My development activities require me to have various desktops open which can become confusing, especially if you keep to the default desktop image. The following two resources have worked best for me in changing up my desktop display.
For those DBAs are using SQL for data discovery, the move to data science can involve a brand-new set of varied tools and technologies. This article is a walkthrough of setting up the tooling to do some data discovery using Python. By setting up your workflow using, GitHub, VSCode and Python you will have the basic architecture set up for Data Exploration.
This first article in the Introduction to Data Analysis series will walk you through setting up Microsoft's free Office Online and introduce you to using Excel Online with OneDrive.
This post covers the automation and creation of an; Azure Resource Group, Blob storage, Azure SQL Server and an Azure Analysis Services in a PaaS offering using Azure Automation PowerShell Runbooks.
This article will review a major trap you can find yourself in when using PowerShell in your Runbooks as Azure Automation does not keep your referenced modules up to date.
Power BI lacks the ability to add KPI calculations when creating a data model in Power BI Desktop. I will show you how to get the same functionality by using the recently released feature “Conditional Formatting by a Different Field”. This will allow you to take a calculated score and have an Icon displayed for each row in your table. A working Live Interactive Power BI Sample is attached at the bottom of this post.
There is an odd error when trying to install SQL Server Data Tools (SSDT), either as a stand-alone, or adding to an instance of Visual Studio 2017. I had the same setup failure, no matter what configuration I was using. The following steps worked for me, and may for your situation.
This article reviews the process of using Azure Data Factory V2 sliding windows triggers to archive fact data from SQL Azure DB. This process will automatically export records to Azure Data Lake into CSV files over a recurring period, providing a historical archive which will be available to various routines such as Azure Machine Learning, U-SQL Data Lake Analytics or other big data style workloads.