E-Mail marketing is a powerful way for companies to carry out large-scale promotions globally without exhausting their marketing resources. Although Social Media Marketing is gaining popularity, E-Mail remains an effective way to promote your business, attract new customers, and retain the existing ones. Research shows that E-Mail has huge marketing potential, and companies should not ignore it as a viable means to expand their customer base, strengthen their brand and increase sales. Mailchimp is an automation platform that allows you to manage mailing lists, create your own E-Mail templates, and develop and automate all marketing activities.

Also, the last two decades have witnessed a total revolution in the field of data management. With the vast sea of information that is growing every day, most organizations are looking towards Cloud-based solutions to collect, store and organize their data. Google BigQuery is one such Cloud-based Data Warehouse that emphasizes seamless Scalability, Simplicity, and Abstraction. BigQuery is serverless and hence provides services dynamically which means that no provisioning or hardware management is required from the user.

Now, moving data from Mailchimp to BigQuery might be a good first step if your Marketing team is generating a lot of E-Mail campaign data from Mailchimp and you want to take a deep dive into your data to generate meaningful insights and analytics. This blog will show you two methods by which you can move data from Mailchimp to BigQuery effectively. It will also discuss the key features of both Mailchimp and BigQuery. Furthermore, the blog will enable you to evaluate both methods and choose the one which best suits your needs for setting up your Mailchimp to BigQuery Integration.


  • Working knowledge of Databases and Data Warehouses.
  • A Mailchimp account.
  • A BigQuery account.
  • Working Knowledge of SQL.
  • Clear idea regarding what data is to be transferred.

Introduction to Mailchimp

Mailchimp to BigQuery Integration - Logo of Mailchimp.
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Mailchimp is an all-in-one E-Mail Marketing software that enables you to create and manage your E-Mail interactions with your customers. These may be Newsletters, Automated E-mail campaigns, Mailing lists, etc. Mailchimp enables you to have full end-to-end control of all these interactions. Mailchimp also enjoys a lot of popularity in the market due to its simplicity and ease of use. 

Key Features of Mailchimp

Mailchimp has the following feature which makes it one of the most convenient E-Mail Marketing tool available in the market:

  • Geo-targeting: Using Mailchimp makes it possible for you to target individuals from specific locations or regions. 
  • Analytics/Reporting Feature: Mailchimp offers a reporting and analytics feature that enables you to quickly gain high-level insights.
  • E-Commerce Functionality: You can track campaign-specific revenue and purchases with Mailchimp. This makes it easier to track and monitor individual campaign performance.
  • Automated E-Mail Campaigns: You can fully automate your Marketing campaigns and execute them once a customer meets certain conditions.

To read more about Mailchimp visit here.

Introduction to Google BigQuery

Mailchimp to BigQuery Integration - Logo of BigQuery.
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Google BigQuery is a cloud-based Data Warehouse that is offered as part of the Google Cloud Products stack. BigQuery provides scalable, fast, and easy analysis of Big Data with SQL code. BigQuery is also a Google-managed service. This makes it easier to use than many other Data Warehouse solutions in the market as it eliminates the need to manually manage to compute clusters. BigQuery currently enjoys a good reputation in the market due to its high performance, which can be attributed to its tree-based architecture and its columnar storage.

Key Features of BigQuery

Google BigQuery has many unique features that make it a popular Data Warehouse. Some of those features are:

  • Managed Service: BigQuery’s performance tuning and backend configuration are handled by Google. This makes it easier to use than other Data Warehouses where you may be required to manually handle these.
  • Distributed Architecture: Google manages to compute resources dynamically and so you do not have to handle them.
  • Easy to use: You do not have to build your own data center when using BigQuery as you only have to load your data into BigQuery and pay for what you use.
  • Fast and detailed insights: BigQuery enables seamless integration with many widely-used analytics tools like Looker and Google Data Studio. This makes it easy to understand your data.

To read more about Google BigQuery visit here.

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Methods to Set up Mailchimp to BigQuery Integration

You can implement the following two methods to set up your Mailchimp to BigQuery Integration:

Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Learn the best practices and considerations for setting up high-performance ETL to BigQuery

Method 1: Using Custom Code to Set up Mailchimp to BigQuery Integration

This method involves setting up a manual ETL process to push data from Mailchimp to BigQuery. The ETL process periodically extracts data from Mailchimp using the REST API, transforms the data according to an appropriate custom architecture of destination, and then sends it into BigQuery using certain commands.

This manual data transfer process from Mailchimp to BigQuery is carried out using the following steps:

Step 1: Extract the data from Mailchimp

You can extract your Mailchimp data through its REST API. Mailchimp has a rich REST API that exposes a large number of endpoints on lists, campaigns, etc. for you to interact with. Your data will be returned in JSON. You can choose to interact with the API through tools like postman or curl, or directly. 

Example(with curl):

'https://usX.api.mailchimp.com/3.0/lists/57afe96172/members' --user 'anystring:apikey' —include

The response will take the following form:

{ "members": [ { "id": "f777bbffab8d1ceca8b757df63c47cb8", "email_address": "urist.mcvankab+1@freddiesjokes.co", "unique_email_id": "882e9bec19", "email_type": "html", "status": "subscribed", "status_if_new": "", "merge_fields": { "FNAME": "", "LNAME": "" }, "interests": { "9143cf3bd1": true, "3a2a927344": false, "f9c8f5f0ff": false, "f231b09abc": true, "bd6e66465f": false },…………

Additional information on the Mailchimp API can be found through this link.

Step 2: Prepare the data

You may want to create a schema for your data tables to receive the Mailchimp data. You may also need to flatten your JSON data if it is nested. Special care must also be taken to ensure that the data types in your Mailchimp data match their corresponding types in BigQuery. BigQuery provides support for many of the widely used data types today. Specific information on the BigQuery data types can be found here.

Step 3: Load the data

The data can be loaded through the following steps:

  • Use gsutil to load your data to Google Cloud Storage(GCS)
  • Use the bq load command(via the BigQuery command line)  to write code to create tables for storing your data and specify the schema
  • Load the data into your tables

Additional information on loading data via BigQuery’s command line interface can be found here.

Limitations of the Manual ETL Approach:

  • Needs Developer Bandwidth: This method requires your engineering team to write a lot of code. This makes it very time-consuming and inefficient. It may also prove to be problematic when tight deadlines need to be met.
  • Real-time data: This method cannot bring data in real-time. Additional code has to be written and cron jobs configured to achieve even limited real-time functionality.
  • Maintenance: This method will return inaccurate data in situations where the Mailchimp API is down. As a result, you will have to continually monitor the connection.
  • Difficulty with Data Transformations: It is impossible to perform quick data transformations (like date/time conversions etc) under this method.

Method 2: Using Hevo Data to Set up Mailchimp to BigQuery Integration

Mailchimp to BigQuery - Hevo Data logo.
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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from Mailchimp and 100+ other data sources (including 40+ free data sources such as Mailchimp) to Data Warehouses such as BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

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Hevo Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.

Moving your data from Mailchimp to BigQuery with Hevo Data is a very straightforward and automated process. It can be completed by using the following steps:

  • Configure the Data Source: Configure and connect your Mailchimp account as a data source.
Mailchimp to BigQuery Integration - Configuring Mailchimp with Hevo Data to set up Mailchimp to BigQuery Integration.
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To get more details about Configuring Mailchimp with Hevo Data visit this link.

  • Configure Destination: Configure your Google BigQuery account as the destination.
Mailchimp to BigQuery Integration - Configuration of BigQuery with Hevo.
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You now have a real-time pipeline for syncing data from Mailchimp to BigQuery.

More reasons to try Hevo Data:

  • Simplicity: Hevo is easy to use and intuitive. Using Hevo ensures that your data is transferred in just a few clicks without any developer help.
  • Minimal Setup: Setting up Hevo requires minimal effort on your end. This is because it is fully managed and completely automated.
  • Reliable Data Load: Hevo has a fault-tolerant architecture which ensures that data loads are done reliably with minimal data loss
  • Scalability: Hevo easily handles data from a wide array of free data sources including Mailchimp at any scale. Thus, it helps you scale your data infrastructure to meet your growing needs
  • Real-time: Hevo allows you to gain real-time insights through its real-time architecture. This ensures that you can move your data instantly and without delays


The article gave you an introduction to Mailchimp and Google BigQuery and also explained the key features that make them essential in the current business context. It also explained the step-by-step process of setting up Mailchimp to BigQuery integration. The first approach of creating the ETL process through custom codes, although effective, will require a good amount of time and resources on your part.

Visit our Website to Explore Hevo

Hevo Data with its strong integration with 100+ data sources (including 40+ Free Sources such as Mailchimp) allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

Share your experience of understanding the process of setting up Mailchimp to BigQuery Integration in the comment section below! We would love to hear your thoughts.

Rashid Y
Freelance Technical Content Writer, Hevo Data

Rashid is passionate about freelance writing within the data industry, and delivers informative and engaging content on data science by incorporating his problem-solving skills. He finds joy in breaking down complex topics for helping data practitioners solve their everyday problems.

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