A developer guide for Amazon Redshift

Explore Amazon Redshift architecture: a petabyte-scale, fully managed data warehouse cluster with leader and compute nodes, parallel query execution, and data slices, enabling scalable, fast analytics via standard JDBC/ODBC connections.

Create schema and tables in Redshift for a demo database, manage users (create and drop), and load data from S3 with copy; use a manifest for parallel, multi-file loading.

Apply where predicates in sql to filter data on Amazon Redshift, using comparisons, in, like, ilike, and between, while handling case sensitivity and multiple conditions.

Sort the employees table by salary in ascending order with a select statement, or in descending order using the appropriate keyword for active employees filtered by location and name.

Explore aggregate functions in Redshift, including sum, count, min, max, and list aggregation, and learn how to group by location, use distinct, and format results with delimiters.

Use the having clause to filter grouped payroll data by location, selecting active employees with salaries over 500000, as shown for California and Chicago.

Master data transfer in Redshift by using select into, insert into, and alter table to copy, append, or create tables, with techniques for ignoring extra columns and filling nulls.

Explore how data moves between disk and memory in Redshift, highlighting columnar storage, selective column reads, and in-memory temporary tables to minimize I/O costs.

Explore how to use coalesce to return the first non-null value between primary and secondary customer names, and apply decode and case when patterns to handle nested nulls in redshift.

Understand how sort keys and distribution keys optimize redshift queries by block pruning with min/max maps, and compare compound versus interleaved sort keys.

Explore distribution styles in Amazon Redshift, including key distribution, even distribution, and all distribution, and how hashed values map data to cluster slices across nodes.

Define primary and foreign keys between customer and order tables, and create views in Amazon Redshift to query data from underlying tables.

Learn to configure Amazon Redshift workload management by creating query queues, including a super user queue, with up to eight queues and fifty slots, using the management console.

Configure workload management concurrency in Amazon Redshift, understand why concurrency levels matter, manage query queues like Q1, and prioritize faster queries while enabling up to five parallel queries.

learn how to manage workload with wlm user groups in Amazon Redshift, assign users to groups, route queries to specific queues, and view group membership and configuration.

Explore how Amazon Redshift workload management enables query hopping between queues, using Q1 and Q2, time thresholds, prerelease rights, and cancellations when limits are reached.