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SQL Server to Snowflake

Ready-to-use data delivered to Amazon S3, Amazon Redshift, and Snowflake at lightning speeds with BryteFlow data management tool. This automated tool is completely self-service, low on maintenance and requires no coding. It can integrate data from any API and legacy databases like SAP, Oracle, SQL Server, and MSQL.

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SQL Server to Snowflake

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  1. Migrating Database to Snowflake From Microsoft SQL Server

  2. More and more organizations around the world are opting to migrate databases to Snowflake from the traditional tried and tested Microsoft SQL Server. This is because there are certain advantages to being on Snowflake, a cloud-based data warehousing solution. Microsoft SQL Server SQL Server is used primarily for data storage and retrieval. It is useful for both Structured Query Language (SQL) and Relational Database Management System (RDMS). For small databases, Microsoft offers a freeware version while the data center version is used for more complex and large databases. A look at Snowflake features will help to understand the reasons for migrating databases from SQL Server to Snowflake.

  3. Snowflake A cloud-based data warehouse solution, Snowflake is provided as a Data Warehouse-as-a-Service (DWaaS). It operates on Amazon Web Service which is the most popular cloud provider in the world. Snowflake is a high-performing solution with almost unlimited computing and storage abilities. Users can scale up or down in either of them, paying only for the resources used. Further, multiple users can simultaneously execute intricate queries without a drop in performance. Also, both structured and unstructured data can be natively migrated to Snowflake including JSON, AVRO, XML, and PARQUET data.

  4. Migrating Database from SQL Server to Snowflake Essentially, migrating database from SQL Server to Snowflake is done in four simple steps. With the right automated tools, the process is easy and quick and does not require a great deal of human intervention. Step 1 The first step is to extract data from the SQL Server before loading it to Snowflake. It is done through queries for extraction with select statements that help to sort, filter, and limit the data during the extraction process. Microsoft SQL Server Management Studio Tool comes in very handy when large databases have to be extracted in CSV, text, or SQL queries formats. Step 2 The extracted data from SQL Server cannot be loaded to Snowflake but has to be first processed and formatted. This is because the data to be loaded from SQL Server to Snowflakehas to match the data types that are supported by Snowflake and hence need to be processed. JSON and XML data do not have to be formatted again before migration. Step 3 Even now, after formatting, the data cannot be loaded directly into Snowflake but has to be kept in a temporary staging area. An internal stage has to be created by the user and file formats and names allotted to it. For the external stage, currently, Amazon S3 and Microsoft Azure are supported by Snowflake. Step 4 The final stage to migrate databaseSQL Server to Snowflakeis to load it from the temporary staging area to Snowflake. The data loading wizard of Snowflake is used for small databases while the Data Loading Overview tool of Snowflake is used for large databases. Follow these steps for seamless migration process.

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