Loading Microsoft SQL Server to Snowflake Data Warehouse
Huge volumes of data are generated by modern businesses all over the world. Databases are used for making mission-critical decisions and administrators are continually looking for ways to optimally create value from the data. One of the methods used is to load the Microsoft SQL Server to Snowflake. This is a flexible and smooth process and in most cases can be completed quickly in a few clicks. Before going into the SQL to Snowflake process, a quick look at the brief descriptions of the two will be in order. Microsoft SQL Server is a relational database management system and supports applications across the web on a local area network or a single machine. It facilitates a wide range of analytics and business transactions and operations in organizations. The SQL Server is based on SQL, a programming language. It is commonly used by administrators to query data in databases and manage them.
Snowflake runs on Amazon Web Services EC2 and S3. It is a cloud-based data warehouse and has separate compute and storage resources. Users have the flexibility to scale up and down depending on the quantum of data and pay only for the resources used. It can load both structured and unstructured data with multiple workloads operated by multiple users working together without any drop in performance in Snowflake. A major advantage of Snowflake is that it can automatically create tables and columns and detect schema changes. The snowflake table is always kept updated with the most accurate data types and data loading and processing are done quickly and in real-time. A few specific steps have to be followed for loading SQL to Snowflake. The first is to get the data out of the SQL Server. The traditional way is queries for extraction through filtering, sorting, and limiting the data that has to be retrieved. Microsoft SQL Server Management Studio tool is used for bulk export of data, databases, and entire tables. Formats relied upon for this activity are text, CSV, or SQL queries that can restore the databases when loaded to Snowflake. Once the data is extracted, the next step before transferring SQL to Snowflake is the preparation of the data. The amount of work that needs to be done now depends on the existing data structures. Hence it is essential to confirm the data type for Snowflake to ensure that the new data matches accurately with it. A Schema should be fixed in advance before loading data into Snowflake. Once these two steps have been completed the process of SQL to Snowflake transfer of data can be taken up. The Data Loading Overview of Snowflake will guide the user through the data loading process. The COPY INTO TABLE command loads the ready data into a pre-prepared table while the PUT command is used to stage the files. The data can be copied from Amazon S3 or the local drive.