TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Microsoft SQL Server (MSSQL) as a source connector (using Auth, or usually an API key)
2. set up Postgres destination as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Microsoft SQL Server (MSSQL)

Microsoft SQL Server Consultants help companies choose the best business software solutions for their needs. Microsoft SQL Server Consultants help businesses resolve questions and issues, provide businesses with reliable information resources, and, ultimately, make better decisions on the software most appropriate for their unique needs. Consultants are available to help on call and can connect remotely to businesses’ computers to upgrade outdated editions of SQL servers to bring functions up to date for improved productivity.

What is Postgres destination

An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many web, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.

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Prerequisites

1. A Microsoft SQL Server (MSSQL) account to transfer your customer data automatically from.
2. A Postgres destination account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Microsoft SQL Server (MSSQL) and Postgres destination, for seamless data migration.

When using Airbyte to move data from Microsoft SQL Server (MSSQL) to Postgres destination, it extracts data from Microsoft SQL Server (MSSQL) using the source connector, converts it into a format Postgres destination can ingest using the provided schema, and then loads it into Postgres destination via the destination connector. This allows businesses to leverage their Microsoft SQL Server (MSSQL) data for advanced analytics and insights within Postgres destination, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Microsoft sql server to postgres

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

Method 1: Connecting Microsoft sql server to postgres using Airbyte

Step 1: Set up Microsoft SQL Server (MSSQL) as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.

2. Click on the "Add Source" button and select "MSSQL - SQL Server" from the list of available connectors.

3. Enter a name for the connector and click on the "Next" button.

4. Enter the required credentials for your MSSQL - SQL Server database, including the server name, port number, database name, username, and password.

5. Test the connection to ensure that the credentials are correct and the connection is successful.

6. Select the tables or views that you want to replicate from the MSSQL - SQL Server database.

7. Choose the replication mode that you want to use, either full or incremental.

8. Configure any additional settings, such as the replication frequency and the maximum number of rows to replicate.

9. Click on the "Create Source" button to save the configuration and start the replication process.

10. Monitor the replication process and troubleshoot any issues that may arise using the Airbyte platform's monitoring and logging features.

Step 2: Set up Postgres destination as a destination connector

Step 3: Set up a connection to sync your Microsoft SQL Server (MSSQL) data to Postgres destination

Once you've successfully connected Microsoft SQL Server (MSSQL) as a data source and Postgres destination as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Microsoft SQL Server (MSSQL) from the dropdown list of your configured sources.
3. Select your destination: Choose Postgres destination from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Microsoft SQL Server (MSSQL) objects you want to import data from towards Postgres destination. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Microsoft SQL Server (MSSQL) to Postgres destination according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Postgres destination data warehouse is always up-to-date with your Microsoft SQL Server (MSSQL) data.

Method 2: Connecting Microsoft sql server to postgres manually

Moving data from Microsoft SQL Server to PostgreSQL without using third-party connectors can be a complex task, but it can be done by following these steps:

Step 1: Prepare the Environment

1. Install PostgreSQL: If not already installed, set up PostgreSQL on your target system.

2. Enable Network Access: Ensure both SQL Server and PostgreSQL can communicate over the network if they are not on the same machine.

3. Backup Your Data: Always make a backup of your SQL Server database before starting the migration process.

Step 2: Schema Conversion

1. Generate SQL Server Schema: Use SQL Server Management Studio (SSMS) to generate the SQL scripts of your database schema (tables, views, stored procedures, etc.).

    - Right-click the database > Tasks > Generate Scripts.

    - Follow the wizard and choose to script all objects.

    - Save the scripts to a file.

2. Convert Data Types: Manually convert SQL Server data types to PostgreSQL-compatible data types in the scripts.

    - For example, `VARCHAR(MAX)` in SQL Server should be converted to `TEXT` in PostgreSQL.

3. Adjust Syntax: Modify SQL syntax in the script to match PostgreSQL syntax, such as:

    - Changing `IDENTITY` to `SERIAL` for auto-increment columns.

    - Replacing square brackets `[]` with double quotes `""`.

    - Adjusting function and stored procedure definitions.

4. Create PostgreSQL Schema: Run the modified script in PostgreSQL to create the schema.

Step 3: Export Data from SQL Server

1. Use BCP or SQL Server Management Studio: Export data from SQL Server to flat files (CSV).

    - Use the BCP command-line tool or SSMS Export Wizard.

    - Choose a character that is not present in your data as a field separator.

    - Ensure you export data in a text format that PostgreSQL can import, such as CSV.

Step 4: Import Data into PostgreSQL

1. Prepare PostgreSQL: Create the necessary tables in PostgreSQL if you haven't done so in Step 2.

2. Use COPY or \copy: Import the data from the CSV files into PostgreSQL.

    - If you have access to the PostgreSQL server, use the `COPY` command.

    - If you do not have server file system access, use the `\copy` command in `psql`, which works from the client side.

Example of using `COPY`:

```sql

COPY your_table FROM '/path/to/your/file.csv' WITH CSV HEADER DELIMITER ',';

```

Step 5: Verify Data Integrity

1. Check Row Counts: Compare the row counts in SQL Server and PostgreSQL to ensure they match.

2. Sample Data: Run some sample queries on both databases and verify that the results are identical.

3. Check for Errors: Review the PostgreSQL logs for any errors that might have occurred during the import process.

Step 6: Migrate Indexes, Triggers, and Stored Procedures

1. Indexes: Create indexes in PostgreSQL as needed, converting any SQL Server-specific syntax.

2. Triggers and Stored Procedures: Manually rewrite SQL Server triggers and stored procedures using PL/pgSQL or an appropriate procedural language in PostgreSQL.

Step 7: Test the Application

1. Update Connection Strings: Change your application’s database connection strings to point to the PostgreSQL database.

2. Run Tests: Thoroughly test your application to ensure it interacts correctly with the PostgreSQL database.

Step 8: Perform Final Data Sync (if necessary)

1. Lock SQL Server Database: Prevent any new data from being added to the SQL Server database.

2. Repeat Data Export/Import: Perform the data export and import steps again to capture any data changes that occurred since the initial migration.

3. Unlock Database: Once you have confirmed that PostgreSQL is working as expected, you can decommission the SQL Server database.

Step 9: Go Live

1. Switch Over: Redirect all clients and applications to the new PostgreSQL database.

2. Monitor: Keep an eye on performance and error logs to ensure everything is running smoothly.

Notes:

- This process assumes a basic migration without complex transformations or dependencies.

- Always test the migration process in a development or staging environment before applying it to production.

- The complexity of migrating stored procedures, functions, and triggers can vary greatly depending on their use of database-specific features.

- You may need to perform additional steps to handle special cases, such as full-text search data, binary data, or hierarchical data.

Remember, while this guide avoids third-party tools, using them can significantly simplify and automate the migration process.

Use Cases to transfer your Microsoft SQL Server (MSSQL) data to Postgres destination

Integrating data from Microsoft SQL Server (MSSQL) to Postgres destination provides several benefits. Here are a few use cases:

1. Advanced Analytics: Postgres destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Microsoft SQL Server (MSSQL) data, extracting insights that wouldn't be possible within Microsoft SQL Server (MSSQL) alone.
2. Data Consolidation: If you're using multiple other sources along with Microsoft SQL Server (MSSQL), syncing to Postgres destination allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Microsoft SQL Server (MSSQL) has limits on historical data. Syncing data to Postgres destination allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Postgres destination provides robust data security features. Syncing Microsoft SQL Server (MSSQL) data to Postgres destination ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Postgres destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Microsoft SQL Server (MSSQL) data.
6. Data Science and Machine Learning: By having Microsoft SQL Server (MSSQL) data in Postgres destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Microsoft SQL Server (MSSQL) provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Postgres destination, providing more advanced business intelligence options. If you have a Microsoft SQL Server (MSSQL) table that needs to be converted to a Postgres destination table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Microsoft SQL Server (MSSQL) account as an Airbyte data source connector.
2. Configure Postgres destination as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Microsoft SQL Server (MSSQL) to Postgres destination after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!