Data Warehousing on AWS

1. Scalability: AWS offers scalable infrastructure to handle varying workloads without upfront investment.
2. Performance: High-performance computing resources ensure fast query processing.
3. Managed Services: AWS provides managed data warehousing services like Amazon Redshift for easier management.
4. Security: Robust security features and compliance certifications ensure data protection.

Before learning Data Warehousing on AWS, it's beneficial to have:

1. Basic AWS Knowledge: Familiarity with AWS services and terminology like EC2, S3, IAM, and VPC.
2. SQL Skills: Understanding of SQL for querying and manipulating data in databases.
3. Data Modeling: Knowledge of data modeling concepts for designing efficient data warehouse schemas.
4. ETL Concepts: Understanding of Extract, Transform, Load (ETL) processes for data ingestion and transformation.

By learning Data Warehousing on AWS, you gain the following skills:

1. AWS Services: Proficiency in using AWS services specifically tailored for data warehousing, such as Amazon Redshift, Amazon RDS, Amazon Aurora, and Amazon S3.

2. Data Modeling: Ability to design and implement efficient data models for warehousing large datasets on AWS.

3. ETL Processes: Skills in building and managing Extract, Transform, Load (ETL) processes using AWS tools like AWS Glue, AWS Data Pipeline, or custom scripts.

4. Data Integration: Knowledge of integrating various data sources into AWS data warehousing solutions, including databases, data lakes, and streaming data.