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 Metabase as a source connector (using Auth, or usually an API key)
2. set up DuckDB 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 Metabase

Metabase is accessible to all. Metabase is a self-service business intelligence software and it is a BI tool with a friendly UX and integrated tooling to let your company explore data on its own. Metabase is the easy, open-source way for everyone in your company to ask questions and learn from data. Metabase is an open-source business intelligence tool that lets you create charts and dashboards using data from a variety of databases and data sources. It generally assists users to create charts and dashboards from their databases.

What is DuckDB

DuckDB is an in-process SQL OLAP database management system. It has strong support for SQL. DuckDB is borrowing the SQLite shell implementation. Each database is a single file on disk. It’s analogous to “ SQLite for analytical (OLAP) workloads” (direct comparison on the SQLite vs DuckDB paper here), whereas SQLite is for OLTP ones. But it can handle vast amounts of data locally. It’s the smaller, lighter version of Apache Druid and other OLAP technologies.

{{COMPONENT_CTA}}

Prerequisites

1. A Metabase account to transfer your customer data automatically from.
2. A DuckDB 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 Metabase and DuckDB, for seamless data migration.

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

Methods to Move Data From Metabase to duckdb

• Method 1: Connecting Metabase to duckdb using Airbyte.
• Method 2: Connecting Metabase to duckdb manually.

Method 1: Connecting Metabase to duckdb using Airbyte

Step 1: Set up Metabase as a source connector

1. Open Metabase and navigate to the "Databases" tab.
2. Click on "Add Database" and select "Generic SQL" as the type.
3. In the "Connection Details" section, enter the following information:  - Name: A name for your database connection  - Host: The hostname or IP address of your Airbyte instance  - Port: The port number used by your Airbyte instance (default is 8000)  - Database: The name of the database you want to connect to  - Username: Your Airbyte username  - Password: Your Airbyte password
4. Click on "Save" to save your connection details.
5. Once your connection is saved, you can use it to create queries and visualizations in Metabase. Simply select your Airbyte connection from the list of available databases when creating a new query or visualization.

Step 2: Set up DuckDB as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Add Destination" button located in the top right corner of the screen.
3. Scroll down the list of available destinations until you find "DuckDB" and click on it.
4. Fill in the required information for your DuckDB database, including the host, port, database name, username, and password.
5. Test the connection to ensure that the information you provided is correct and that Airbyte can successfully connect to your DuckDB database.
6. If the connection is successful, click on the "Save" button to save your DuckDB destination connector.
7. You can now use this connector to transfer data from your source connectors to your DuckDB database. Simply select the DuckDB destination connector when setting up your data integration pipelines in Airbyte.

Step 3: Set up a connection to sync your Metabase data to DuckDB

Once you've successfully connected Metabase as a data source and DuckDB 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 Metabase from the dropdown list of your configured sources.
3. Select your destination: Choose DuckDB 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 Metabase objects you want to import data from towards DuckDB. 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 Metabase to DuckDB according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your DuckDB data warehouse is always up-to-date with your Metabase data.

Method 2: Connecting Metabase to duckdb manually

Moving data from Metabase to DuckDB without using third-party connectors or integrations requires a few steps. Metabase is a visual interface for databases, not a database itself, so I will assume you want to export data from the underlying database that Metabase is connected to. Here’s a guide to manually export data from the source database and import it into DuckDB.

Step 1: Determine Source Database

• Identify the database that Metabase is connected to (e.g., MySQL, PostgreSQL, SQLite).
• Ensure you have direct access to this database and the necessary credentials.

Step 2: Export Data from Source Database

• Connect to your source database using a database client or command-line tool.
• Choose the tables or data you want to export.
• Use the appropriate command or tool to export the data. For example:some text
  • For MySQL: mysqldump -u username -p database_name > data_export.sql
  • For PostgreSQL: pg_dump -U username -d database_name -t table_name > data_export.sql
  • For SQLite: .output data_export.sql followed by .dump table_name

Step 3: Prepare the Exported Data

• Open the data_export.sql file in a text editor.
• Review the SQL export to ensure it contains only the data and commands you want to import into DuckDB (e.g., remove any database-specific commands).

Step 4: Install DuckDB

• If you haven’t already, download and install DuckDB from the official website or use a package manager.some text
  • For example, using Python’s pip: pip install duckdb
• Alternatively, you can use the DuckDB CLI or a GUI tool that supports DuckDB.

Step 5: Create a New DuckDB Database

• Launch DuckDB through your chosen method (CLI, Python, GUI).
• Create a new database or connect to an existing one.some text
  • For the CLI: duckdb my_duckdb_database.duckdb
  • For Python: import duckdb; con = duckdb.connect('my_duckdb_database.duckdb')

Step 6: Import Data into DuckDB

• Using the DuckDB command-line interface, you can import the SQL file:some text
  • .read data_export.sql
• If you are using Python, you can execute the SQL commands from the file:some text
  • with open('data_export.sql', 'r') as f:
  • sql_commands = f.read()
  • con.execute(sql_commands)

Step 7: Verify Data Integrity

• Once the data is imported, run some queries to ensure that the data has been imported correctly.
• Check for any errors or warnings that occurred during the import process.

Step 8: Optimize DuckDB Database (Optional)

• After importing, you might want to optimize the database or create indexes to improve query performance in DuckDB.
• Use DuckDB commands to create indexes or perform other optimizations as needed.

Step 9: Clean Up

Remove any temporary files or sensitive information used during the data transfer process.

Step 10: Secure the New Database

Ensure that your new DuckDB database is secured and that proper access controls are in place.

Remember that the above steps are a general guide and might need adjustments based on the specific databases and data involved. Always back up your data before performing migrations or imports to prevent data loss.

Use Cases to transfer your Metabase data to DuckDB

Integrating data from Metabase to DuckDB provides several benefits. Here are a few use cases:

1. Advanced Analytics: DuckDB’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Metabase data, extracting insights that wouldn't be possible within Metabase alone.
2. Data Consolidation: If you're using multiple other sources along with Metabase, syncing to DuckDB 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: Metabase has limits on historical data. Syncing data to DuckDB allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: DuckDB provides robust data security features. Syncing Metabase data to DuckDB ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: DuckDB can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Metabase data.
6. Data Science and Machine Learning: By having Metabase data in DuckDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Metabase provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to DuckDB, providing more advanced business intelligence options. If you have a Metabase table that needs to be converted to a DuckDB table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Metabase account as an Airbyte data source connector.
2. Configure DuckDB as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Metabase to DuckDB 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!