Data Warehousing on AWS
Duration : 3 Days (24 Hours)
Data Warehousing on AWS Course Overview:
The Data Warehousing on AWS course provides a comprehensive introduction to designing cloud-based data warehousing solutions using Amazon Redshift, a powerful petabyte-scale data warehouse service in AWS. Throughout the course, participants will learn essential concepts, strategies, and best practices for effectively building and managing a data warehouse on AWS.
The course covers various aspects of the data warehousing process, starting from data collection and storage to data preparation for analysis. Participants will explore and utilize AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3 to collect, store, and prepare data for the data warehouse.
Additionally, the course demonstrates how to leverage Amazon QuickSight, a business intelligence tool, to perform data analysis on the stored data. Participants will learn how to utilize QuickSight’s features to gain insights and visualize data effectively.
By the end of the course, participants will have a solid understanding of the key components and strategies involved in designing a cloud-based data warehousing solution on AWS. They will be equipped with the knowledge and skills to collect, store, prepare, and analyze data using Amazon Redshift and other AWS services.
Course level: Intermediate
Intended audience
- This course is intended for:
- Database Architects
- Database Administrators
- Database Developers
- Data Analysts
- Data Scientists
Module 1: Introduction to Data Warehousing
- Relational databases
- Data warehousing concepts
- The intersection of data warehousing and big data
- Overview of data management in AWS
- Hands-on lab 1: Introduction to Amazon Redshift
Module 2: Introduction to Amazon Redshift
- Conceptual overview
- Real-world use cases
- Hands-on lab 2: Launching an Amazon Redshift cluster
Module 3: Launching clusters
- Building the cluster
- Connecting to the cluster
- Controlling access
- Database security
- Load data
- Hands-on lab 3: Optimizing database schemas
Module 4: Designing the database schema
- Schemas and data types
- Columnar compression
- Data distribution styles
- Data sorting methods
Module 5: Identifying data sources
- Data sources overview
- Amazon S3
- Amazon DynamoDB
- Amazon EMR
- Amazon Kinesis Data Firehose
- AWS Lambda Database Loader for Amazon Redshift
- Hands-on lab 4: Loading real-time data into an Amazon Redshift database
Module 6: Loading data
- Preparing Data
- Loading data using COPY
- Maintaining tables
- Concurrent write operations
- Troubleshooting load issues
- Hands-on lab 5: Loading data with the COPY command
Module 7: Writing queries and tuning for performance
- Amazon Redshift SQL
- User-Defined Functions (UDFs)
- Factors that affect query performance
- The EXPLAIN command and query plans
- Workload Management (WLM)
- Hands-on lab 6: Configuring workload management
Module 8: Amazon Redshift Spectrum
- Amazon Redshift Spectrum
- Configuring data for Amazon Redshift Spectrum
- Amazon Redshift Spectrum Queries
- Hands-on lab 7: Using Amazon Redshift Spectrum
Module 9: Maintaining clusters
- Audit logging
- Performance monitoring
- Events and notifications
- Lab 8: Auditing and monitoring clusters
- Resizing clusters
- Backing up and restoring clusters
- Resource tagging and limits and constraints
- Hands-on lab 9: Backing up, restoring and resizing clusters
Module 10: Analyzing and visualizing data
- Power of visualizations
- Building dashboards
- Amazon QuickSight editions and features
We recommend that attendees of this Data Warehousing on AWS course have:
- Taken AWS Technical Essentials (or equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
Q: What is this training about?
A: This training is a comprehensive program designed to provide individuals with the knowledge and skills needed to design and implement data warehousing solutions using Amazon Web Services (AWS). It covers various aspects of data warehousing, including data modeling, data loading, query optimization, and analytics using AWS services such as Amazon Redshift, Amazon Athena, and AWS Glue.
Q: Who should consider taking this training?
A: This training is suitable for data engineers, data architects, database administrators, and IT professionals who want to leverage AWS services for building scalable and high-performance data warehousing solutions. It is beneficial for those involved in data management, data integration, and performing complex analytics queries on large datasets.
Q: What topics are covered in this training?
A: The training covers a range of topics, including data warehouse concepts, data modeling and schema design, data loading strategies, query optimization techniques, managing and monitoring data warehouse clusters, integrating with other AWS services, and best practices for building efficient data warehousing solutions on AWS.
Q: Are there any prerequisites for taking this training?
A: We recommend that attendees of this course have
- Taken AWS Technical Essentials (or equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
Q: How can I prepare for this training?
A: To prepare for the training, it is recommended to have a good understanding of data warehousing concepts, SQL queries, and database design principles. Familiarizing yourself with AWS services like Amazon Redshift, Amazon Athena, and AWS Glue will also be advantageous. Exploring AWS documentation and hands-on exercises related to data warehousing can enhance your preparation.
Q: Is this training available online?
A: Yes, we offer online training options for Data Warehousing on AWS to provide flexibility for learners.
Q: Can this training help in obtaining AWS certifications?
A: While the training provides valuable knowledge and skills in building data warehousing solutions, it does not directly prepare you for specific AWS certifications. However, it lays a solid foundation for pursuing advanced certifications related to data analytics or AWS architecture.
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