Database management is one of the primary functions of organizations today. It is because the modern business ecosystem is based on big data analytics, database storage and retrieval, high computing performance, and generation of reports. Based on these only, businesses take considered decisions for future development and growth.
Microsoft SQL Server
Microsoft SQL Server has been the mainstay of organizations globally and is used to store data. It is a combination of Structured Query Language (SQL) and Relational Database Management System. Microsoft SQL Server supports high levels and scales of applications through the data center version as well as lower levels through freeware on a single machine either across the web or on a local area network.
There are several benefits of loading Microsoft SQL Server to Snowflake, a comparatively recently-introduced cloud-based data warehousing solution. This is mainly because of the features of Snowflake that make data management a whole lot smoother and easier.
Here are some of the positive factors that Snowflake brings to the table.
Snowflake supports a wide range of cloud vendors and hence users can work on one or more of them with the same set of tools. Both structured and unstructured data can be loaded to Snowflake, a feature that is not available on other database management systems like SQL Server or Oracle. Snowflake also has very high computing abilities and multiple users can execute multiple intricate queries simultaneously without feeling any drop in speeds.
Another critical advantage of Snowflake is that it has separate and flexible computing and storage options. It is possible to scale up or down in either of them by paying only for the quantum of resources used. This results in savings of time and money.
Further, Snowflake offers almost unlimited computing and storage facilities. Organizations do not have to invest in hardware and software infrastructure in case of a rise in data requirements. Finally, Snowflake offers comprehensive services from the encoding of columns to automatically clustering data without defining indexes. Users have the option to co-locate data through cluster keys for very large tables.
These are some of the reasons why organizations are today opting to load databases from Microsoft SQL Server to Snowflake.
For more information about the database modeling tool visit here.
Process of loading databases from Microsoft SQL Server to Snowflake
There are four steps in the process.
- The first is to mine data from SQL Server through queries for extraction. For sorting, filtering, and limiting the data while doing so, select statements are used. While extracting large databases in CSV, SQL queries, or text format, the Microsoft SQL Server Management Studio tool is used.
- The mined data has to be processed and formatted to match the data types that are supported by the Snowflake architecture. A schema need not be specified before loading JSON or XML data.
- The processed and formatted data too cannot be loaded directly from SQL Server to Snowflake but has to be kept in a temporary location called the staging area. There are two components here. An internal staging area has to be created by the user and a name and file format assigned to it. External locations currently supported by Snowflake are Microsoft Azure and Amazon S3.
- The database is now ready to be loaded to Snowflake from one of the staging areas where it is located. For loading bulk data, the Data Loading Overview tool of Snowflake is used. The Snowflake Wizard has to be applied for small databases.
The loading process is mostly automated and becomes easier once the right tools are used.
Features of optimized tools for loading databases to Snowflake
Select the right tool with the following features for loading databases from SQL Server to Snowflake.
- The selected tool should be able to handle large databases without any fall in speed or performance. This factor is critical for any organization regardless of size or scale.
- Should be completely automated and should not require human intervention by the DBAs. Otherwise, it will result in a high cost of ownership of the solution. The process will not be lengthy and tedious as merging, transforming, and reconciling the data can be done with a simple point and click interface.
- The tool should continually reconcile data in the Snowflake data warehouse. The reconciliation may be done continually or over a pre-determined period and compare checksum and row count at a basic granular level.
- The selected tool should not have to undertake full data refreshes every time there is a change in source data. Choose a tool that uses SQL CDC (Change Data Capture). It will use database transaction logs to query SQL Server data at the source and copy the changes and modifications only to the Snowflake database. The data is therefore updated to Snowflake in real-time.
At first glance, the whole process of loading databases from Microsoft SQL Server to Snowflake might seem overwhelming but with the right tools, and by following the required processes, it is quite smooth and seamless.