I’m a data geek 🤓 In fact, I like data so much that I have made it my career! I work with Azure Data and the Microsoft Data Platform, focusing on Data Integration using Azure Data Factory (ADF), Azure Synapse Analytics, and SQL Server Integration Services (SSIS).
In this category, I write technical posts and guides, and share my experiences with certification exams. You can also find a few interviews with Azure and SQL Server experts!
Azure Data posts may cover topics like Azure Data Factory, Azure Synapse Analytics, Azure SQL Databases, and Azure Data Lake Storage. Microsoft Data Platform posts may cover topics like SQL Server, T-SQL, and SQL Server Management Studio (SSMS), and SQL Server Integration Services (SSIS).
On April 4th, 2019, I presented my Pipelines and Packages: Introduction to Azure Data Factory session at 24 Hours of PASS. I was excited to show some cool features and use cases, including how to handle schema drift in the new Mapping Data Flows feature.
Aaaaand… I failed! 🤦🏼♀️
Or, more specifically, my demo failed…
Today, I had to get a single dataset ID from a report I had deployed to the Power BI Service. I quickly realized I had no idea where or how to get it! Turns out, it’s super fast to find - if you know where to look 😅
Since I had to click around for a bit, do some searches, and get sidetracked in the REST APIs and PowerShell Cmdlets before I finally realized the ID was staring me right in the face all along, I figured I’d share this quick tip. That way, the next time I search for it, I might find my own blog post 😂 And who knows, maybe it can help one or two others?
In January 2019, I was honored to be asked to contribute to the PASS Insights BI Edition Newsletter. I said yes, of course! 😊 I chose to create an Azure Data Factory Data Flows introduction video. This is a sneak preview of the upcoming Data Flows feature, with a quick walkthrough of how easy it can be to create scalable data transformations in the cloud - without writing any code!
About a month ago, I learned something new. I learned how to run SQL Server 2019 in Docker and how to set up my demo environment in a container. Cool stuff! I like whales. Whales are cool.
While learning, I started writing this blog post. Then I got distracted and never finished it. This weekend, I had to set up my demo environment again. It was the perfect opportunity to update the content and finally publish this post.
(Why did I have to set up everything again? Oh, it’s a long story that involves disk cleanup and a Cathrine who likes to delete things to keep her computer tidy. Ok, it’s not really a long story. It was more like “oops, I accidentally deleted my container”.)
Anyway! Back to the actual content.
In this post, I share my approach and code snippets for:
- Installing Docker
- Getting SQL Server 2019
- Running SQL Server 2019 in a Docker Container
- Restoring Demo Databases (AdventureWorks and WideWorldImporters)
I knew nothing about Docker or containers a month ago. But! I’m lucky to have smart friends 🤩 Andrew Pruski (@dbafromthecold) wrote Running SQL Server 2019 CTP in a Docker container as part of his brilliant blog post series on containers.
I decided to start with his walkthrough and do exactly what he did. It worked pretty well for me! See below 👇🏻
Last year at Microsoft Ignite, I was fortunate enough to interview Mike Flasko and Sanjay Krishnamurthi. This year, I got to have a follow-up chat with Mike Flasko and Sharon Lo! We talked about the recent and upcoming Azure Data Factory updates 🤓
In this interview, Mike and Sharon share the highlights from their session at Microsoft Ignite 2018. What are visual Data Flows? How are Azure Data Factory Data Flows different from the recently announced Power BI Dataflows? What’s on the Azure Data Factory roadmap? And finally, how can you provide feedback and get involved in private previews?
Azure Data Factory Updates with Mike Flasko and Sharon Lo
(I apologize for the unsteady video 😔 Unfortunately, I didn’t see how shaky it was until post-production. If it gets too distracting to watch, please just listen. Mike and Sharon share a lot of interesting things!)
Thank you so much to Mike and Sharon for chatting with me on a busy day 😃