The best online Hadoop course exists: Here’s what we know

Last updated on April 6th, 2024 at 07:31 pm

Hadoop is an inseparable part of Data Science and Analytics. Without Hadoop, there is no weight in the analytics field at all. Also, it wouldn’t be wrong to say that the mere existence of the analytics would be questionable without a proper database system to support it.

Where would you store all this data? So yeah, Hadoop is important. Now, where would you pick up this important skill? You can learn Hadoop online and hone your skills.

So, if you want to enrol in a data science course, you must learn Hadoop online. Imarticus Learning is the best place that you can trust. Read more.

What is Hadoop? What is it Used for?

Apache Hadoop is an open-source software framework that helps to store a large amount of data and process it for computation using a network of many servers. The size of the datasets ranges from Gigabytes to Petabytes. By clustering the servers, not only do you get to store a large amount of data (already mentioned before), but you can also process them quickly and efficiently.

Hadoop also helps you with a structured data warehousing system with the help of the MapReduce function.

Four main Hadoop modules can describe its entire functioning:

Hadoop Distributed File System (HDFS): Low-end hardware is used to run the standard distributed file system. Capable of providing an efficient data throughput than the conventional file systems, HDFS fault tolerance is quite high. It also provides native support to large datasets.
Yet Another Resource Negotiator (YARN): A cluster node manager monitors resource usage and schedules tasks and jobs.
MapReduce: MapReduce is a framework that helps programs perform parallel computation while the data is being compressed. The map task consumes the input data transforming the dataset into an intermediary dataset that can undergo clustering in key-value pairs. The output reduces the tasks to aggregate and provides the desired result.
Hadoop Common: The function of this module is to provide common Java libraries that all the other modules can use for performing their tasks.

Why is Hadoop Important for the Data Science and Analytics Field?

There are many uses of Hadoop in the field of Data Science and Analytics. Needless to say, if you pursue PG in Data Analytics, you must know Hadoop. As mentioned above, most of the data warehousing and storage needs are taken care of by Hadoop only. The rest of the uses are mentioned below:

Data Engagement with Large Datasets

Earlier, data scientists faced restrictions in using large datasets on their local servers. Since most of the data science and analysis requires cleaning and processing a large amount of data.

The increase in data usage requires databases for analysing and exploring the datasets. Therefore, Big Data and Hadoop give a common platform for this task. With the help of Hadoop, one can use Map Reduce, PIG or HIVE to process the data and obtain the required result.

Processing of Data

Data Scientists use Big Data Training for pre-processing most of the datasets. Apart from acquiring the data, you need to transform it after cleaning it and extracting the required features. After the datasets have been transformed into standardised feature vectors from raw data, we get the required output. This way, the entire tasks of the data scientists and analysts have been simplified by Hadoop.

Data Mining Dataset for Richer Machine Learning Algorithms

ML algorithms perform better with larger datasets. The results are also extremely precise because techniques like product recommenders, clustering and outlier detection can produce a good statistical output. Conventionally, ML engineers used to incorporate smaller datasets in which data was quite limited. Such models used to be less accurate and had lower performance. The Hadoop system allowed the storage of scalable linear datasets in RAW format.

Data Agility

Conventional database systems require a limited schema structure. Hadoop uses a flexible schema that helps its users to handle Big Data. With the help of this flexible schema, users can eliminate the requirement for schema redesign whenever new fields are required. It also speeds up the entire process.