This GitHub project provides a series of lab exercises which help users get started using the Redshift platform.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse solution that uses columnar storage to minimise IO, provides high data compression rates, and offers fast performance. This GitHub project provides a series of lab exercises which help users get started using the Redshift platform. It also helps demonstrate the many features built into the platform.
| # | Lab Name | Lab Description |
|---|---|---|
| 1 | Creating Redshift Clusters | Cluster setup and connectivity with SQL Workbench/J |
| 2 | Data Loading | Table creation, data load, and table maintenance |
| 3 | Table Design & Query Tuning | Setting distribution and sort keys, deep copy, explain plans, system table queries |
| 4 | Modernize Your Data Warehouse with Amazon Redshift Spectrum | Query petabytes of data in your data warehouse and exabytes of data in your S3 data lake, using Redshift Spectrum |
| 5 | Amazon Redshift Spectrum Query Tuning | Diagnose Redshift Spectrum query performance and optimize by leveraging partitions, optimizing storage, and predicate pushdown. |
| 6 | Query Aurora PostgreSQL using Federation | Leverage the Federation capability to JOIN Amazon Redshift AND Amazon RDS PostgreSQL. |
| 7 | Amazon Redshift Operations | Step through some common operations a Redshift Administrator may have to do to maintain their Redhshift environment including Event Subscriptions, Cluster Encryption, Cross Region Snapshots, and Elastic Resize |
| 8 | Querying Nested JSON | Query Nested JSON datatypes (array, struct, map) and load nested data types into flattened structures. |
This sample code is made available under the MIT-0 license. See the LICENSE file.