With the increasing volumes of data generated by modern businesses, organizations are now looking for technologically advanced database platforms to optimize their data management functionalities. One of the ways to do so fast is to migrate existing databases from traditional SQL Server to Snowflake, a recently-introduced cloud-based data warehousing solution.
Before going to the steps in detail on how to do so, a look at the respective platforms will be in order.
Microsoft SQL Server
Microsoft SQL Server is a combination of Structured Query Language (SQL) and Relational Database Management System (RDBMS) primarily used to store and retrieve data. A lower version caters to freeware and a data center version ensures support to higher levels and scale of application. The advantage here is that applications are supported either across the web or on a local area network on a single machine with SQL Server matching seamlessly with the full Microsoft ecosystem.
Snowflake is a cloud-based data warehousing solution offered as a Software-as-a-Service product, running on the popular cloud provider Amazon Web Service. There are several benefits of this platform and is the reason why enterprises want to migrate databases from SQL Server to Snowflake.
Snowflake allows the migration of both structured and unstructured data including JSON, AVRO, XML, and PARQUET data. Snowflake is a high-performing platform and multiple workgroups can work on multiple workloads at a time without experiencing any lag or slowdown. It also offers separate flexible storage and computing capabilities. Users can work and scale up and down in either of them, paying only for the quantum of resources used in each.
Additionally, as the Snowflake architecture is cloud-based, it supports a wide range of cloud vendors. Hence, users can work with the same set of tools in Snowflake while working with various vendors. Finally, in Snowflake, data is automatically clustered and separate indexes do not have to be specified. However, the clustering keys of Snowflake have to be employed when working with very large tables to co-locate table data.
With all these cutting-edge benefits, it makes sense for businesses to migrate databases from SQL Server to Snowflake.
Migrating database to Snowflake from Microsoft SQL Server
Several steps need to be followed to make the migration process a smooth and easy affair. However, it is preferable to contact the experts if DBAs are not aware of it.
- Mining data from SQL Server – The first step is to extract data from SQL Server and the most common method is queries for extraction. Select statements are used for sorting, filtering, and limiting the data while mining it. To extract bulk data and large databases, Microsoft SQL Server Management Studio is used for exporting complete databases in CSV, SQL queries, or text format.
- Processing the extracted data – The data extracted has to be first processed and formatted. It cannot be migrated directly into Snowflake after extraction. It has to be verified that the data structures supported by Snowflake match that of the mined data. For JSON or XML data though, a schema need not be specified before migration.
- Loading data to a temporary location – Even after the extracted data has been processed and formatted it cannot be loaded directly into Snowflake. The data has to be kept in an internal or external staging area. An internal staging area is customized with SQL statements. In this method, there is greater flexibility as users can allot file formats and other options to the named stages. External stages are the locations that are supported by Snowflake and where the data can be uploaded using their specific interfaces. Currently, the staging areas supported by Snowflake are Amazon S3 and Microsoft Azure.
- Migrating data into Snowflake – The database from SQL Server to Snowflake is now ready for loading into Snowflake from the staging area where it is located. For loading bulk and large databases, the Data Loading Overview tool of Snowflake is used. The PUT command is used to stage files, the COPY INTO command to load processed data into an intended file, and the Copy command for migrating data from an external staging area. For small databases, the data loading wizard of Snowflake may be applied.
Once the database is loaded to Snowflake, provision should be made for the system to only load changes and incremental data and not take up lengthy full data refreshes.