This page provides you with instructions on how to extract data from Microsoft Azure and analyze it in Superset. (If the mechanics of extracting data from Microsoft Azure seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Microsoft Azure?
Microsoft Azure is a cloud services platform that developers can use to build, deploy, and manage applications. Several databases can run on the Azure platform, including Microsoft Azure SQL Database, Azure Database for MySQL, and Azure Database for PostgreSQL.
Getting data out of Azure
In most cases, the easiest way to retrieve data from relational databases is by writing SQL queries. Alternatively, you can use SQL Server Server Management Studio to export data in bulk as delimited text, CSV files, or SQL queries that would restore the database if run.
Preparing Azure data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Azure's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
Keeping data from Azure up to date
At this point you've successfully moved data into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Azure.
And remember, as with any code, once you write it, you have to maintain it. If Azure sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Microsoft Azure to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Microsoft Azure data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Microsoft Azure to Redshift, Microsoft Azure to BigQuery, Microsoft Azure to Azure SQL Data Warehouse, Microsoft Azure to PostgreSQL, Microsoft Azure to Panoply, and Microsoft Azure to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Microsoft Azure with Superset. With just a few clicks, Stitch starts extracting your Microsoft Azure data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.