Big Data Hadoop is the most popular technology in the IT industry. Today, the employees who are upgraded in Big Data- Hadoop skills are in huge demand. People like Data Scientists and Data Analysts are the one who gets the offer from big organizations.

As you know data is tremendously increasing, it has become extremely important to store the data and not to lose any information. Preserving data today can only build a better future of any organization.

Big Data Analytics has played a crucial role in analyzing the data within no time and resulted in overall business growth, better decision making, and an edge over the competitors.

“Without Big Data Analytics, Companies are Blind & deaf, wandering out onto the web like deer on a freeway”  ~ Geoffrey, Moore

Role of Python and Data Analytics in Hadoop

Data architects who have learned Python, don’t need to learn java language to work on Hadoop. It is possible and easy if Hadoop programs are coded in python. Well, it’s good news for python lovers!  Even, the world of analytics doesn’t need many of Java programmers.

Python is the most user-friendly and easy to learn language, is used as a powerful tool in handling advanced analytics applications.

Hadoop is a ‘distributed file system’ or you can say, it’s an ‘open-source software framework’ which handles millions of data. Also, it helps the applications to run on systems with thousands of hardware nodes.

Along with this, it tends to increase the data transfer rate among the nodes. If a node failure occurs, the system continues to operate even in that condition. This approach lowers the risk of sudden system failure or data loss. This is what we all know about Hadoop.

Looking at some of the features of Hadoop, we can say:-

  • It possesses the ability to store and process a huge amount of data quickly.
  • As Hadoop processes data in a quick manner, the more computing nodes are used, the more the processing power increases.
  • It provides protection of data and applications from sudden hardware failure.
  • Hadoop, being an open-source framework, is free to use.
  • You can simply add nodes in case you want to grow your system for data handling, little administration is required.

Hadoop Ecosystem

As we know, Hadoop is a framework. If we consider Hadoop as a place to live in, one might not prefer it. Because for a place to live in, we need to convert a framework into a home which requires a proper ecosystem.

This ‘Hadoop ecosystem’ provides proper decor which makes it a ‘comfortable home’ for big data activities to be performed.

A Hadoop Ecosystem includes Apache Open Source projects along with a lot of commercial tools. Some of the open-source examples are Pig, Oozie, Sqoop, Spark, Hive.

Hadoop Ecosystem Elements

  • Hive and Pig

Hive is a data warehouse software. It addresses the structuring of data and how it is queried in  Distributed Hadoop clusters. The hive is also needed to write queries for data. A hive consists of several components like HCatalog, WebHCat, and HiveQL.

Unlike Hive, Pig is something different. It is a procedural language used for creating applications for large sets of data. Pig is very much popular because of its automation in some complexity in the MapReduce environment.

  • Sqoop and Flume

Imagine sqoop to be a front-end loader for big data. Sqoop acts as an interface for the process of transferring bulk data from Hadoop into other data stores or relational databases.

Flume is a distributed service for collecting, accumulating, and moving a large amount of streaming data.

  • MongoDB and HBase

MongoDB is a plugin(connector) for the Hadoop ecosystem through which it can be used for input and output sources. MongoDB and Hadoop are the foundation for the operations of big data. Whether the matter is providing customer service, enhancing business, or reducing risk, it performs operations in the best way for big data.

HBase is a distributed, scalable, NoSQL database and is accessible through Java API.

  • Spark and Hadoop

Spark is treated both as a programming model as well as a computing model. It is used for accessing data from HDFS. It also provides the entrance to in-memory computing. One of the main reasons for Spark becoming popular is that it supports SQL. Spark can also run on non-Hadoop clusters, that’s why it is often considered as an independent model.

Hadoop Job Market

The scope for the Big Data Hadoop market is not limited to the IT industry only, other industries also want Big Data enthusiasts to be a part of their team. Under this niche, Big Data gives you exponential growth in your career. But, you need to be trained in Big Data technology before stepping into the IT industry.

Here, the graph indicates the vacant job opportunities in Big Data:

Hadoop job market 

No need to worry, with proper practice and job-oriented training and certifications, you can easily make a career in this niche.

Go for Big Data Training

As the market is getting heavy with data day-by-day and more companies are moving towards Big Data so this revolution of data has brought many companies into trouble with a lot of issues and challenges bringing a big opportunity and challenges for the job seekers. The presence of Big Data has given birth to many institutions in the educational world which help people learn Big Data Hadoop. 

Our institute, Gyansetu is also an educational institute that provides multiple courses including Python Training, Apache Spark Training, and DevOps Training. The big difference which categorizes us among top institutes is that our team of experts for Big Data Hadoop has specially designed an industry-oriented course that is manipulated in learning and getting jobs easily.

During the training, you will be analyzing large data sets of Social Media Sites, Stock Market, New York Uber City Trips, Tourism & Aviation Industry.