IntroductionUntil recently I have been using SQL Server Management Studio for developing code with SQL Server. I really love this tool, but it has grown into a comprehensive - and sometimes awkward tool to use. Azure Data Studio is a lightweight software tool that makes developing and administration of SQL Server easier than SQL Server Management Studio. ADS can be used on multi platforms like MacOS, Linux and Windows and is integrated with Git. You can find more information here.
So in short Azure Data studio has the following interesting features :
- Different kernels like SQL and python.
- Code snippets.
- Integration with Source control.
- Powershell support.
Installation of Azure Data Studio
First let's start with downloading the Azure Data Studio from the download location. Here you can find the installation files for Linux, MacOS en Windows. I choose the windows User installation files of the latest version (May 2019, version 1.7.0). The installation is fairly easy and it's a matter of Next, Next and Next.
The initial screen is a simple screen where you can set the database connection.
After you set the database connection strings you're set to go using the Azure Data Studio.
Executing a scriptThe first thing I tried is executing a script in ADS.
I checked the error messages in SSMS and they are exactly the same.
Searching objectsFinding objects in ADS is a bit different than in SSMS. You can find objects by using prefixes like t: for tables and sp: for stored procedures.
Browsing objects on a server is also possible.
NotebooksNotebooks are new in Azure Data Studio. I know about notebooks because of I use them during jobs, R Courses and AI courses (jupyter). It is an easy way to share code. I like the way notebooks work. It's like telling story with code all together. Who hasn't joined a project with nothing else but code. Wouldn't it be great when thoughts, decisions are well written in a story together with the code. I'm note sure whether developers are the targetted people of notebooks, I think that people who work with data like data scientists and analysist will appreciate this functionality very much
There are a couple of options creating a notebook. One option is with the menu option and another way is to use the command palette. I choose the latter one. Yet another surprise is that you can write in Spark | R, Python and Pyspark in Azure Data Studio. These are kernels.
Creating a notebook is easy to do. You can add codeblocks and you can add text(blocks) and that by each other. It is possible to have multiple lines of code in a code block.
The notebook is saved as a .ipynb extension and that is can be used in Microsoft Azure notebooks.
Code snipppetsAdding code snippets is very easy in Azure Data Studio. Open a query and type in sql. Typing sql will open a dropdown menu where it is possible to choose a template.
In this particular case I'll download git and install git in a standard manner.
Next step is creating a working folder. Click on open Folder and locate the folder you want to work from and click OK. In my case, I'm using D:\Git. Now it is possible to use the git integratoin in ADS.
After initializing git the following git options are available.
It seems there is not a native support for Azure DevOps yet. It's possible to download extensions where you add support for Azure DevOps.
Final thoughtsI've scratched the surface on how to work with Azure Data Studio aka ADS. It is an interesting tool to use and I'll decide in the near future whether I'm going to leave SSMS and use ADS. Time will tell.
One thing I noticed is that ADS is quite CPU intensive. I'm using a fairly old laptop with a VM and it happens that the CPU is sky high on 100% and that problem doesn't occur with SSMS. Probably it's my old laptop that gives this problem.