In the modern data-driven ecosystem, organizations are increasingly looking for systems and databases that optimally power growth and assist in making key operational decisions based on advanced analytics. The need of the hour is to have robust data processing capabilities for data drawn from both in-house and external sources.
To make this happen, businesses are increasingly shifting from traditional databases to ones that provide several cutting-edge benefits. One of the tried and tested methods in this direction in recent times is migrating databases from Microsoft SQL Server to Snowflake.
Before going to the actual process of migration of databases to Snowflake from SQL Server, it is necessary to study the two in isolation.
Read more: sql dump through postgre sql
Microsoft SQL Server
The Microsoft SQL Server database platform has been used by businesses for decades and has several leading features. It is a relational database management system that supports various applications and merges seamlessly into the overall Microsoft ecosystem. Along with Oracle Database and IBM DB2, SQL Server has always been considered among the leaders in database management systems.
Snowflake is a cloud-based and highly optimized data warehousing solution. Since it brings all the benefits of the cloud to the table, organizations are now choosing to migrate databases from SQL Server to Snowflake.
Here are some of the benefits of Snowflake.
- The key benefit of migrating databases from SQL Server to Snowflake is that data in its native format – structured, semi-structured, or unstructured – can be loaded into Snowflake. This feature is not available in other database platforms like SQL Server or Oracle.
- Storage and computing facilities in Snowflake are structured in different silos making cost calculation for each very easy. Users can therefore scale up and down in either of them by paying only for the quantum of resources used.
- Snowflake has robust computing capabilities. There is no drop or lag in performance even when multiple users are simultaneously executing multiple intricate queries.
- Being a cloud-based solution, Snowflake offers almost unlimited storage capabilities. This is another vital reason for businesses wanting to migrate databases from SQL Server to Snowflake as there is no necessity to invest in additional hardware or software whenever there is an additional need for storage.
- Snowflake provides comprehensive fully-managed services like column encoding and clustering of data automatically without defining indexes.
These advanced features make SQL Server to Snowflake database migration a viable alternative for organizations for database management.
How to Migrate Databases from SQL Server to Snowflake
Databases from SQL Server can be migrated to Snowflake in four easy steps. The full process is completely automated and can be done without any human intervention.
- The first step is to use queries for extraction to mine data from the SQL Server. Before it is extracted, the data has to be sorted and filtered through select statements. The Microsoft SQL Server Management Tool is used to extract bulk data and entire databases in text, SQL queries, and CSV format.
- The extracted data is then processed and formatted so that it matches one of the specific data structures that are supported by Snowflake. However, it is not required to first define a schema before loading JSON or XML data.
- At this stage, the processed and formatted data has to be loaded to a staging area which can be either an internal or external one. The internal stage has to be created with SQL statements and a name and type of file format have to be allotted to the location. So far as the external staging area is concerned, Snowflake currently supports Amazon S3 and Microsoft Azure. After assigning an external staging area, data can be uploaded there using any cloud interface.
- The last stage in migrating databases from SQL Server to Snowflake is loading the processed and formatted data from a staging area to Snowflake. The Data Loading Overview tool of Snowflake is used to load large and bulk databases whereas the data loading wizard of Snowflake is ideal for smaller databases. Use the PUT command to stage files for bulk databases and the COPY INTO command to load the processed data into an intended table in Snowflake from the staging area where the data is temporarily located.
After the process is completed, organizations should ensure that it does not become a one-off activity. The focus should now shift to continually load and update all changes and incremental data from SQL Server to Snowflake. It is now the responsibility of the DBAs to create a script to do so. This script should recognize new data at the source and use an auto-incrementing field as a tool to continuously update the target database.
Features of tools that Optimize SQL Server to Snowflake Database Migration
While it is understood that the complete SQL Server to Snowflake migration process is fully automated, some tools make the process that much quicker and seamless.
The tools selected should have the following features.
- Should be able to flawlessly migrate large databases without any drop in speeds or lag in performance. This is especially critical for large organizations with voluminous databases.
- Should be fully automated and complete the migration without the need for DBAs. Automated tools can effectively merge, transform, and reconcile data accurately.
- Should be able to reconcile data in Snowflake without a break between the source and the target database provided both are always kept in sync.
Using tools with these features makes the activity of SQL Server to Snowflake database migration very simple and easy.
For more valuable information visit this website