Why Should You Migrate Databases from Microsoft SQL Server to Snowflake


The management of databases in the modern business environment has moved away from traditional systems to more advanced and cutting-edge technologies. Organizations depend greatly on data analytics to drive growth and take key decisions that increase operational efficiencies.

Hence, Database Administrators should have access to tools that make the handling of databases a smooth and seamless affair.

Data processing today is not only complex but requires collation from various internal and external sources. To optimize this process, it becomes essential to load and migrate databases from the current systems to those that provide a distinct edge over the others. To reach this goal, more and more businesses are choosing to migrate databases from Microsoft SQL Server to Snowflake.

Before going into the method of loading databases from SQL Server to Snowflake, it is necessary to understand the intricacies of each platform. It will make it easier to know why this migration is necessary to meet the requirements of modern data-driven businesses.

Microsoft SQL Server

Microsoft SQL Server has been the mainstay of businesses for decades in the area of database management. It has excellent features, integrates easily into the entire Microsoft ecosystem, and supports the .NET framework out of the box.

A Relational Database Management System (RDMS), SQL Server supports all applications on a single machine regardless of whether they are working across the web or locally. The server is built on the widely-used programming language SQL and is considered to be at par with other databases like DB2 from IBM and Oracle databases.

The question now is that despite what SQL Server has to offer why do businesses want to migrate databases from SQL Server to Snowflake. The answer is simple – it is because of the many benefits that Snowflake brings to the table that is critical for operating today’s databases.


Snowflake is a data warehouse that is based in the cloud. It is built on top of AWS (Amazon Web Service) and Microsoft Azure, both platforms being cloud-based too. It assists businesses to get all the benefits of the cloud for their databases without having to install or configure additional hardware or software.

Since all servers of Snowflake operate from the cloud, it offers fully-managed services like setup, maintenance, and support of in-house servers.

Here are some of the benefits offered by Snowflake that make migration from SQL Server to Snowflake a very attractive proposition for organizations.

  • Snowflake supports several cloud vendors and hence, users can capably work on one or any of them with the same tools and skillsets.
  • Data in its native format – unstructured, semi-structured, or structured – can be loaded into Snowflake. This feature is not available in SQL Server and is one of the main reasons for migrating databases from SQL Server to Snowflake.
  • The platform offers unmatched computing speeds and there is no drop in performance even when multiple users simultaneously execute multiple intricate queries.
  • Snowflake provides unlimited storage facilities with users having to pay only for the quantum of resources used. Compared to flat fees of other databases, this leads to substantial savings in storage costs.
  • An important benefit provided by Snowflake is that it offers fully-managed services ranging from clustering data automatically without defining indexes to the encoding of columns. The option of co-locating data through cluster keys is available for very large tables.

These are some of the reasons why businesses migrate databases from SQL Server to Snowflake.

How to Migrate Data from Microsoft SQL Server to Snowflake

There are four steps in migrating data from SQL Server to Snowflake.

  • Data Extraction: The first step is extracting the data to be migrated from SQL Server through queries for extraction. Before extraction, the data has to be sorted and filtered through select statements. The Microsoft SQL Server Management Studio tool is used to extract bulk data or entire databases in CSV, SQL queries, or text format.
  • Processing data: The data that is extracted cannot be loaded directly during SQL Server to Snowflake database migration but has to be processed and formatted first so that it matches a data type that is supported by Snowflake. However, this step is not required to specify a schema before loading JSON or XML data.
  • Keeping data in a staging area: Even after the data is formatted to match a structure that is supported by Snowflake, it still cannot be loaded directly on the platform but has to be kept temporarily in a staging area. The staging area can either be an internal or an external one. An internal staging area has to be created with SQL statements and a name and file format allotted to it. For the external staging area, the Snowflake architecture supports only Microsoft Azure or Amazon S3 (Simple Storage Service).
  • Loading the data to Snowflake: The final stage in SQL Server to Snowflake database migration is loading the data from one of the staging areas where the data is located to the Snowflake platform. For large databases, the Data Loading Overview tool is used whereas for small databases, the data loading wizard of Snowflake is ideal. Commands used in the process are PUT for staging files for bulk databases and COPY INTO to load the processed data into a table in Snowflake from the internal or external staging areas where the data is located.

However, the SQL Server to Snowflake database migration process should not stop here but go on to loading changes and incremental data too. For this, a script has to be created that will recognize any changes in the source database and use an auto-incrementing field to continually update the data in the target database.