Data Warehousing on AWS
Using Amazon Web Services, the AWS Data Warehousing Training course offers a comprehensive introduction to the field of cloud-based data warehousing. It is intended to help students become proficient with AWS’s data warehousing solutions, with a particular emphasis on Amazon Redshift, a fully managed, scalable, and quick data warehouse service.
Overview
The course Data Warehousing on AWS teaches you the principles, tactics, and best practices for creating a cloud-based data warehousing solution with AWS’s petabyte-scale data warehouse, Amazon Redshift. This course explains how to leverage AWS services including Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3 to gather, store, and prepare data for the data warehouse. This lesson also shows you how to analyze your data using Amazon QuickSight.
What you will Learn in this Data Warehousing on AWS Course?
- Understand the fundamentals of data warehousing and the role of Amazon Redshift in data warehousing solutions.
- Gain hands-on experience launching and configuring Amazon Redshift clusters to meet data warehousing requirements.
- Learn to design robust and scalable database schemas tailored for data warehousing needs.
- Identify and integrate various data sources for data ingestion into Amazon Redshift.
- Master the process of loading data into Amazon Redshift with best practices for efficiency and reliability.
- Develop proficiency in writing complex SQL queries and optimizing query performance in a Redshift environment.
- Explore Amazon Redshift Spectrum for querying exabytes of data in S3 without loading it into Redshift clusters.
- Acquire skills for maintaining and monitoring the health and performance of Amazon Redshift clusters.
- Enhance the ability to analyze and visualize data using Amazon Redshift and complementary AWS services.
- Understand best practices for security, cost management, and scaling in the context of AWS data warehousing.
Who should take up this Data Warehousing on AWS Course?
- Data Engineers
- Database Administrators
- Business Intelligence Professionals
- Data Analysts
- IT Managers overseeing data management teams
- Cloud Solutions Architects
- Data Scientists interested in data warehousing solutions
- Developers working on big data analytics
- System Administrators managing cloud infrastructure
- Technical Project Managers involved in data-centric projects
- Professionals seeking AWS Certification in Big Data and Data Analytics
Our Package
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
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
Data Warehousing on AWS FAQs
Q. What will I learn in the Data Warehousing on AWS training?
Ans.
The course covers AWS data warehousing solutions, focusing on Amazon Redshift design, performance, data loading, security, with practical exercises to establish solid understanding and skillset for scalable data warehousing.
Q. What are the career prospects after completing the Data Warehousing on AWS training?
Ans.
After Data Warehousing on AWS training, one could pursue roles like AWS Data Engineer, Cloud BI Developer, or Data Architect across tech, finance, healthcare—often advancing to senior technical positions, enhancing career growth.
Q. What are the prerequisites for enrolling in the Data Warehousing on AWS course?
Ans.
- Basic understanding of database principles, including relational databases.
- Familiarity with SQL and experience executing basic SQL queries.
- Basic knowledge of core AWS services and public cloud implementation. Prior experience with AWS is beneficial but not mandatory.
- Understanding of data warehousing concepts and the purpose they serve within an organization’s data management infrastructure.
Q. Which certification exam is linked with the Data Warehousing on AWS course?
Ans.
The course Data Warehousing on AWS is linked to DAS-C01.
Q. What is the duration of the Data Warehousing on AWS course?
Ans.
The duration of the course 24 hours.
Q. Is the Data Warehousing on AWS course available online or in-person?
Ans.
The course is available both online and in-person.
Q. What is required for online training?
Ans.
A laptop, decent internet speed, a Headset with microphone is required.
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