Big Data on Amazon Web Services

Description:

This training is designed to provide a comprehensive understanding of Big Data solutions on Amazon Web Services (AWS). Over three days, participants will explore how AWS enables scalable and secure data processing, storage, and analytics. The training covers essential services such as Amazon S3, EMR, Redshift, and Kinesis, while emphasizing best practices in managing large datasets. Attendees will gain hands-on experience with designing and implementing Big Data workflows and learn how to harness AWS tools to drive insights and business value from data.

Duration: 3 Days

Course Code: BDT28

Learning Objectives:

By the end of this training, participants will be able to:

• Describe the key components of Big Data solutions on AWS.
• Implement scalable storage and processing of large datasets using AWS services.
• Analyze data using AWS analytics tools such as Redshift and QuickSight.
• Design Big Data workflows to handle real-world business challenges.
• Optimize performance and cost for Big Data workloads on AWS.

• Participants should have basic knowledge of cloud computing and familiarity with fundamental data concepts such as storage, databases, and analytics.

• Data professionals looking to leverage AWS for Big Data solutions.
• Cloud architects and engineers exploring AWS Big Data services.
• Developers and analysts seeking to build scalable analytics pipelines.

Course Outline:

Day 1: Foundations of Big Data and AWS Services

Module 1: Introduction to Big Data

• What is Big Data? Characteristics and Challenges
• The Role of Cloud in Big Data

Module 2: AWS Big Data Ecosystem

• Overview of Key Services: S3, EMR, Redshift, Kinesis, and Athena

• Big Data Architecture on AWS

Module 3: Storage and Data Ingestion

• Data Storage: Deep Dive into Amazon S3
• Ingestion Services: AWS Glue, Kinesis, and Data Pipelines
• Hands-On: Setting Up Data Ingestion

Day 2: Data Processing and Analysis

Module 4: Processing Big Data on AWS

• Amazon EMR for Data Processing
• Introduction to Apache Spark and Hadoop on AWS
• Hands-On: Running a Data Processing Job on EMR

Module 5: Data Warehousing with Redshift

• Setting Up and Managing Redshift Clusters

• Optimizing Redshift for Query Performance
• Hands-On: Querying Data with Redshift

Module 6: Data Visualization and Analysis

• Using Amazon QuickSight for Business Intelligence
• Hands-On: Creating Dashboards

Day 3: Advanced Topics and Best Practices

Module 7: Streaming Data Processing

• Real-Time Analytics with Amazon Kinesis
• Hands-On: Building a Real-Time Data Pipeline

Module 8: Security and Governance

• Best Practices for Data Security on AWS
• Compliance and Data Governance

Module 9: Cost Management and Optimization

• Cost-Effective Strategies for Big Data Workloads
• Using AWS Cost Management Tools

Module 10: Capstone Project

End-to-End Workflow: Ingest, Process, Store, and Analyze Data

• Presenting Findings and Solutions

Module 11: Review and Wrap-Up

• Key Takeaways and Resources for Further Learning
• Q&A Session

Training material provided: Yes (Digital format)