The Big Data Hadoop industry is accelerating at a high pace, and its future seems to be brighter than what it is today. There is also an increased demand for big data hadoop training courses in the IT industry, as more and more professionals are willing to enhance their skills. In this article, we’ll tell you about the essential things you should know about Apache Hadoop.
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- Things You Need to Know About Apache Hadoop
Apache Hadoop has been a crucial pillar for the growth of the big data industry. Its ability to store and process data (large data sets) at a cheap cost is the reason behind its popularity. You’ll hear it combined with technologies like Spark, Pig, and Hive. What’s the logic behind its fancy name, and what are the things that we should know? Let’s take a look:
Things You Need to Know About Apache Hadoop
How was the name ‘Hadoop’ given?
Hadoop was created in 2005 by Mike Cafarella and Doug Cutting. The story behind the origin of the name ‘Hadoop’ is rather cute. Doug’s son had a toy elephant when he was just 2. The child had just begun to talk and called his toy elephant ‘Hadoop.’ This is how Doug named his framework ‘Hadoop.’
What is Apache Hadoop?
Hadoop is an open-source framework that empowers users to easily store vast volumes of data sets (size can be 10-100 GBs or even more) and process it simultaneously. Big corporations like Yahoo, Facebook, Twitter, LinkedIn, and the like use Hadoop for data storage as it provides quicker storage for large data sets. It can also be scaled up as the requirement increases; all you need to do is add more nodes.
Benefits of Apache Hadoop
Data processing and storage
Apache Hadoop has the ability to store and process vast amounts of data from multiple sources. As unstructured data from social media, smart devices, pictures, and the like is increasing, Hadoop is the best way to draw meaningful insights from it.
Hadoop’s computing power depends upon the number of nodes being used. If you need more computing power, add more nodes, and you’ll have what you desire.
Apache Hadoop offers cheap data storage and processing. In addition to this, the open-source framework is free for everyone.
Traditional databases required the user to pre-process data before storing it. With Hadoop, there’s no such need. The user can save any amount of structured and unstructured data.
What happens when one node fails? The tasks do not get hampered and are transferred onto other nodes of the network.
Components of Apache Hadoop
It is a standard module that contains all the necessary files for other Hadoop components.
Hadoop Distributed File System (HDFS)
It is a system written in Java designed to store and access data that is very large in size (gigabytes or terabytes). The data does not require RAID storage, and the reliability lies in the fact that the information is stored across multiple hosts. The default replication value is 3 – meaning that the data is stored across 3 nodes: 2 on the same rack, and one on a different.
YARN is a component of Apache Hadoop and was introduced in Hadoop 2.0. Hadoop 2.0 segregates the processing components and resource management. There was a need to broaden the array of interaction patterns for the stored data in HDFS to MapReduce. The birth of YARN enables a processing platform that is not limited to MapReduce.
Those mentioned above are the qualities of Apache Hadoop, which have made it so popular. We’ve also discussed what can be achieved in the world of big data and Hadoop.
If you’re looking for a big data hadoop certification to enhance your skills, we offer a training course in the same. Our curriculum is designed for people with basic knowledge of big data and will train them in the basic fundamentals and complex concepts of big data and Hadoop.