SQL Server to Snowflake replication is crucial for many organizations looking to leverage the power of cloud data warehousing while maintaining their existing on-premises databases. This article explores two methods to achieve this replication: using Airbyte, an open-source data integration platform, and a manual approach involving data export and import using Snowflake's COPY command.

What is SQL Server?

SQL Server is a relational database management system developed by Microsoft. It's primarily used for storing and retrieving data as requested by other software applications, which may run on the same computer or across a network. SQL Server is widely used in enterprise environments for transactional systems, data warehousing, and business intelligence applications.

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that provides a single integrated solution for data storage, processing, and analytics. It's designed to be highly scalable, allowing organizations to store and analyze massive amounts of structured and semi-structured data. Snowflake is particularly popular for its ability to separate compute and storage resources, enabling flexible scaling and cost-effective operations.

Benefits of moving data from Microsoft SQL Server to Snowflake

Moving data from MSSQL to Snowflake may be part of an overall data integration strategy, which will provide your organization with:

• A unified view of data and a single source of truth – achieved by copying data from MSSQL Server and other operational systems into Snowflake.
• Improved analytics capabilities – Snowflake is purpose built for running large analytics jobs.
• The ability to transform data in a single location – moving data from multiple systems into Snowflake allows you to transform and join data from multiple disparate systems.
• Improved security – limit the number of people that require access to your MSSQL Server, as they can analyze your MSSQL data in Snowflake.

In addition to the benefits listed above, Snowflake is designed for storing massive amounts of data. Therefore MSSQL replication into Snowflake may be used for backups, or for archiving historical MSSQL data as required for compliance or regulatory requirements.

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Methods to Move Data From Microsoft sql server to snowflake

• Method 1: Connecting Microsoft sql server to snowflake using Airbyte
• Method 2: Connecting Microsoft sql server to snowflake manually

Method 1: Connecting Microsoft sql server to snowflake using Airbyte

Prerequisites

In this tutorial Airbyte OSS will be used to replicate your Microsoft SQL Server data to Snowflake. You will therefore need the following prerequisites:

1. Microsoft SQL Server.
2. Airbyte OSS. ‍
3. Snowflake.

Step 1: Configuring Microsoft SQL Server

Depending on your operating system, you may use the Microsoft SQL Server docker image or you can install SQL Server on Windows by downloading the .exe installer file. In this example, we will set up SQL Server on macOS using Docker. First, download the latest version of the Microsoft SQL server image by running the following command.

docker pull mcr.microsoft.com/mssql/server

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Once downloaded, start an instance by running the following command. First, choose a password by setting it in the highlighted section.

docker run --name airbyte-mssql -e "ACCEPT_EULA=Y" -e "SA_PASSWORD={YOUR_PASSWORD}" -e "MSSQL_AGENT_ENABLED=True" -p 1433:1433 -d mcr.microsoft.com/mssql/server:latest

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You can also run T-SQL containers by connecting to the sqlcmd, which is a command-line shell for SQL Server. Again, use the same password configured in the previous step.

docker exec -it airbyte-mssql /opt/mssql-tools/bin/sqlcmd -S localhost -U sa -P {YOUR_PASSWORD}

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Create a new database by running the following commands:

CREATE DATABASE Airbyte;
GO

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You can verify that the database is created by running the following:

SELECT Name FROM sys.Databases;
GO

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Which should respond with the following:

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Run the following T-SQL Statements to create schema and tables that will be used as our sample data:

USE Airbyte;
GO

CREATE SCHEMA sales;
GO

CREATE TABLE sales.customers (
  customer_id INT IDENTITY (1, 1) PRIMARY KEY,
  first_name VARCHAR (255) NOT NULL,
  last_name VARCHAR (255) NOT NULL,
  phone VARCHAR (25),
  email VARCHAR (255) NOT NULL,
  street VARCHAR (255),
  city VARCHAR (50),
  state VARCHAR (25),
  zip_code VARCHAR (5)
);

CREATE TABLE sales.stores (
  store_id INT IDENTITY (1, 1) PRIMARY KEY,
  store_name VARCHAR (255) NOT NULL,
  phone VARCHAR (25),
  email VARCHAR (255),
  street VARCHAR (255),
  city VARCHAR (255),
  state VARCHAR (10),
  zip_code VARCHAR (5)
);

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Add some rows to the customers table by running the following:

INSERT INTO sales.customers(first_name, last_name, phone, email, street, city, state, zip_code)
VALUES('Debra','Burks',NULL,'debra.burks@yahoo.com','9273 Thorne Ave. ','Orchard Park','NY',14127);
INSERT INTO sales.customers(first_name, last_name, phone, email, street, city, state, zip_code) VALUES('Kasha','Todd',NULL,'kasha.todd@yahoo.com','910 Vine Street ','Campbell','CA',95008);
INSERT INTO sales.customers(first_name, last_name, phone, email, street, city, state, zip_code) VALUES('Tameka','Fisher',NULL,'tameka.fisher@aol.com','769C Honey Creek St. ','Redondo Beach','CA',90278);
INSERT INTO sales.customers(first_name, last_name, phone, email, street, city, state, zip_code) VALUES('Daryl','Spence',NULL,'daryl.spence@aol.com','988 Pearl Lane ','Uniondale','NY',11553);
INSERT INTO sales.customers(first_name, last_name, phone, email, street, city, state, zip_code) VALUES('Charlotte','Rice','(916) 381-6003','charolette.rice@msn.com','107 River Dr. ','Sacramento','CA',95820);

GO

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Add rows to the stores table by executing:

INSERT INTO sales.stores(store_name, phone, email, street, city, state, zip_code)
VALUES('Santa Cruz Bikes','(831) 476-4321','santacruz@bikes.shop','3700 Portola Drive', 'Santa Cruz','CA',95060),
    ('Baldwin Bikes','(516) 379-8888','baldwin@bikes.shop','4200 Chestnut Lane', 'Baldwin','NY',11432),
    ('Rowlett Bikes','(972) 530-5555','rowlett@bikes.shop','8000 Fairway Avenue', 'Rowlett','TX',75088);

GO

Step 2: Setting up your Snowflake Account

If you don’t already have a Snowflake account, you can create a trial account. When creating a Snowflake account, you’ll need to pick a Snowflake edition and a cloud provider as part of the account creation process.

Once your account is successfully created, you'll be redirected to the Snowflake dashboard. The worksheet area will be the primary place you’ll run scripts for creating and modifying resources. You will need to set up the destination database, user, role, and schema on Snowflake for the sync.

Airbyte provides a convenient script in the Snowflake destination connector documentation which you should copy into your Snowflake worksheet area. After you have copied the script into your Snowflake worksheet select ‘All queries’ and run the script by clicking on the run button as shown below.

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ℹ️ Before running the script, be sure to change the airbyte_password variable to your preferred password value.

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Step 3: Setting up Microsoft SQL Server as the Airbyte Source

Go to Airbyte and create a new source connection. Give the connection a name and select Microsoft SQL Server as the Source Type.

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ℹ️ See the Microsoft SQL Server source connector documentation for additional information.

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Enter the values for the host and port you configured when setting up your MSSQL Server docker container as shown below:

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Step 4: Setting up Snowflake Destination in Airbyte

Set Snowflake as the destination, give the destination a name, and select Snowflake as the destination type.

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ℹ️ See the Snowflake destination connector documentation for more information.

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Enter the values for the fields based on the values set in the script in Step 2. For example, enter the URL you received by email for the host when signing up for Snowflake. If you updated the password in your script, enter the new password.

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Step 5: Setting up the SQL Server to Snowflake Airbyte Connection

Once the source and destination connectors are configured, you can access your connection settings. You should be able to see the tables that are available in your Microsoft SQL Server as shown below.

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Set the sync frequency and choose your Sync mode. In this example, the Full refresh | Append mode has been selected.

Step 6: Sync your data

Save the connection and select Sync now.

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Once the Sync is complete, you can go to the Database section in the Snowflake UI to see the tables that have been copied. Snowflake should contain the normalized data in the same format as the SQL Server Table. The replica also includes the raw data in a separate set with the name _AIRBYTE_RAW_{TABLE_NAME}

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You can view the structure of the table and the data types for each of the fields. Airbyte automatically maps the data types in the SQL Server tables to the corresponding data types in Snowflake.

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Method 2: Connecting Microsoft sql server to snowflake manually

Moving data from Microsoft SQL Server to Snowflake without third-party connectors or integrations involves several steps, including exporting data from SQL Server, preparing the data for Snowflake, and importing the data into Snowflake. Below is a detailed guide to accomplish this task.

Step 1: Export Data from Microsoft SQL Server

1. Choose the Data to Export: Decide which tables or data you want to move from SQL Server to Snowflake.

2. Generate Scripts for Table Schema:

   - Connect to your SQL Server instance using SQL Server Management Studio (SSMS).

   - Right-click the database containing the data you want to export.

   - Navigate to Tasks > Generate Scripts.

   - Follow the wizard to select the specific tables and choose the schema only.

   - Save the scripts to a file.

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3. Export Data to Flat Files:

   - In SSMS, right-click the database again and navigate to Tasks > Export Data.

   - Use the SQL Server Import and Export Wizard to export data.

   - Select the data source and the destination as a flat file format (CSV is commonly used).

   - Configure the flat file destination with the appropriate field terminators and encoding.

   - Run the package to export the data to the chosen location.

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4. Validate the Exported Data: Ensure that the data has been exported correctly and completely by checking a few records in the flat file against the source database.

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Step 2: Prepare Data for Snowflake

1. Modify the Table Schema Scripts: 

   - Edit the previously saved schema scripts to match Snowflake's syntax.

   - Adjust data types and remove any SQL Server-specific constructs that are not compatible with Snowflake.

   - Save the modified scripts.

2. Prepare the Flat Files:

   - If necessary, modify the CSV files to meet Snowflake's requirements (e.g., UTF-8 encoding).

   - Ensure that the files are accessible from a location that Snowflake can access, such as an Amazon S3 bucket or Azure Blob Storage.

3. Stage Files for Snowflake:

   - Upload the CSV files to a cloud storage location supported by Snowflake (Amazon S3, Google Cloud Storage, or Azure Blob Storage).

   - Verify that the files are successfully uploaded and accessible.

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Step 3: Import Data into Snowflake

1. Set Up Snowflake Environment:

   - Log in to your Snowflake account.

   - Create a database and schema if they don't already exist.

   - Use the modified schema scripts to create the tables within Snowflake.

2. Create a File Format:

   - Create a file format in Snowflake that matches the format of your exported CSV files, including field delimiter, encoding, etc.

3. Create a Stage:

   - Create a stage in Snowflake that points to the location of the CSV files in the cloud storage.

   - Use the previously created file format in the stage definition.

4. Copy Data into Snowflake:

   - Use the `COPY INTO` command to load data from the staged files into the corresponding tables in Snowflake.

   - Verify the success of the data load by checking the number of rows loaded and looking for any errors.

5. Validate the Imported Data:

   - Run queries against the imported tables to ensure that the data has been loaded correctly.

   - Compare record counts and sample data between the source and destination.

6. Perform Post-Load Tasks:

   - Apply any necessary transformations or data cleanup in Snowflake.

   - Create additional indexes, views, or stored procedures as needed.

Step 4: Clean Up

1. Remove Temporary Files:

   - Once the data is verified, you can remove the CSV files from the cloud storage to avoid unnecessary storage costs.

2. Document the Process:

   - Document the steps taken, any issues encountered, and how they were resolved for future reference.

3. Automate the Process (Optional):

   - If this is a recurring task, consider automating the process using Snowflake's tasks or stored procedures, or SQL Server Agent jobs, to streamline future data transfers.

Remember, the complexity of the migration can vary significantly based on the size and complexity of the data, as well as specific requirements such as data transformation, data type mapping, and handling of large binary objects (LOBs). Always perform thorough testing to ensure data integrity throughout the migration process.

Which SQL Server to Snowflake replication method should you choose?

Ease of use and setup

Airbyte generally offers an easier setup process with its user-friendly interface and pre-built connectors. It abstracts much of the complexity involved in data replication.

Scalability and performance

Airbyte is designed to handle large-scale data replication efficiently and can automatically manage incremental updates. It's built to scale with your data needs. The manual method may struggle with very large datasets and requires custom scripting for efficient incremental updates, potentially leading to performance issues at scale.

Data consistency and integrity

Both methods can maintain data consistency, but Airbyte has built-in features for error handling and data validation.

Automation capabilities

Airbyte excels in automation, offering scheduled runs and monitoring features out of the box. The manual method requires additional tools or custom scripts to achieve similar levels of automation, making it more time-consuming to set up and maintain.

Learning curve and skill requirements

Airbyte has a shorter learning curve for teams already familiar with ETL tools. The manual method requires deeper knowledge of both SQL Server and Snowflake, as well as data export/import processes and potentially scripting languages.

Wrapping up

Both methods offer viable solutions for replicating data from SQL Server to Snowflake. Airbyte provides an automated, user-friendly approach with built-in scheduling and error handling, making it ideal for organizations seeking a streamlined, low-maintenance solution. The manual method, while more labor-intensive, offers greater control over the process and may be suitable for occasional transfers or smaller datasets.

Ultimately, the choice depends on your specific needs, technical expertise, and resources. Consider factors such as data volume, frequency of updates, and available manpower when deciding between these two approaches.

Ready to simplify your data replication process? Sign up for Airbyte today and experience seamless, automated data transfers between SQL Server and Snowflake.

If you have enjoyed this tutorial, you may be interested in other Airbyte tutorials, or in Airbyte’s blog. You can also join the conversation on our community Slack Channel, participate in discussions on Airbyte’s discourse, or sign up for our newsletter.