Trello to BigQuery Data Replication: Best 3 Methods



Keep it simple, and stupid.

Kelly Johnson

Michael Pryor used these 5 simple words to build a company that has more than 50 million registered users today.


The numbers certainly hint at the value it adds to your project management. 

To tap into the true potential of your Trello data, you need to collect it in a central location such as a data warehouse like BigQuery. Performing data integration from Trello to BigQuery will help you centralize the data and derive useful insights from it. In this blog, I will walk you through the three methods to achieve that. 

Let’s get started!

Method 1: Using CSV/JSON Files

Get Your Data Ready to Send from Trello to BigQuery

The first thing to ensure is that the format of data from the API you pull data from is the same as that of the destination. Currently, CSV and JSON are two data formats supported by BigQuery.

And, the data types supported by BigQuery are STRING, INTEGER, FLOAT, BOOLEAN, RECORD, and TIMESTAMP.

Load Your Data 

Now, we have the data ready. Next, let’s take a look at the supported data sources to load the data into.

  • Google Cloud Storage
  • Use a POST request to send data directly to BigQuery
  • Google Cloud Datastore Backup
  • Streaming insert
  • App Engine log files
  • Cloud Storage logs

Let’s take Google Cloud Storage. First, you have to load your data into it. There are a few options for this like directly using the console to load the data. Another way is to post your data through the JSON API. It’s very easy, just use an HTTP POST request using a tool like CURL or Postman. Here is an example:

POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=myObject HTTP/1.1
Content-Type: application/text
Content-Length: number_of_bytes_in_file
Authorization: Bearer your_auth_token your Trello data

If the data is loaded correctly, you will get the following response from the server:

HTTP/1.1 200
Content-Type: application/json
"name": "myObject"

However, Curl or Postman is best suited for testing only. You should code to send your data to Google Cloud Storage for automating the data loading into BigQuery. If you are developing on the Google App Engine, you need to use the library that is available for the languages that are supported by it. Those libraries are:

  • Python
  • Java
  • PHP
  • Go

After loading data to Google Cloud Storage, create a Load Job for BigQuery. This Job should point to the source data in Cloud Storage that is yet to be imported. You can do this by giving source URIs that point to the right objects.

A POST request to GCS API will store the data there and load it to Google BigQuery.  Or, a direct HTTP POST request to BigQuery with the data you need to query does the job. You can interact with the data using the HTTP client library of the language or framework. A few ways are:

  • Apache HttpClient for Java
  • Spray-client for Scala
  • Hyper for Rust
  • Ruby rest-client
  • Python http-client

The disadvantages of using the CSV/JSON method are the following:

  • You can use CSV/JSON only to move the most basic data. It can’t be used to export complex configurations.
  • It can’t differentiate between text and numeric values. Also, it offers poor support for special characters.
  • There is no standard way to represent binary data and control characters.

Method 2: Building a Data Pipeline

In this method, Trello to BigQuery integration is done by building data pipelines. You need to use the Kafka platform for that which acts as the streaming platform. 

Kafka can help in two ways:

  • Self-managed (Using your own servers or cloud machines)
  • Fully managed by Confluent (a company that created Kafka)

In the case of Trello and BigQuery, a ready-made connector is available for Trello, not for BigQuery. 

The data replication can be done through the following steps:

  • You pull data from Trello by building a connector using any programing language.
  • Push it into Kafka and carry out data transformations
  • Push it into  BigQuery using a Kafka connector for BigQuery

Although the steps look easy, it has some disadvantages:

  • Maintaining the Kafka cluster is a tedious task. 
  • The entire process demands a lot of bandwidth from your data engineering team which could otherwise go into other high-priority tasks.
  • Maintaining the data pipeline is a complex task. 

Do you feel the need for a better method for data replication? We will come to that in the next section. 

Method 3: Using an Automated Data Pipeline

With this method, you can seek the help of third-party tools like Hevo to provide an automated Trello to BigQuery data pipeline for you. 

The benefits of this method are: 

  • Completely managed: You no longer have to spend time building your own data pipelines for Trello BigQuery integration. 
  • Data transformation: An automated data pipeline offers a user-friendly interface with drag-and-drop functions and Python scripts to let you clean, alter, and change your data. 
  • Faster insight generation: Automated data pipelines provide near real-time data replication, enabling you to generate insights instantly and take quicker decisions.
  • Schema management: All of your mappings will be automatically discovered and handled to the destination schema using the auto schema mapping feature of automated data pipelines.
  • Scalable infrastructure:  A fully automated data pipeline can handle millions of records per minute with little delay as the number of sources and volume of data grow.

Hevo Data provides a no-code pipeline that helps you leverage all these benefits of Trello to BigQuery integration in just 2 easy steps:

Step 1: Configure Trello as the source

Configuring Trello as a Source
Configuring Trello as a Source

Step 2: Configure BigQuery as the destination

Configuring BigQuery as the Destination
Configuring BigQuery as the Destination

That’s it. 

What Can You Achieve by Replicating Data from Trello to BigQuery

  • It enables you to acquire data from a variety of operational data sources along with Trello, such as software development and testing systems.
  • Monitor the progress of significant projects with lots of data without experiencing any performance problems.
  • Keep all of the data about your project environment accessible and secure in the BigQuery cloud data warehouse.
  • Improve the infrastructure of your project to reduce the possibility of data loss that happens during data migration


The intuitive user interface and other features of Trello make it a go-to tool for project management. But, to leverage the benefits of Trello data to the fullest, you need to carry out data replication to a data warehouse like BigQuery. There are three ways to do the Trello to BigQuery integration. 

  • Using CSV/JSON files which have many shortcomings to use for a large chunk of data. 
  • By building a data pipeline that requires your data engineering team’s constant support. 
  • The final method is using an automated data pipeline by using a third-party tool. 

You need to understand your requirements and choose the suitable one from the three methods. 

You can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources) like Trello to BigQuery. Hevo Data is helping many customers take data-driven decisions through its no-code data pipeline solution for Trello BigQuery integration. 

Visit our Website to Explore Hevo

Saving countless hours of manual data cleaning and standardizing, Hevo Data’s pre-load data transformations to connect Trello to BigQuery gets it done in minutes via a simple drag and drop interface or your custom Python scripts. No need to go to BigQuery for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your Trello to BigQuery data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Anaswara Ramachandran
Content Marketing Specialist, Hevo Data

Anaswara is an engineer-turned writer having experience writing about ML, AI, and Data Science. She is also an active Guest Author in various communities of Analytics and Data Science professionals including Analytics Vidhya.

No-code Data Pipeline For BigQuery