Learn to Use Big Data with Spark and Hadoop with this IBM Course

The Introduction to Big Data with Apache Spark and Hadoop is an online course offered by the University of California, San Diego, on the Coursera platform. The course is part of the ‘Big Data and Spark Foundations’ specialization and is designed for learners who want to gain a foundational understanding of big data and its applications using Apache Spark and Hadoop.

In this course, you will learn about the characteristics of big data and its application in big data analytics. You will gain an understanding of the features, benefits, limitations, and applications of some of the big data processing tools. You will explore how Hadoop and Hive help leverage the benefits of big data while overcoming some of its challenges. Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets in various databases and file systems that integrate with Hadoop. This course will help you to make it big in the big data domain.

Apache Spark is an open-source processing engine that provides users with new ways to store and use big data. It is an open-source processing engine built around speed, ease of use, and analytics. This course will teach you how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the components that make up Apache Spark.

In this course, you will also learn about Resilient Distributed Datasets, or RDDs, that enables parallel processing across a Spark cluster's nodes.

The course instructors are:

• Karthik Muthuraman a Software Engineer and Data Scientist at IBM’s Center for Open Source Data and AI Technologies (CODAIT). 
• Aije Egwaikhide is a Senior Data Scientist at IBM with a degree in Economics and Statistics from the University of Manitoba and a Post-graduate in Business Analytics from St. Lawrence College, Kingston.

The course is divided into 5 modules:

• Introduction to Big Data
   
   This module provides an overview of big data and the technologies used to process it.

• Hadoop and MapReduce
   
  This module introduces learners to Hadoop and MapReduce, two of the most popular technologies for processing big data.

• Spark and Resilient Distributed Datasets (RDDs)
   
   This module covers Apache Spark, a fast and powerful open-source engine for large-scale data processing, and Resilient Distributed Datasets (RDDs), which are the building blocks of Spark.

• Spark SQL and DataFrames
   
  This module introduces learners to Spark SQL, a Spark module for structured data processing, and DataFrames, a distributed data collection organized into named columns.

• Spark SQL and DataFrames
   
  This module covers Spark Streaming, a Spark module for real-time data processing.
   

The course includes video lectures, quizzes, programming assignments, and a final project. Learners will gain hands-on experience with Apache Spark and Hadoop through programming assignments and a final project. The course is self-paced and can be completed in about 6 weeks. Learners earn a certificate on completion.

Overall, this course is a great option for learners who want to understand big data and its applications using Apache Spark and Hadoop. The course covers a wide range of topics, from the basics of big data to the more advanced topics of Spark Streaming, making it a comprehensive introduction to big data processing with Spark and Hadoop.

Until a few years ago, businesses gathered information, ran analytics, and unearthed information that could be used for future decisions. Today, businesses can collect data in real time and analyze big data to make immediate, better-informed decisions. The benefits of big data analytics are speed and efficiency. This ability to work faster – and stay agile – gives organizations a competitive edge they did not have before.

As a result, the demand for big data talent is rapidly increasing, yet a significant supply gap exists. Even though data analytics is a popular career, there are still a lot of vacancies due to a global skills shortage. According to a McKinsey Global Institute study, the United States would be short 190,000 data scientists and 1.5 million managers and analysts that can analyze and make choices based on big data. 

Since the demand for professionals is high in this field, I decided to do a big data course. On researching, I came across this course on Coursera with the highest rating and applied for it. I was in my Final year of Bachelor in Technology when I enrolled for this course. This course has proved helpful for me in giving job interviews.