Migrate a data warehouse to a dedicated SQL pool in Azure Synapse Analytics
The following sections provide an overview of what's involved with migrating an existing data warehouse solution to an Azure Synapse Analytics dedicated SQL pool.
Before you begin your migration, you should verify that Azure Synapse Analytics is the best solution for your workload. Azure Synapse Analytics is a distributed system designed to perform analytics on large data. Migrating to Azure Synapse Analytics requires some design changes that aren't difficult to understand but that might take some time to implement. If your business requires an enterprise-class data warehouse, the benefits are worth the effort. However, if you don't need the power of Azure Synapse Analytics, it's more cost-effective to use SQL Server or Azure SQL Database.
Consider using Azure Synapse Analytics when you:
- Have one or more terabytes of data.
- Plan to run analytics on substantial amounts of data.
- Need the ability to scale compute and storage.
- Want to save on costs by pausing compute resources when you don't need them.
Rather than Azure Synapse Analytics, consider other options for operational (OLTP) workloads that have:
- High frequency reads and writes.
- Large numbers of singleton selects.
- High volumes of single row inserts.
- Row-by-row processing needs.
- Incompatible formats (for example, JSON and XML).
After you decide to migrate an existing solution to Azure Synapse Analytics, you need to plan your migration before you get started. A primary goal of planning is to ensure that your data, table schemas, and code are compatible with Azure Synapse Analytics. There are some compatibility differences between your current system and Azure Synapse Analytics that you'll need to work around. In addition, migrating large amounts of data to Azure takes time. Careful planning will speed up the process of getting your data to Azure.
Another key goal of planning is to adjust your design to ensure that your solution takes full advantage of the high query performance that Azure Synapse Analytics is designed to provide. Designing data warehouses for scale introduces unique design patterns, so traditional approaches aren't always the best. While some design adjustments can be made after migration, making changes earlier in the process will save you time later.
Performing a successful migration requires you to migrate your table schemas, code, and data. For more detailed guidance on these topics, see the following articles:
The Customer Advisory Team has some great Azure Synapse Analytics (formerly Azure SQL Data Warehouse) guidance published as blog posts. For more information on migration, see Migrating data to Azure SQL Data Warehouse in practice.
Migration assets from real-world engagements
For more assistance with completing this migration scenario, see the following resources. They were developed in support of a real-world migration project engagement.
|Data Workload Assessment Model and Tool||This tool provides suggested "best fit" target platforms, cloud readiness, and application or database remediation level for a given workload. It offers simple, one-click calculation and report generation that helps to accelerate large estate assessments by providing an automated and uniform target platform decision process.|
|Handling data encoding issues while loading data to Azure Synapse Analytics||This blog post provides insight on some of the data encoding issues you might encounter while using PolyBase to load data to SQL Data Warehouse. This article also provides some options that you can use to overcome such issues and load the data successfully.|
|Getting table sizes in Azure Synapse Analytics dedicated SQL pool||One of the key tasks that an architect must perform is to get metrics about a new environment post-migration. Examples include collecting load times from on-premises to the cloud and collecting PolyBase load times. One of the most important tasks is to determine the storage size in SQL Data Warehouse compared to the customer's current platform.|
The Data SQL Engineering team developed these resources. This team's core charter is to unblock and accelerate complex modernization for data platform migration projects to Microsoft's Azure data platform.
Watch how Walgreens migrated its retail inventory system with about 100 TB of data from Netezza to Azure Synapse Analytics in record time.
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