This page provides you with instructions on how to extract data from Microsoft Azure and load it into Azure Synapse. (If this manual process sounds onerous, 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.
What is Azure Synapse?
Azure Synapse (formerly Azure SQL Data Warehouse) is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.
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.
Loading data into Azure Synapse
Azure Synapse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:
- AZCopy uses the public internet.
- Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
- The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.
From Azure Storage you can load the data into Azure Synapse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.
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.
Other data warehouse options
Azure Synapse is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Panoply, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Panoply, To S3, and To Delta Lake.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from Microsoft Azure to Azure Synapse automatically. With just a few clicks, Stitch starts extracting your Microsoft Azure data, structuring it in a way that's optimized for analysis, and inserting that data into your Azure Synapse data warehouse.