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Loading Data to Snowflake from SQL Server – Process and Benefits

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Loading Data to Snowflake from SQL Server – Process and Benefits

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  1. Loading Data to Snowflake from SQL Server – Process and Benefits Processing and analyzing data are at the core of almost all organizations around the world today and is why enterprises are choosing to work on a data solution platform that optimizes their needs.  Microsoft SQL Server software, a Relational Database Management System (RDMS) supports the SQL (SEQUEL) language. It creates and maintains databases, generates reports through SQL Server Reporting Services, and analyzes data through SQL Server Analysis Services (SSAS). Most importantly, all applications are supported across the web or a local area network on a single machine.

  2. Now, why would businesses want to load data from SQL Server to Snowflake? Snowflake is a cloud-based data warehousing solution offered as a Software-as-a-Service product. It has many highly useful features.

  3. Snowflake has separate computing and storage facilities. Users can scale up or down in either of them, paying only for the resources used. Further, several workgroups can simultaneously execute complex queries without facing any lag or drop in performance. Snowflake allows the loading of both structured and unstructured data, supporting Avro, JSON, XML, and Parquet data. This is not possible on platforms like SQL Server or Oracle database. Finally, Snowflake architecture is in sync with several cloud vendors, enabling users to employ the same tools while working with different cloud vendors

  4. How would you load data from SQL Server to Snowflake? There are several steps.  Extracting data from Microsoft SQL Server The process begins with mining data from SQL Server which is done through queries for extraction. Specific statements are used to sort, filter, and limit the data that is being extracted. For mining bulk databases in formats such as text, CSV, or SQL queries, Microsoft SQL Server Management Studio tool is used. Preparing and Formatting Mined Data The extracted data has to be first formatted and processed before it can be loaded in Snowflake. This is because the architecture of the Snowflake data warehousing solution supports specific data types only. To ensure that the loading passes off seamlessly, it has to be first verified whether the format of the extracted data matches that supported by Snowflake. This step is not necessary when loading JSON or XML data to Snowflake.

  5. Placing Data in a Staging Area The formatted data cannot be loaded directly to Snowflake and has to be kept in a temporary location called a staging area. An internal stage is created with SQL Statements and a file name and other options are assigned to this stage. The External stages currently supported by Snowflake are Microsoft Azure and Amazon S3.  Loaddata from SQL Server to Snowflake This final stage is carried out through Data Loading Overview with the loading wizard being used for small databases. For the large ones, the COPY INTO table command loads the processed data. Follow these steps to quickly load data from SQL Server to Snowflake. 

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