The Need to Transfer Data from SQL Server to Snowflake
Why are organizations opting to transfer data from Microsoft SQL Server to Snowflake? What are the steps required to go through the process?
Microsoft SQL Server supports applications on a local area network or across the web on a single machine and blends easily into the Microsoft ecosystem. Snowflake, on the other hand, is a recently introduced cloud-based data warehousing solution that resolves many issues that were hitherto hurdles in traditional systems.
There are multiple benefits of Snowflake, reasons why organizations now prefer to load data from SQL server to Snowflake.
•Snowflake architecture supports a wide range of cloud vendors and users can use the same set of tools to work with and analyze data from different vendors.
•Storage and computing facilities are separated and users can scale up or down according to their requirements, making payments only for resources used.
•Snowflake automatically clusters data and no indexes are to be defined. But when users work with large volumes of data Snowflake’s clustering keys are utilized to co-locate table data.
•The same set of data can be accessed by multiple workgroups for multiple workloads simultaneously without any dip in performance or speed.
•Both structured and unstructured data can be loaded natively to Snowflake. This data warehousing solution supports JSON, Avro, XML, AND Parquet data.
It is now obvious why businesses would want to work with this current generation cloud-based data warehousing solution.
Follow these steps to load data from SQL server to Snowflake.
•The first is mining data from Microsoft SQL Server through queries for extraction. Select statements are used to sort, filter, and limit the data being retrieved. Microsoft SQL Server Management Studio may be used to export bulk data or entire databases.
•The extracted data has to be processed and prepared for loading. It should be ensured that the data structure in Microsoft SQL Server matches the data types supported by Snowflake. However, this is not necessary when loading JSON or XML data into Snowflake.
•Data files have to be loaded now into a temporary location before they can be transferred to Snowflake. This process is called Staging. There are two components here. The first is the Internal Stage which is created with respective SQL statements and offers a great degree of flexibility while loading data. The second is the External Stage. Amazon S3 and Microsoft Azure are the locations presently supported by Snowflake.
•Finally, the Data Loading Overview tool in Snowflake guides users through the process to load data from SQL server to Snowflake. A PUT command is used to stage files and COPY INTO command to load processed data into an intended table. At first glance, the whole process might seem overwhelming but in reality, it can be completed in a few clicks only.