Overview of AWS Data Analytics
The AWS Data Analytics Training course offered by Stalwart Learning is designed to provide participants with the skills and knowledge required to work with data on the AWS cloud platform.
Throughout the training, participants will learn how to use AWS data analytics services such as Amazon Kinesis, Amazon EMR, Amazon Redshift, Amazon Athena, and Amazon QuickSight, among others, to process, store, and analyze data. They will also learn about data integration, data warehousing, and big data processing techniques, as well as machine learning and artificial intelligence applications in data analytics.
The course will also cover best practices for managing data on AWS, including data security, access control, and compliance considerations. Participants will gain hands-on experience with AWS data analytics services through practical exercises, use cases, and case studies, and learn how to design, implement, and manage data analytics solutions on the AWS cloud platform.
By the end of the training, participants will have a solid understanding of AWS data analytics services and their applications in various industries. They will also be well-prepared to take the AWS Certified Data Analytics – Specialty certification exam, which will demonstrate their proficiency in data analytics on the AWS cloud platform.
This AWS Data Analytics Training program is ideal for data analysts, data engineers, business analysts, and IT professionals who want to develop their skills in data analytics on the AWS cloud platform.
Duration
24-40 Hours
Module 1: Introduction to AWS Data Analytics
- Overview of AWS data analytics services and features
- Understanding the AWS data analytics ecosystem
- Exploring the benefits of using AWS for data analytics
- Introduction to data analytics workflows on AWS
Module 2: Amazon S3 (Simple Storage Service) for Data Storage
- Introduction to Amazon S3 and its key features
- Creating and managing S3 buckets for data storage
- Organizing data in S3 using folders and prefixes
- Understanding S3 data consistency and durability
Module 3: Amazon Athena
- Introduction to Amazon Athena and serverless query execution
- Creating and querying data in Athena tables
- Partitioning and optimizing data in Athena
- Integrating Athena with other AWS services
Module 4: Amazon Redshift
- Introduction to Amazon Redshift and data warehousing
- Creating and managing Redshift clusters for data analytics
- Data loading and transformation in Redshift
- Query optimization and performance tuning in Redshift
Module 5: Amazon EMR (Elastic MapReduce)
- Introduction to Amazon EMR and big data processing
- Setting up and managing EMR clusters for data analytics
- Data processing with Apache Spark and Hadoop on EMR
- Integrating EMR with other AWS services
Module 6: Amazon Glue
- Introduction to Amazon Glue and data cataloging
- Data preparation and ETL (Extract, Transform, Load) with Glue
- Crawling and organizing data using Glue crawlers
- Integrating Glue with other AWS data analytics services
Module 7: Amazon QuickSight
- Introduction to Amazon QuickSight and data visualization
- Creating and managing data visualizations in QuickSight
- Building interactive dashboards and reports
- Integrating QuickSight with other AWS data services
Module 8: Data Lakes and Data Pipelines
- Designing and building data lakes on AWS
- Building data pipelines for data ingestion and processing
- Data lake architecture and best practices
- Orchestration and scheduling with AWS Step Functions and AWS Data Pipeline
Module 9: Advanced Analytics and Machine Learning
- Introduction to advanced analytics and machine learning on AWS
- Utilizing Amazon SageMaker for machine learning workflows
- Implementing predictive analytics with AWS services
- Integrating analytics and ML with data pipelines
Module 10: AWS Data Analytics Exam Preparation
- Reviewing exam objectives and test-taking strategies
- Practicing with sample questions and scenarios
- Final exam review and preparation