Database
CoreAmazon Redshift
Analytical warehousing for SQL, bulk movement, external access, materialized results, and governed sharing.
Key points
- Redshift is the analytical warehouse for columnar SQL, bulk loading, warehouse-local transformations, and governed sharing.
- COPY, UNLOAD, Spectrum, federated query, materialized views, distribution, and sort design solve different data paths.
Best-known use cases
- Build a SQL data warehouse for analytics and business intelligence.
- Load curated S3 data and serve dimensional reporting models.
- Query external lake data with Redshift Spectrum.
- Share governed warehouse datasets across teams or accounts.
What candidates often confuse it with
- Redshift optimizes repeated warehouse analytics; Athena queries S3 directly and RDS or Aurora serves transactions.
Key takeaway
Choose Redshift for managed analytical warehousing where table design and workload processing belong inside the warehouse.
Relevant exam tasks
- D1.1 — Task 1.1: Perform data ingestion
- 1.1.1 — Read data from streaming sources (for example, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka [Amazon MSK], Amazon DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift).
- D1.2 — Task 1.2: Transform and process data
- 1.2.5 — Implement data transformation services based on requirements (for example, Amazon EMR, AWS Glue, Lambda, Amazon Redshift).
- D2.1 — Task 2.1: Choose a data store
- 2.1.1 — Implement the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka [Amazon MSK]).
- 2.1.6 — Manage locks to prevent access to data (for example, Amazon Redshift, Amazon RDS).
- D2.3 — Task 2.3: Manage the lifecycle of data
- 2.3.1 — Perform load and unload operations to move data between Amazon S3 and Amazon Redshift.
- D2.4 — Task 2.4: Design data models and schema evolution
- 2.4.1 — Design schemas for Amazon Redshift, DynamoDB, and Lake Formation.
- D3.1 — Task 3.1: Automate data processing by using AWS services
- 3.1.4 — Use the features of AWS services to process data (for example, Amazon EMR, Amazon Redshift, AWS Glue).
- D3.2 — Task 3.2: Analyze data by using AWS services
- 3.2.3 — Use SQL in Amazon Redshift and Athena to query data or to create views.
- D4.2 — Task 4.2: Apply authorization mechanisms
- 4.2.3 — Provide database users, groups, and roles access and authority in a database (for example, for Amazon Redshift).
- D4.5 — Task 4.5: Understand data privacy and governance
- 4.5.1 — Grant permissions for data sharing (for example, data sharing for Amazon Redshift).