Organizations today need technologically advanced database platforms to meet their data management requirements. One of the preferred routes taken is migrating databases from the existing traditional systems to the modern cloud-based ones such as Microsoft SQL Server to Snowflake.
Microsoft SQL Server
Microsoft SQL Server has been the database workhorse for decades now and has many leading features. It integrateseasily into the Microsoft ecosystem and backs up the .NET framework out of the box. SQL Server is a relational database management system and supports all applications at one source regardless of where they are located. SQL Server is ideal for database transactions and analytics and is considered to be equal in performance to other industry leaders such as IBM DB2 and Oracle Database. The server is designed on the programing language SQL mostly used by DBAs to query data in the databases.
Before going into the attributes of migrating data from SQL Server to Snowflake, a quick look at Snowflake will be in order.
Snowflake is a comparatively recently-introduced data warehousing solution that is based in the cloud. It is compatible with a wide range of cloud vendors and users can work on one or more of them with the same set of tools. Both unstructured, semi-structured, and structured data can be loaded natively directly into Snowflake, a critical feature that is not available in Oracle database or SQL Server.
Some of the other features of Snowflake are as follows.
- Snowflake offers high computing capabilities. Multiple users can simultaneously execute multiple intricate queries without facing any performance degradation.
- The computing and storage capabilities in Snowflake are in different silos. Hence users can scale up and down in either of them by paying only for the quantum of resources used leading to substantial savings.
- Since this solution is cloud-based, Snowflake offers unlimited storage and computing capabilities. This is an important reason why organizations want to migrate databases from SQL Server to Snowflake as they do not have to set up additional hardware and software every time there is a spike in demand for more data management resources.
- Snowflake offers packaged services from the encoding of columns to automatic clustering of data without defining indexes. Users can co-locate data through cluster keys when working on very large tables.
These are some of the cutting-edge features of Snowflake data warehousing solution.
Migrating database from Microsoft SQL Server to Snowflake
Four distinct steps have to be followed for migrating data from SQL Server to Snowflake.
- Extracting data from SQL Server – The first step is extracting data from Microsoft SQL Server and is done through queries for extraction. The in-built SQL Server Management Studio tool is used to extract bulk data and entire databases in CSV, SQL queries, or text format.
- Formatting the data – The extracted data has to be processed and formatted before it can be loaded into Snowflake. It is because the mined data structure has to match that supported by the Snowflake architecture. However, this is not applicable when migrating JSON or XML data into Snowflake.
- Staging of data files – The formatted and processed data has to be kept in a staging area from where it can be loaded into Snowflake. One is an internal staging area and the other an external one. An internal staging location is created with SQL statements and a name for the stage and the file type allotted to it. For the external stages, Snowflake currently supports Amazon S3 and Microsoft Azure.
- Loading data to Snowflake – This is the final stage in the SQL Server to Snowflake The data is now ready to be migrated to Snowflake from one of the staging areas where it is located. In the case of large databases, Snowflake guides users through the process with its Data Loading Overview tool. On the other hand, the data loading wizard of Snowflake is used for loading small databases.
Even after the database is migrated from SQL Server to Snowflake, provision has to be made for loading incremental data and changes. For this, a script should be created that identifies new data at source and uses an auto-incrementing field to update the data continually to the target database.
Selecting the right tool for migration
While the migration process is primarily automated, not using the right tools can make it challenging and complicated. The tool selected for loading data should have the following features.
- Be able to take care of migrating very large databases without any performance degradation or drop in speeds. For large organizations, this is a critical feature.
- Should be completely automated without the need of human intervention by the DBAs. If this is not the case, there will be a high cost of ownership of the solution. Automated tools ensure that migrating database SQL Server to Snowflake isnot lengthy and tedious as merging, transforming, and reconciling the data can be done quickly and effectively.
- The tool should reconcile data in Snowflake continually between the source and the target databases. It can be over pre-set periods too.
Loading databases from Microsoft SQL Server to Snowflake is smooth and seamless once the right tools are used.