SSIS Amazon Redshift ExecuteSQL Task (AWS)

Run Amazon Redshift SQL commands in SSIS using a dedicated ExecuteSQL task.

Execute DDL and DML statements such as CREATE, ALTER, COPY, UNLOAD, UPDATE, and DROP without custom code. Configure connection and SQL command flow in SSIS Control Flow for repeatable warehouse automation.
Part of ZappySys SSIS PowerPack.

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Pro Tip: The Visual Studio download installs a basic extension. Use the guide above to install and run the full SSIS PowerPack (all connectors & UI).

Compatibility: SQL Server 2012–2025+ (32/64-bit) | Azure Data Factory / Azure-SSIS IR Ready

⚡ Key Benefits

Task-Based SQL Automation — Execute Redshift SQL as a first-class SSIS control-flow task
DDL + DML Support — Run CREATE, ALTER, DROP, COPY, UNLOAD, and UPDATE commands
No ODBC Scripting — Avoid custom scripts for common admin and data operations
Connection Reuse — Use configured Redshift connections across package steps
Operational Control — Integrate SQL actions into orchestrated ETL workflows

📸 Visual Overview & Setup

1. Drag Redshift ExecuteSQL Task 🔍 View Full Size	2. Configure Redshift Connection 🔍 View Full Size	3. Enter Direct SQL Command 🔍 View Full Size
4. Run Package Execution 🔍 View Full Size	5. Review Full Settings UI 🔍 View Full Size	6. Advanced Settings UI (Tab 2) 🔍 View Full Size

🔧 Key Features

Execute Redshift DDL and DML from SSIS control flow
Support for CREATE/ALTER/DROP/COPY/UNLOAD style commands
Reusable Redshift connection manager configuration
Task-driven orchestration across package steps
Fits admin automation and pre/post-load SQL workflows

💡 Common Use Cases

Pre-Load Setup: Create staging tables before data transfer tasks run.
Post-Load Processing: Execute merge/update/grant statements after loads.
Maintenance Automation: Run cleanup and schema-change scripts on schedule.
Operational Control: Include COPY/UNLOAD command steps in ETL orchestration.

🎯 Summary

SSIS Redshift Execute SQL Task brings SQL command automation into control flow so teams can manage Redshift schema and data operations without custom scripting. It is ideal for production-ready, repeatable ETL orchestration.

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