Mastering AWS Elastic Map Reduce EMR For Data Engineers
 
This course is tailored for individuals eager to master the deployment and management of Pyspark and Spark SQL applications on AWS EMR, orchestrate workflows using Step Functions, utilize Boto3 for EMR management, and much more.

AWS Elastic Map Reduce (EMR) is a pivotal AWS service for constructing extensive data processing infrastructures, harnessing Big Data Technologies like Apache Hadoop and Apache Spark. Throughout this course, you'll delve into AWS EMR, building comprehensive data pipelines leveraging Apache Spark and AWS Step Functions.

Here's a breakdown of the course outline:

1. **Introduction to AWS EMR**: Learn to navigate the AWS Web Console to create and manage EMR Clusters. Explore key features, connect to cluster nodes, and validate essential CLI interfaces and commands.

2. **Setting up Development Cluster**: Learn to set up a development cluster and discover the benefits of using AWS EMR Clusters for development.

3. **Development Life Cycle of Spark Applications**: Explore the development life cycle of Spark applications using Visual Studio Code Remote Development on the AWS EMR Development Cluster.

4. **Deploying Spark Application on EMR Cluster**: Build and deploy Spark applications on EMR Clusters, understand deployment modes, troubleshoot issues, and navigate relevant logs.

5. **Managing EMR Clusters with Boto3**: Learn to create clusters programmatically and deploy Spark applications in steps using Python Boto3.

6. **Building End-to-End Data Pipelines using AWS Step Functions**: Understand how to orchestrate EMR-based workflows or pipelines using AWS Step Functions, including cluster creation, application deployment, and termination.

7. **Enhancing EMR-based State Machine or Pipeline**: Perform validations within State Machines, such as file existence checks.

8. **Data Processing Applications with Spark SQL**: Design and develop solutions using Spark SQL Script, validate with appropriate commands and understand runtime arguments.

9. **Deploying Data Pipeline using AWS Step Function**: Learn to deploy Spark SQL Scripts on EMR Cluster using AWS Step Function, ensuring linear execution using Boto3 Waiters.

By the end of this course, you'll have a comprehensive understanding of various AWS EMR functionalities and be equipped with the skills to develop, deploy, and manage data pipelines efficiently in real-world scenarios.

**Requirements**:

- A computer science or IT Degree or 1-2 years of IT Experience
- Basic Linux Skills with the ability to run commands using Terminal
- Proficiency in Python programming
- Valid AWS Account to utilize AWS Services for building Data Pipelines

**Who Should Take This Course**:

- University Students seeking hands-on experience with AWS EMR for processing large volumes of data
- Aspiring Data Engineers and Data Scientists aiming to master data pipeline construction using AWS EMR
- Experienced Application Developers interested in building end-to-end data pipelines with Python and AWS EMR
- Experienced Data Engineers keen on leveraging Python and AWS EMR for comprehensive data pipeline development
- Any IT Professional eager to delve into AWS EMR for heavyweight Data Processing opportunities.