Last updated on April 6th, 2024 at 07:22 pm
Big data and Hadoop are two of the most searched terms today on the internet. The main reason behind this is that Hadoop is considered the framework of big data.
If you are interested in learning about Hadoop, then it is important that you have some basic knowledge of big data. In this article, we will discuss big data first and then move to Hadoop and related aspects.
What is Big Data?
Big data comprises huge datasets, which are extremely large in volume and complex to store and process for traditional systems. Big data faces problems in regards to velocity, volume, and variety.
The volume of data produced every day is simply enormous. Social media contributes to maximum data generation. The time taken for processing data varies from one enterprise to another. With big data, it is possible to have high-speed data computation. Most importantly, data is available in different formats like images, audio, video, text, and XML. With big data, it is possible to carry out analytics on different varieties of data.
What is Hadoop?
If you are interested in knowing how to become a data analyst or make a data scientist career, it is important that you know Hadoop and big data. Hadoop provides solutions to various big data problems. Hadoop is an emerging technology, with which you will be able to store huge volumes of datasets on a cluster of machines in a distributed manner.
Hadoop also offers big data analytics through a distributed computing framework. Hadoop is open-source software, which was initially developed as a project by Apache Software Foundation. Since its inception, two versions of Hadoop have been released.
There are different flavors in which Hadoop is available. Some of them are MapR, Cloudera, Hortonworks, and IBM BigInsight.
Prerequisites for Learning Hadoop
Whether you are looking to make a career as a data scientist or a data analyst, you have to know Hadoop pretty well. However, before learning Hadoop, there are certain things about which you should have a fair idea. They are as follows:
- Basic Java concepts - Learning Java simultaneously with Hadoop or having prior knowledge in Java proves to be helpful in learning Hadoop. You can reduce functions or write maps in Hadoop by using other languages like Perl, Ruby, C, and Python. This is possible with streaming API. It supports writing to standard output and reading from standard input. There are also high-level abstraction tools in Hadoop like Hive and Pig. For these, there is no need to be familiar with Java.
- Knowledge of some basic Linux commands - Hadoop is set over Linux operating system. Therefore, knowing some basic Linux commands is definitely an added advantage. These commands are used for downloading and uploading files from HDFS.
Core Components of Hadoop
There are three core components of Hadoop. We will discuss them here.
- Hadoop Distributed File System (HDFS) - Hadoop Distributed File System caters to the need for distributed storage for Hadoop. There is a master-slave topology in HFDS. While the high-end machine is the master, the general computers are the slaves.
The big data files are broken into a number of blocks. With Hadoop, these blocks are stored in a distributed manner on the cluster of slave nodes. Metadata is stored on the master machine.
- MapReduce - In Hadoop, MapReduce is the data processing layer. Data processing takes place in two phases. They are:
- Map Phase - In this phase, there is the application of business logic to data. The input data gets transformed into key-value pairs.
- Reduce Phase - The output of Map Phase is the input of Reduce Phase. It applies aggregation depending on the important key-value pairs.
- YARN - It is the short form of Yet Another Resource Locator. The main components of YARN are resource manager, node manager, and job submitter.
The main idea of YARN is to split the work of job scheduling and resource management. There is also one global resource manager and application master per application. A single application can either be one job or a DAG of jobs.
Different Hadoop Flavours
There are different flavors of Hadoop. They are as follows:
- Hortonworks - This is a popular distribution in the industry
- Apache - This can be considered the vanilla flavor. The actual code resides in Apache repositories
- MapR - It has rewritten HDFS and the HDFS is faster when compared to others
- Cloudera - This is the most popular in the industry
- IBM BigInsights - Proprietary distribution
Learning the Basics of Hadoop Online
The best way to learn the basics of Hadoop is online. There are many tutorials and e-books available on the web where you will have a fair knowledge of the basics of Hadoop. Many institutes like Imarticus Learning offer dedicated courses in learning big data, Hadoop, and related subjects. On the successful completion of the course, you will get certification from the institute, which will help in your professional career as well.
