{"id":88717,"date":"2020-10-20T00:30:20","date_gmt":"2020-10-20T00:30:20","guid":{"rendered":"https:\/\/www.red-gate.com\/simple-talk\/?p=88717"},"modified":"2020-10-20T00:42:53","modified_gmt":"2020-10-20T00:42:53","slug":"connecting-log-analytics-using-azure-data-studio-kql","status":"publish","type":"post","link":"https:\/\/www.red-gate.com\/simple-talk\/blogs\/connecting-log-analytics-using-azure-data-studio-kql\/","title":{"rendered":"Connecting to Log Analytics using Azure Data Studio and KQL"},"content":{"rendered":"

Azure Data Studio<\/strong> is a great tool and supports way more than only SQL. Recently, Azure Data Studio<\/strong> included the support to Kusto language, or KQL<\/strong>.<\/p>\n

Kusto is a query language used for massive amounts of streamed data, perfect for logs. That’s why Kusto was initially created for Azure Monitor<\/strong> and Azure Log Analytics<\/strong>.<\/p>\n

However,\u00a0Azure Data Studio <\/strong>only supports Kusto connections with\u00a0Azure Data Explorer<\/strong>, not\u00a0Log Analytics<\/strong>. All this huge focus on Azure Data Explorer<\/strong> given by Microsoft still puzzles me. Log Analytics<\/strong> is a ready-to-use monitoring solution for cloud and on-premises environment.<\/p>\n

On the other hand,\u00a0Azure Data Explorer<\/strong> is a database storage MPP (Massive Parallel Processing) solution for massive stream storage – logs – allowing anyone to create their own monitoring solution using it as a storage, but not a ready-to-use solution as Log Analytics<\/strong>.<\/p>\n

Considering these differences, the use cases for Log Analytics<\/strong> seems related to migrations to the cloud and all the following monitoring work.\u00a0Log Analytics<\/strong> can be a fundamental tool on the process of cloud adoption, because it gives numbers to the process. These numbers allow the IT Administrators to do in the cloud what only a few really manage in an organized way: Keep baselines and compare results after every environment change.\u00a0<\/p>\n

On the other hand, software houses creating custom solutions or, in some situations, solution providers trying to manage a big number of clients, may find\u00a0Azure Data Explorer<\/strong>\u00a0as a better solution for them. It puzzles me that one receives more attention than the other. What do you think? Let’s talk about on the comments.<\/p>\n

Now it’s time to go direct to the point. Of course, you already noticed how I got disappointed by discovering Azure Data Studio<\/strong> couldn’t connect to Log Analytics<\/strong>. However, with some help from Microsoft community on twitter I was able to find a work around: We can use KqlMagic<\/strong>, a Python<\/strong> package, to connect to Log Analytics<\/strong><\/a>.<\/p>\n

Yes, Azure Data Studio<\/strong> is so flexible it supports Python and we can install Python packages.<\/p>\n

The Challenges and What Doesn’t Work<\/h2>\n

If you are thirsty for the solution, you can skip this part and go to the solution a bit below on the blog. However, I like to explain how I reached the solution and what didn’t work. At the bare minimum, you will find some great links and ideas.<\/p>\n

What may appear easy in the beginning, is not at all. Let’s face the challenges:<\/p>\n