BigData Course

Course Description

This course is designed to provide participants with a comprehensive understanding of Big Data and its technologies. You will learn the core concepts, tools, and techniques needed to work with Big Data in a real-world context. Topics covered include distributed computing, data processing, and storage technologies such as Hadoop, Spark, and NoSQL databases. You will also learn how to work with Big Data using programming languages such as Python and R.

Course Objectives

By the end of this course, you will be able to:

• Understand the fundamentals of Big Data and distributed computing
• Work with Hadoop and other Big Data processing and storage technologies
• Use programming languages such as Python and R to work with Big Data
• Develop and implement Big Data solutions using AWS services
• Build event-driven Big Data processing pipelines using Apache Kafka and Spark Streaming
• Use NoSQL databases for storing and retrieving Big Data

Course Outline

Module 1: Introduction to Big Data

• Understanding Big Data and its importance
• Overview of distributed computing and Hadoop ecosystem
• Overview of NoSQL databases

Module 2: Hadoop Ecosystem

• Hadoop Distributed File System (HDFS)
• MapReduce programming model
• Hadoop ecosystem tools and technologies (Hive, Pig, Sqoop)

Module 3: Big Data Processing with Spark

• Spark architecture and components
• RDD (Resilient Distributed Datasets)
• Spark Streaming and Structured Streaming

Module 4: Big Data Storage with NoSQL Databases

• Overview of NoSQL databases
• MongoDB
• Cassandra

Module 5: Programming with Python and R for Big Data

• Overview of Python and R for Big Data
• Working with PySpark and SparkR
• Data analysis and visualization using Python and R

Module 6: Big Data Solutions with AWS

• Introduction to AWS Big Data services
• Setting up Big Data solutions on AWS
• Working with Amazon EMR, Amazon Redshift, and Amazon DynamoDB

Module 7: Event-Driven Big Data Processing

• Introduction to event-driven architecture
• Apache Kafka and Kafka Streams
• Spark Streaming for event processing

Prerequisites

• Basic knowledge of programming concepts and algorithms
• Familiarity with SQL and databases
• Basic understanding of Linux commands and shell scripting
• Knowledge of Python or R programming is a plus

Course Duration

The course is expected to take approximately 40 hours to complete, including lectures, hands-on exercises, and assignments.

Course Delivery

This course can be delivered in-person, online, or through a combination of both. Hands-on exercises and assignments will be provided to participants throughout the course to reinforce learning.

Target Audience

This course is ideal for:

• Data Engineers
• Big Data Developers
• Solution Architects
• Software Engineers
• Data Analysts
• Anyone interested in working with Big Data