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. |