I'm a data engineer and Microsoft Data Platform MVP. I specialise in building and automating analytics platforms using Microsoft technologies from the SQL Server stack to the Azure cloud. I’m a fervent advocate of DataOps, with a technical focus on bringing automation to both analytics development and operations. An active member of the data community and keen knowledge-sharer, I'm a volunteer, organiser, speaker, blogger, open-source contributor and author – my book Azure Data Factory by Example was published by Apress in June 2021. I have a PhD in Computer Science and participate enthusiastically in choral singing, craft ale and cocktails, most successfully in that order.

Contact me at:

• richard@RichardSwinbank.net

• LinkedIn

• Twitter

• Mastodon

A multi-part series on creating a pro dev experience for Power BI, aimed at bringing DevOps or DataOps approaches to Power BI report development. In the series I'll be assembling an approach to creating developer workflows that provide meaningful version control and automate deployment to multiple environments. I'll be writing a new post every couple of weeks and will link them here when they're done:

1. Pro DevEx for Power BI
2. Better version control for Power BI datasets
3. Publish automatically to Power BI environments with Azure DevOps pipelines
4. Reusable deployment pipelines for Power BI
5. Working with multiple Power BI dataset environments
6. Create Power BI deployment pipelines automatically
7. Deploy TMDL datasets to Power BI
8. Better version control for Power BI reports
9. Automated testing for Power BI datasets

Hit the ground running in Azure Data Factory v2 (ADF) with my book Azure Data Factory by Example. The tutorial-first approach gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft’s cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components.

The hands-on introduction to ADF found in this book is equally well-suited to data engineers embracing their first ETL/ELT toolset as it is to seasoned veterans of Microsoft’s SQL Server Integration Services (SSIS). The example-driven approach leads you through ADF pipeline construction from the ground up, introducing important ideas and making learning natural and engaging. SSIS users will find concepts with familiar parallels, while ADF-first readers will quickly master those concepts through the book’s steady building up of knowledge in successive chapters. Summaries of key concepts at the end of each chapter provide a ready reference that you can return to again and again.

A six-part series on test automation for Azure Data Factory pipelines. Automated testing is a key component of CI/CD development pipelines: it allows you to run more tests, more often, more quickly, with guaranteed repeatability. In the series I build up an approach to writing tests for ADF and triggering them automatically.

1. Set up automated testing for Azure Data Factory
2. Automate integration tests in Azure Data Factory
3. Isolated functional tests for Azure Data Factory
4. Testing Azure Data Factory in your CI/CD pipeline
5. Unit testing Azure Data Factory pipelines
6. Calculating Azure Data Factory test coverage

If you're wondering if all this testing is worth the trouble, I have some thoughts on the subject here 😃.

Sprockit is a powerful but lightweight ETL process controller for managing SSIS package and T-SQL stored procedure execution. It's free, open-source and written purely in T-SQL. You can download it here.