Data Adventures

(2019-June- 06) I remember that I had a professor at my university who would often encourage his students to learn and get more experienced with simple things first. Learn the basics in and out and then move forward to more complicated concepts and practices; that was his motto, and he really tried to share this idea with us. Previously published blog posts about using Variables in Azure Data Factory: - Setting Variables in Azure Data Factory Pipelines - Append Variable activity in Azure Data Factory: Story of combining things together - System Variables in Azure Data Factory: Your Everyday Toolbox - Azure Data Factory: Extracting array first element Simple things sometimes can be overlooked as well. With the addition of Variables in Azure Data Factory Control Flow (there were not available there at the beginning), Arrays have become one of those simple things to me. Image by Magnascan from Pixabay

Executing Azure Data Factory pipelines by Power App / Automate Flow

(2021-Jun-30) One of my university professors liked to tell us a quote, “ The Sleep of Reason Produces Monsters ”, in a way to help us, his students, to stay active in our thinking process. I’m not sure if Francisco Goya , had a similar aspiration when he was creating his artwork with the same name. So, let me explain my reasons to create a solution to trigger Azure Data Factory (ADF) pipelines from a Power App and why it shouldn't be considered as a monster :-)

Transforming JSON data with the help of Azure Data Factory - Part 4 - Passing Arrays around

(2020-May- 24) It has never been my plan to write a series of articles about how I can work with JSON files in Azure Data Factory (ADF). While working with one particular ADF component I then had discovered other possible options to use richness and less constrained JSON file format, which in a nutshell is just a text file with one or more ( " key " : " value " ) pair elements. Sometimes you could have an array of those pair elements or an array of other arrays; now you see where I'm going with the name of my current blog post :-)

Passing array of arrays as a parameter in Azure Data Factory and the role of an @item element in this

(2020-Sep- 13) Array of arrays, in a JSON world, it’s a very common concept to have a set of sets of other elements in a dataset. Despite how strange it may sound, our real life is filled with similar analogies: books arranged by categories in a library, plates stacked up in a cupboard, or even someone’s closet has many sections that are filled with different clothing items. Arrays are part of our daily life too :-) Photo by Lovefood Art from Pexels

Blog post about Copying Azure files to SharePoint, comparing Logic App and Power Automate Flow for this, and Trust No One

(2021-May-31) Recently, I created a prototype solution for one of the projects, that would copy data files from Azure to SharePoint location. The task is trivial, doesn't require a lot of effort to build such a workflow with the help of the Azure Logic App. However, SharePoint itself is like another ecosystem and it may throw a curveball to me and slow down my solution development timeline dramatically. This post is a reminder to myself on how I had spent 5% of my time to develop this solution and then the other 95% to understand why it wasn't working initially to later resolve it.

Setting default values for Array parameters/variables in Azure Data Factory

(2020-Aug-14) Setting default values for my parameters or variables in Azure Data Factory (ADF) may be a trivial task, but it gets more interesting when those elements are Arrays. The recent addition of Global Parameters to the Data Factory by Microsoft may create an extra thrill to learn how to set values for such parameters too: https://docs.microsoft.com/en-ca/azure/data-factory/author-global-parameters . Photo by Mihis Alex from Pexels

Azure Data Factory: Extracting array first element

(2019-Apr- 28) Full credit goes to Microsoft for the latest efforts updating Azure products with new features and documenting corresponding changes for the end users. Azure Data Factory (ADF) is a great example of this. A user recently asked me a question on my previous blog post ( Setting Variables in Azure Data Factory Pipelines ) about possibility extracting the first element of a variable if this variable is set of elements (array). So as a spoiler alert, before writing a blog post and adding a bit more clarity to the existing Microsoft ADF documentation, here is a quick answer to this question. The first element of an ArrayVariable is extracted by this expression: @variables(' ArrayVariable ')[0] . I created a simple ADF pipeline to test this out:

Azure DevOps: Merging code to Main branch from a specific branch only

(2022-July-25) I really liked reading printed PC Magazines ( https://www.pcmag.com/ ) or Computerworld newspapers ( https://www.computerworld.com/ ) when I was in school. Those magazines pictured IT System or Database Admins as a special group of IT professionals. Big IT vendor paper ads usually portrayed them in a clean business-casual dress code with button-down collar shirts being surrounded by tall server racks with flickering lights. On another side, if you were part of the development or data analyst team without sufficient knowledge of IT infrastructure maintenance and management practice, then connecting with such an elite workforce team and seeking their help was slightly difficult, however, that wasn’t always the case. Things change: print editions of magazines are discontinued and the dress code is no longer a required sign of an IT group membership. So in real life, one of the ways to fight this “imposter” syndrome is to step out of my comfort zone and learn new things...

Continuous integration and delivery (CI/CD) in Azure Data Factory using DevOps and GitHub

(2019-Feb- 18) With Azure Data Factory (ADF) continuous integration, you help your team to collaborate and develop data transformation solutions within the same data factory workspace and maintain your combined development efforts in a central code repository. Continuous delivery helps to build and deploy your ADF solution for testing and release purposes. Basically, the CI/CD process helps to establish a good software development practice and aims to build a healthy relationship between development, quality assurance, and other supporting teams. Update 2020-Mar-15: Part 2 of this blog post published in 2020-Jan-28: http://datanrg.blogspot.com/2020/01/continuous-integration-and-delivery.html Update 2019-Jun- 24: Video recording of my webinar session on Continuous Integration and Delivery (CI/CD) in Azure Data Factory at a recent PASS Cloud Virtual Group meeting.

Data Masking with Azure Databricks

“ Alice : Would you tell me, please, which way I ought to go from here? The Cheshire Cat : That depends a good deal on where you want to get to.” — Lewis Carroll, Alice’s Adventures in Wonderland (2025-Jan-13) Working on the data masking project has been a long journey; writing about it will take some time. I’m fine if you’re not a big fan of long reads and prefer scrolling, skipping, or engaging in shorter, bite-sized learning experiences. I will keep this writing for myself as a chronicle of my journey along a path that may seem overwhelming. However, the experience throughout this journey and the joy of reaching the final destination will be hard to forget. When I think of data masking, or the term "masking" in particular — the intention to “hide” something from someone comes to mind. I picture a man in a mask, presumably to conceal his identity, or an image blurred beyond recognition. In the world of computer data, masking represents the process of obfuscating, redacting...

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