This post will take you through the key points of transferring databases from Microsoft SQL Server to Snowflake. It will include a summary of the two databases, the benefits of the migration, the process, and ultimately, the features of the best tools to be used.
So, let us get started.
In the modern business environment, most organizations are data-driven and have to handle massive volumes of data, both from internal and external sources. It is essential to increase performance and analyze the data in real-time to make cutting-edge decisions and stay ahead of the competition.
Therefore, one of the primary goals of database administrators is to make sure that all data is swiftly processed and reports generated without any lag. One of the ways to meet this objective is to migrate databases from traditional platforms to a cloud-based ecosystem that offers multiple technologically advanced benefits. This is where migrating databases from Microsoft SQL Server to Snowflake comes into the picture.
Before going into the migration process, let us check the working of the SQL Server Snowflake individually in isolation. It will help you to understand better why migrating databases from SQL Server to Snowflake is so important.
Microsoft SQL Server
Microsoft SQL Server is based on the common programming language SQL and is among the top database technologies along with Oracle databases and the DB2 from IBM. SQL Server supports applications on local area networks across the web or on a single standalone system and integrates completely into the environment provided by Microsoft.
Primarily, SQL Server is an RDMS (Relational Database Management System) and is available out of the box with the Microsoft .NET framework. Most complex and intricate tasks and a wide range of analytics necessary for businesses can be carried out on the SQL Server.
Snowflake
Snowflake is a comparatively new platform and in a short period has ironed out many glitches faced by users of traditional databases. It is a cloud-based data warehousing solution that offers multiple benefits, leading to businesses preferring to migrate their existing databases on SQL Server to Snowflake.
Here are some of the major benefits of this cloud-based data warehousing system.
- Unlike traditional database systems, Snowflake offers separate storage and computing facilities. Hence, users can seamlessly scale up or down in using these resources by paying only for the quantum used. Since these two components are segregated, businesses can estimate the expenses of using each of them. Being cloud-based, users of Snowflake can avail unlimited storage space as per their requirements.
- Snowflake offers high database performance. It allows multiple users to execute multiple intricate queries simultaneously without registering any drop in database speed or efficiency.
- Users do not have to learn the use of new tools and technologies to work on Snowflake since it is compatible with a wide variety of different cloud vendors.
- Data in its native format, regardless of whether it is unstructured, semi-structured, or structured can be migrated to Snowflake. It also offers built-in support for JSON, Avro, XML, and Parquet data.
- Snowflake is a fully-automated cloud-based solution and indexes need not be defined while clustering data. This automation benefit stretches from computing to encoding of columns. However, where the tables are very large, it is necessary to use the clustering keys manually to co-locate the table data,
Because of these many benefits, organizations are today increasingly migrating their databases from SQL Server to Snowflake.
Migrating a database to Snowflake from Microsoft SQL Server
There are four steps to migrate data from Microsoft SQL Server to Snowflake.
# Extracting data from the SQL Server: This is the first step and constitutes mining data from the SQL Server. This is done through queries for extraction with select statements that sort, filter, and limit the data during the extraction process. If large databases in CSV, text, or SQL query format have to be migrated, it is done automatically by the Microsoft SQL Server Management tool.
# Formatting the extracted data: The data that is extracted from the SQL Server cannot be migrated to Snowflake in its native form. It must be processed and formatted to align with the data structures supported by Snowflake. This is not required for XML or JSON data.
# Staging in a temporary location: Even in this third stage, the processed and formatted data cannot be moved directly to Snowflake but has to be kept in an internal or external temporary location.
An internal staging area has to be created by users with SQL statements. This is very flexible as it offers the leeway to create file formats and other options to named stages as per requirements.
Regarding the external staging area, data is uploaded to the cloud interface directly. Currently, only Amazon S3 and MS Azure are supported by Snowflake as external staging areas.
# Moving data from the temporary location: This is the final stage in the SQL Server to Snowflake data migration process. For migrating small databases, the data loading wizard of Snowflake is used. For moving bulk data and very large databases, the Data Loading Overview of Snowflake is used. The commands are PUT to stage files and COPY INTO for loading processed data into a specific table.
Even though the SQL Server to Snowflake process is now complete, DBAs must make provisions to move incremental data at specified intervals. This is because, each time a change is made at the source database, the entire process cannot be refreshed to update the target database. Create a script to track new data at the source (SQL Server) and continuously update the target database (Snowflake).
