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 Mailchimp as a source connector (using Auth, or usually an API key)
2. set up Databricks Lakehouse 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 Mailchimp

What is Databricks Lakehouse

Prerequisites

1. A Mailchimp account to transfer your customer data automatically from.
2. A Databricks Lakehouse 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 Mailchimp and Databricks Lakehouse, for seamless data migration.

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

Step 1: Set up Mailchimp as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

Step 3: Set up a connection to sync your Mailchimp data to Databricks Lakehouse

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

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

Use Cases to transfer your Mailchimp data to Databricks Lakehouse

Integrating data from Mailchimp to Databricks Lakehouse provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Mailchimp account as an Airbyte data source connector.
2. Configure Databricks Lakehouse as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Mailchimp to Databricks Lakehouse after you set a schedule