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7 pages in this section.
Where Redshift fits versus RDS/Aurora for analytical workloads - the columnar, massively-parallel model that makes it a data warehouse rather than a transactional database, and why you drive it with the Data API instead of row-by-row SDK calls.
Provision a Redshift Serverless namespace and workgroup, then create a table, load rows, and run your first analytic query through the Data API - in both boto3 and AWS SDK v3, with no JDBC connection.
Choosing a Redshift deployment model via SDK - provisioned clusters (CreateCluster with NodeType and node count) versus Redshift Serverless (CreateNamespace + CreateWorkgroup with base RPU capacity), with runnable boto3 and AWS SDK v3 code and a decision table.
Backup automation and disaster recovery for Redshift clusters via SDK - CreateClusterSnapshot for manual backups, EnableSnapshotCopy for automatic cross-region copies, CopyClusterSnapshot to retain a durable copy, and RestoreFromClusterSnapshot to rebuild a cluster in any region.
A checklist for building on Amazon Redshift from the SDK - columnar table design, bulk loading with COPY, connectionless queries via the Data API, workload management and cost-aware orchestration, snapshots and DR, and security.