Redshift Immersion Labs


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 set of workshops provides a series of 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 a Cluster Cluster setup and connectivity with SQL Workbench/J
2 Data Loading Table creation, data load, and table maintenance
3 Table Design and Query Tuning Setting distribution and sort keys, deep copy, explain plans, system table queries
4 Modernize w/ Spectrum Query petabytes of data in your data warehouse and exabytes of data in your S3 data lake, using Redshift Spectrum
5 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 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.
9 Use SAML 2.0 for SSO with Redshift Enable SSO using the Redshift BrowserSAML plugin with any SAML 2.0 provider.
10 Speedup predicative model training with Redshift Learn how to use Redshift to do Data Wrangling and speedup machine learning use case.
11 Oracle to Redshift Migration Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (DMS) to migrate data and code from an Oracle database to Amazon Redshift.
12 SQL Server to Redshift Migration Use AWS Schema Conversion Tool (AWS SCT) to migrate data and code from a Microsoft SQL Server database to Amazon Redshift.
13 ETL/ELT Strategies Modernize your ETL/ELT process using Materialized Views, Stored Procedures, and Query Scheduling.
14 Data Sharing Isolate your workloads by sharing data between 2 Redshift clusters.