahoo released Hadoop as an open sources project from Apache Software Foundation in the year 2008. After the Apache foundation received it, they tested 4000 node clusters successfully. Then after it was renamed Apache Hadoop. The first version of Hadoop was released in December 2011, version 1.0, and the recent version is 2.10.1, which was released on 21 September 2020.
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What is the use of Hadoop?
Let us know what Hadoop used for is? The first thing is that it is a framework that helps us store Big Data in a distributed environment to process it parallelly. There are two essential and fundamental components in Hadoop:
- HDFS:- The HDFS stands for Hadoop Distributed File System. This helps us to store the data in different formats all over the cluster.
- YARN:- The YARN is used to manage Hadoop, it helps the parallel processing over the data, which is stored across the HDFS.
An answer for the problems of Big Data?
Let us see the solution to the problem of Big Data with the help of Hadoop.
The first problem is storing the Big Data?
HDFS helps in a distributed way of storing Big Data. The data is stored in blocks all across the DataNodes, and we can also assign the size of the blocks. HDFS has also helped in the scaling problem, and the HDFS focuses on horizontal scaling instead of vertical scaling. It always helps us to add some more data nodes to an HDFS cluster whenever it is required.
The second problem is storing a variety of data.
With the help of HDFS, we can store all kinds of data like structured, semi-structured, or unstructured.
The third problem was how to access and process the data fast.
It is a huge problem when it comes to access and process the data fast. To solve this problem, the HDFS has a different way of processing. Usually, the data is sent to processing, which takes time, but in HDFS, the processing is sent to information, reducing processing time.
When should we use Hadoop and when not to use?
Now the question comes where to use Hadoop and where not to use it. It can be utilized in a search engine like Yahoo, Amazon, or you can use it in Log Processing, then Data Warehouse, and many more.
It can’t be utilized in Low Latency Data Access, Multiple Data Modification, and many small files.
What will be the salary for the Big Data Analyzer Hadoop?
The salary of a Hadoop Analyzer in India depends typically on the qualification and skills of the candidate. Well, the postgraduate student can earn a package of around 4 to 8 LAP. As you grow in this field, you can also reach approximately 12 to 18 LAP in just a few years.
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