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 connecting with Query Editor
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 in S3 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.
15 Loading & querying semi-structured data Load semi-structured JSON data into Redshift and manage schema evolution by using the SUPER data type.
16 Redshift Data API Query data via the Redshift Data API by creating Python function in AWS Lambda which is exposed to the API Gateway.
17a Machine Learning using Redshift ML Create a model using auto features.
17b Machine Learning using Redshift ML Create a model specifying PROBLEM_TYPE and OBJECTIVE.
17c Machine Learning using Redshift ML Create a model and provide the MODEL_TYPE , OBJECTIVE, PREPROCESSORS and HYPER PARAMETER.
18 Power BI with Redshift Create dashboards using Power BI on data stored in Amazon Redshift
19 Amazon Sagemaker Data Wrangler Prepare Data For Machine Learning Using Amazon Sagemaker Data Wrangler and Amazon Redshift.

We would love to hear from you

Please let us know if there's a way we can make this lab better, if there is a lab you would like added, or if you want help getting started with your Redshift deployment.