Big Data, Hadoop, and Spark Basics by IBM via Edx: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based	Learning efforts 2-3 Hours Per Week

Course overview

Business organizations now need expert big data professionals to understand customer behaviour out of the complex and unstructured data of pictures, posts, tweets, audio files, videos and satellite images. Edx here presents you with a foundational course to get started with big data and climb up the ladder of a career in the industry. Big Data, Hadoop, and Spark Basics is an introductory computer science course that provides an introductory understanding of big data and its tools.

Big Data, Hadoop, and Spark Basics Certification, provided by edX, is a beginner-level programme offered by IBM. The online programmes will explore big data processing tools such as Apache Spark, Hive and Hadoop. Big Data, Hadoop, and Spark Basics Certification by IBM is a 6-week long programme that demands 2-3 hours per week from the learners to devote. The self-paced programme is a free certification programme that can be upgraded to the fullest mode by paying the fee.

Course and certificate fees

Type of course

Free

EdX provides two enrollment options to get into the Big Data, Hadoop, and Spark Basics Online Courses; audit track and verified track. In the audit track, the candidates can attend the courses without paying any fee and will be given limited access. Whereas in the verified track, the learners will have to pay the Big Data, Hadoop, and Spark Basics Certification Fees and will be provided with certification and graded assignments.

Big Data, Hadoop, and Spark Basics Certification Fees

Course Title	Total Fee in INR
Big Data Analytics (Verified Tracks)	INR 8,273

certificate availability

Yes

certificate providing authority

IBM

certificate fees

₹8,273

Who it is for

Big data developer Big data analytics engineer Big data engineer

Big Data, Hadoop, and Spark Basics Classes is an ideal programme for the professionals such as

Eligibility criteria

Academic Qualifications

EdX specifies some prerequisites for the learning to be eligible for joining the Big Data, Hadoop, and Spark Basics Certification Course. They are computer and IT literate and interested in learning the process of data management.

What you will learn

Knowledge of big data Knowledge of apache spark

Big Data, Hadoop, and Spark Basics Certification Syllabus will help the learners:

Understand the basics of big data, the tools of big data processing, and its use cases.
Learn the application of runtime environment options and Apache Spark development.
Familiarize with fundamental concepts of Spark programming, SparkSQL and DataFrames, and data sets.
Closely know the ecosystem, practices, architecture, and applications of Hadoop such as MapReduce, HBase, Spark, and Distributed File System (HDFS).

The syllabus

Module 1 – What is Big Data?

Introduction to Big Data
What is Big Data?
Impact of Big Data
Parallel Processing, Scaling, and Data Parallelism
Tools of Big Data
Beyond the Hype
Big Data Use Cases
Viewpoints about Big Data

Module 2 – Introduction to the Hadoop Ecosystem

What is Hadoop
An introduction to MapReduce
The Hadoop Ecosystem/Common components: Introducing HDFS, Hive, HBase, and Spark, other modules
Working with HDFS
Working with HBase
Lab: MapReduce

Module 3 – Introduction to Apache Spark

Why use Apache Spark?
Functional Programming Basics
Parallel Programming using Resilient Distributed Datasets
Scale-out / Data Parallelism in Apache Spark
DataFrames and SparkSQL
Lab: Practical examples with PySpark

Module 4 – DataFrames and SparkSQL

Introduction to Data-Frames & SparkSQL
RDDs in Parallel Programming and Spark
Data-frames and Datasets
Catalyst and Tungsten
ETL with Data-frames
Lab: ETL with Data-frames
Real-world usage of SparkSQL
Lab: SparkSQL

Module 5 – Development and Runtime Environment options

Apache Spark architecture
Overview of Apache Spark Cluster Modes
How to Run an Apache Spark Application
Using Apache Spark on IBM Cloud
Lab: Scale-out on IBM Spark Environment in Watson Studio
Setting Apache Spark Configuration
Running Spark on Kubernetes
Lab: Spark on Kube

Module 6 – Monitoring & Tuning

The Apache Spark User Interface
Monitoring Jobs
Debugging of parallel jobs
Understanding Memory resources
Understanding Processor resources
Lab: Monitoring and Performance tuning

Module 7 – Final Quiz

Admission details

Join the Big Data, Hadoop, and Spark Basics Training through the following steps:

Step 1 - Browse the official URL https://www.edx.org/course/big-data-hadoop-and-spark-basics

Step 2 - Get started with the programmes by choosing the option ‘Enroll’.

How it helps

By enrolling in the programmes, the learners will have the Big Data, Hadoop, and Spark Basics certification benefits including a thorough understanding of big data and its management tools. Plus, EdX will confer the paid students enrolled in the verified track certification of completion.

Instructors

Mr Karthik Muthuraman
Software Engineer
IBM

Other Bachelors, Other Masters

Ms Aije Egwaikhide
Senior Data Scientist
IBM

Other Bachelors, Other Masters

FAQs

Big Data, Hadoop, and Spark Basics online course is developed and delivered by?

The online certification programme is a joint venture between EdX and IBM.

What is the duration of Big Data, Hadoop, and Spark Basics online certification? How much time does the learner need to devote to the course per week?

The duration of the online programmes is 6 weeks and the candidates are required to spend 2-3 hours a week.

Which level of the audience is targeted by the online programme by IBM?

The online course on big data is intended for introductory level learners.

What are the prior requirements of the online programme?

The prerequisites for the online certification programme are computer and IT literacy and an understanding of data management.

Who are the instructors who are in the charge of the online programme?

The online certification program is taught by Karthik Muthuraman who is a software engineer (Machine Learning) and Aije Egwaikhide who is a senior Data Scientist at IBM.