The modern business environment has moved away from traditional database management systems to more technologically advanced processes. Enterprises are depending heavily on data analytics to power growth and expansion and to take key operational decisions. It has now become essential that Database Administrators have access to tools and optimized platforms so that their work of managing data is seamless and easy.
Organizations have to process data from different sources in the present-day setup, not only from various in-house departments but from external sources also. One of the most effective and convenient methods is to load and migrate databases from the existing systems to a platform that offers cutting-edge benefits. And with this objective, more and more businesses are choosing to load databases from Microsoft SQL Server to Snowflake.
This post will go into the finer intricacies of this process but before that, a detailed look at the two principal components in the system will be relevant.
Microsoft SQL Server
For decades, Microsoft SQL Server has been the workhorse of organizations for database management because of its many excellent and superior features. The server supports the .NET framework out of the box and integrates seamlessly into the total Microsoft ecosystem. SQL Server is a Relational Database Management System (RDMS) and provides support to all applications on a single machine. This is regardless of whether the applications are working on a local area network or across the web.
The efficiency of Microsoft SQL Server is considered to be at par with other leading well-established databases like DB2 from IBM and Oracle database. It is ideal for database transaction processing and data analytics. The server is built on SQL, a programming language that is widely and commonly used by Database Administrators to query data in the databases.
Given the capabilities of SQL Server, the point is, why do businesses want to load data from Microsoft SQL Server to Snowflake.
The Benefits of Snowflake
Snowflake is a cloud-based data warehouse that is built on top of AWS (Amazon Web Services) or Microsoft Azure. It is ideal for organizations as there is no need to install additional hardware or software or select, configure, and manage them. Resources are not required to be allotted for setup, maintenance, and support of in-house servers as Snowflake is based in the cloud.
Here are some of the benefits of this cloud-based platform that make loading data from SQL Server to Snowflakean attractive proposition for enterprises.
- A wide range of cloud vendors is supported by Snowflake. Users can, therefore, work on any of them with the same set of tools and knowledgebase.
- Both structured and unstructured data can be loaded into Snowflake. This is a feature that is exclusive to this platform and is not available in other top databases like SQL Server or Oracle.
- Snowflake offers unmatched computing speeds and there is no lag or drop in performance even when multiple users execute multiple intricate queries.
- This cloud-based data warehouse offers separate computing and storage facilities. Users can scale up or down in resource usage as required and pay only for the quantum used, resulting in substantial saving of time and money.
- Snowflake provides unlimited computing and storage facilities. Organizations on this platform do not have to invest in additional hardware or software whenever there is a spike in demand for resources and data.
- One of the critical benefits of Snowflake is that it offers all-inclusive services from automatically clustering data without defining indexes to the encoding of columns. For very large tables, users may avail the option of co-locating data through cluster keys.
These are some of the benefits for which businesses prefer to load data from SQL Server to Snowflake.
How to Load Data from Microsoft SQL Server to Snowflake
The process to load a database from Microsoft SQL Server to Snowflake is a four-step one. It is easy for DBAs as it is largely automated.
- The first stage is extracting data from the SQL server and is done through queries for extraction. The data is sorted and filtered through select statements before it is mined. This step is done by using the in-built Microsoft SQL Server Management Studio tool that helps in extracting entire databases or bulk data in CSV, SQL queries, or text format.
- This mined data cannot be loaded directly into Snowflake before it is processed and formatted. This is because there are specific data types that are supported by Snowflake and the data to be loaded into Snowflake has to match one of them. Hence it is necessary to format the extracted data to make it sync with the Snowflake architecture. However, it is not necessary to specify a schema beforehand for loading JSON or XML data.
- Even after the data is processed and formatted, it cannot be loaded directly into Snowflake but has to be kept in a staging area. There are two locations that users can opt for.
The first is an internal staging area that is created by the DBAs with SQL statements. A name and the type of file format are allotted to the location. This allows a great deal of flexibility when data is being loaded from SQL Server to Snowflake.
The second is the external staging area. Presently, the Snowflake architecture supports only Microsoft Azure and Amazon Simple Storage Service S3. Data can be uploaded using any of the cloud interfaces after an external stage is designed using one of these staging areas.
- The final stage in the process of SQL Server to Snowflake is loading this data into Snowflake from one of the staging areas where it is kept. For large databases, DBAs can use the Data Loading Overview tool of Snowflake to complete the process. For smaller databases, the data loading wizard of Snowflake is ideal. The PUT command is used to stage files for bulk databases and the COPY INTO command is used to load the processed data into an intended table in Snowflake. This should be from an internal or external staging area where the database is located temporarily.
Even after loading an entire database from SQL Server to Snowflake, the focus should be on the issues related to updating changes and incremental data. DBAs should create a script that recognizes new data at the source and uses an auto-incrementing field as a tool to update the data in the target database continually.
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