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Massive volumes of data are generated in the modern business environment. Enterprises have to manage data not only from in-house but from what flows in from external sources too. Administrators are continually looking for new processes to manage this data optimally and get the maximum value from it. One of the methods is to load Microsoft SQL Server to Snowflake which, though might seem to be a long-drawn-out and cumbersome process can, in reality, be done in very little time with a few clicks.
Before going into the required steps to carry out this transition, a few basic concepts should be cleared first.
Microsoft SQL Server supports applications on a single machine or a local area network across the web and is a relational database management system. The server supports Microsoft’s .NET framework out of the box and integrates fully into the Microsoft ecosystem. It also supports a range of business and analytics operations and transaction processing in corporate IT environments. Microsoft SQL Server is considered to be one of the three leading database technologies along with Oracle Database and IBM’s DB2.
Microsoft SQL Server is built on SQL, a programming language commonly used by database administrators to manage databases and query the data contained in them.
Snowflake is a cloud-based data warehouse that runs on Amazon Web Services EC2 and S3. It is flexible and easy to work with. One of the main advantages of Snowflake is it separate compute and storage resources. Users can work on one or the other independently, scale up and down anyone as required and pay only for the resources used. It can load and optimize both structured and non-structured data and provide support to JSON, Avro, XML, and Parquet data. Multiple workgroups can work on multiple workloads concurrently with data on Snowflake without any drop in performance or concurrent roadblocks.
There are several benefits to this cloud-based data warehouse. It can automatically create tables and columns with the most accurate data types and can detect schema changes and keep the Snowflake table updated. Further Snowflake can optimize data loading throughput by using the Snowpipe COPY for data processing instantly in real-time. As discussed before, users have to pay only for resources used by using the Suspend and Auto Resume features of the data warehouse.
Microsoft SQL Server to Snowflake
The first step in getting data from SQL Server to Snowflake is to initiate the process of getting data out of the SQL Server. The most used and conventional method is by queries for extraction and is done through specific statements and filtering, sorting, and limiting the data that has to be retrieved. Microsoft SQL Server Management Studio is used for bulk export of data and entire databases and tables. Formats used are text, CSV, or SQL queries that can restore the database when run.
The next step before loading data into snowflake is preparing the data for transfer. The extent of work required depends on the existing data structures. It is essential to verify the supported data types for Snowflake to make sure that the new data maps accurately to it. A Schema should be defined in advance before loading JSON or XML data into Snowflake.
Once the two steps have been completed, the process of loading data from Microsoft SQL Server to Snowflake can begin. The Data Loading Overview of Snowflake’s documentation will guide the user through the data loading process. The PUT command is used to stage the files while the COPY INTO TABLE command loads the ready data into a pre-prepared table. The data can be copied from Amazon S3 or from the local drive with Snowflake allowing users to create a virtual data warehouse that can power the insertion process.
Needless to say, it is necessary to keep the SQL Server updated at all times by building a script to recognize and update records in the source database by using an auto-incrementing field as a key.
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