Redshift Immersion Day Labs
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 Redshift from Amazon RDS PostgreSQL||JOIN Amazon Redshift AND Amazon RDS PostgreSQL WITH dblink|
|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.