Modern-day businesses are entirely data-driven. Any critical operating decisions have to be made after processing, formatting and analyzing massive volumes of data from both in-house and external sources. Hence, the focus is always on ensuring that data is processed not only speedily but also at minimum costs. One of the most common and popular ways to do so is to move databases to the cloud from traditional systems. Migrating databases from Microsoft SQL Server to Snowflake is one such optimized possibility.

Before going into the intricacies of SQL Server to Snowflake database migration an understanding of the two main components is necessary to better know about the need for this step in the present-day business environment.


Microsoft SQL Server

Microsoft SQL Server is a Relational Database Management System (RDMS) that matches the Microsoft .NET framework out of the box. It integrates seamlessly into the Microsoft ecosystem, supporting applications either on a local network across the web or on a single machine. SQL Server supports a range of business operations and analytics that are critical for business operations. The database is based on the programming language SQL that is popular among DBAs. Along with the Oracle database and IBM’s DB2, Microsoft SQL Server has long been among the leading database technologies in the world.

Snowflake

Snowflake is a cloud-based datawarehousing solution. Within a short time of its launch, it had resolved many issues that were inherent in the existing database systems. This, coupled with the many benefits of the cloud has made organizations want to migrate their databases from Microsoft SQL Server to Snowflake.

Given below are some of the benefits of Snowflake.

  • Snowflake supports a range of cloud vendors as its architecture is compatible with them. Hence, users can use the same tools to work on all or any of them.
  • Unlike traditional systems, Snowflake offers separate computing and storage facilities. Hence, businesses can easily identify the costs of using each of them separately. Further, users can seamlessly scale up or down in resource usage by paying only for the quantum used. This reduces the costs of running IT infrastructure making SQL Server to Snowflake database migration a very attractive proposition.
  • Not only does Snowflake provide unlimited storage space due to its cloud-based structure, but it also offers high computing powers. There is no lag in performance or drop in speeds even when multiple users simultaneously execute multiple intricate queries.
  • One of the critical benefits of Snowflake, when compared to other databases, is that all types of data can be migrated to the platform, an attribute not available in others. This includes unstructured, semi-structured, and structured data and built-in support to JSON, Parquet, Avro, and XML data.
  • Snowflake offers automated support for clustering data and indexes do not have to be defined for it. Other tasks like encoding of columns and computing are automatically carried out also. However, it is necessary for users to manually use the clustering keys of Snowflake for very large tables to co-locate table data.

These are some of the cutting-edge advantages of this data warehousing solution that encourage businesses to migrate databases from SQL Server to Snowflake.

The Process of Migrating Databases from SQL Server to Snowflake

There are four steps for migrating databases from SQL Server to Snowflake, a process that is largely automated and does not require any human intervention.

  • The first step is mining and extracting data from the SQL Server. This is done through queries for extraction by select statements that are used during the retrieval process to sort, filter, and limit the data. However, the Microsoft SQL Server Management tool is used while extracting large databases in CSV, text, or SQL queries format.
  • The data that is extracted from the SQL Server is in raw form and cannot be exported directly into Snowflake. It has to be formatted and processed to match the data structures that are supported by Snowflake. The exceptions are JSON and XML data where a schema need not be specified before migration.
  • Even now, this MySQL formatted data cannot be loaded into Snowflake but has to be kept temporarily in a staging area. It can either be an internal or an external staging location.

An Internal staging area is created with SQL statements and offers a great deal of flexibility to users while migrating from SQL Server to Snowflake. This is because it is possible to create file formats and other options to name stages as per specific organizational requirements.

The External staging area is where the formatted data is uploaded through the respective cloud interface. Currently, Snowflake supports Amazon S3 and Microsoft Azure as external staging areas.

  • The final step in SQL Server to Snowflake data migration is loading data from a staging area to Snowflake. For small databases, the data loading wizard of Snowflake is used while the Data Loading Overview of Snowflake takes care of loading large databases and bulk data. The PUT command stages files while the COPY INTO command loads processed data into an intended table.

Users should not stop the process here. A provision should be made so that any changes and updates to data at the source are automatically loaded to the target systems. Create a script that will track incremental data and use an auto-field to update the target database continually. Only then will the process of SQL Server to Snowflake data migration be deemed to be completed.

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