Are you looking to transfer data from Mixpanel to BigQuery? If yes, you are in the right place. In this blog, you are going to learn two of the most popular methods of moving data from Mixpanel to Google BigQuery.
Data can be extracted, and loaded to Google BigQuery using many different methods and platforms. In this blog, we are going to discuss two of the most popular methods. In the end, you will weigh the pros and cons of each based on your use case. Before getting into that, let’s understand these two applications.
Prerequisites
- Mixpanel account with admin access
- GCP (Google Cloud Platform) account with billing enabled
- Google Cloud SDK installed on your CLI
- Enabled BigQuery APIs
- BigQuery admin permissions
Methods to Load Data from Mixpanel to BigQuery
Method 1: Moving Data from Mixpanel to BigQuery using Hevo
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.
Sign up here for a 14-Day Free Trial!
It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using various BI tools such as Power BI, Tableau, etc.
Hevo focuses on two simple steps to move your data from Mixpanel to BigQuery:
- Configure Source: Connect Hevo Data with Mixpanel by providing a unique name for your Pipeline, along with details about your authorized Mixpanel account. You can also choose the historical sync duration for your Mixpanel data.
- Integrate Data: Complete Mixpanel BigQuery migration by providing your destination name, account name, region of your account, database username and password, database and schema name, and the Data Warehouse name.
Check Out What Makes Hevo Amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Method 2: Loading Data from Mixpanel to BigQuery using ETL Scripts
Since Mixpanel is an analytics-as-a-service application, it requires data to offer its analytical features. So, Mixpanel collects data from your customers who use your product. In case you want to analyze data that comes from other sources, you will have to enrich Mixpanel data with incoming data or extract Mixpanel data and load it to a warehouse for storage or further analysis. Let’s learn how we can load Mixpanel data to the warehouse in this section.
Step 1: Extract Data from Mixpanel
In order to extract data, we will use the Export API. Since it is a web API, you can access it using cURL, Postman, or any other HTTP client. These may include:
- Apache HTTP client for Java
- Hyper for Rust
- Python HTTP client
- Spray client for Scala
You may also use libraries or SDKs supported by Mixpanel for the following languages:
- PHP
- Python
- Ruby
- Javascript
Since it is a RESTful API, it offers many resources such as:
- Annotations: Create, update or delete an annotation
- Export: The “raw dump” of tracked events is fetched for a specified time
- Events
- People analytics
You may start by invoking the following endpoint to extract data.
curl https://data.mixpanel.com/api/2.0/export/
-u 'API_SECRET':
-d 'from_date'="2018-02-11"
-d 'to_date'="2018-02-11"
-d 'event'='["Viewed report"]'
-d 'where'="properties["$os"]=="Linux""
The following response will be returned in JSON format.
{
"event": "Viewed report",
"properties": {
"distinct_id": "foo",
"time": 1518314400,
"$os": "Linux",
"$browser": "Chrome",
"Project ID": "3",
"mp_country_code": "US"
}
}
Source: https://developer.mixpanel.com/docs/exporting-raw-data
Once you extract all the data you need, map it to the schema of your warehouse, and load it. You can learn more about this here.
Step 2: Prepare and Transform Mixpanel Data for Google BigQuery
After extracting Mixpanel data, you should transform it into a data format supported by Google BigQuery. For example, if your response data is in XML format, transform it into a serialization supported by Google BigQuery. Currently, two data formats are supported i.e. CSV and JSON.
Also, make sure your data types are supported by Google BigQuery. The following data types are supported by BigQuery.
- INTEGER
- BOOLEAN
- FLOAT
- STRING
- TIMESTAMP
- RECORD
Step 3: Load Data into BigQuery
A helpful guide is provided by GCP (Google Cloud Platform) for loading data into Google BigQuery. To upload files into your datasets, use the bq command-line tool and particularly, bq load command. Ensure you add the data schema and the data type. The bq syntax is available here.
To upload all the data into BigQuery, you will need to iterate the above process multiple times.
Challenges of Mixpanel BigQuery Data Transfer Process using ETL Scripts
You will be facing a few challenges while implementing the Mixpanel BigQuery data transfer process:
- Infrastructure maintenance: The Mixpanel Export API may be updated or changed. Therefore, you will need a team of engineers to constantly check and maintain the code.
- Real-time data: You have created a program that loads data from Mixpanel to Google BigQuery. However, you have not dealt with the challenge of loading new and updated data. Replicating data each time a row is created is slow and resource-intensive. To solve this challenge, you will need to write additional code.
- Ability to transform: The approach described above works when the data moves as it is. When you need to transform data, you will need to write additional code for the required transformation before loading it to the warehouse.
What can we achieve by replicating data from Mixpanel to BigQuery?
Powerful, self-serve product analytics to help you convert, engage, and retain more users.
- What percentage of customers from a region have the most engagement with the product?
- Which features of the product are most popular in a country?
- Your power users are majorly from which location?
- How does Agent performance vary by Product Issue Severity?
- How to make your users happier and win them over?
- What are the custom retention trends over a period of time?
- What is the trend of a particular feature adoption with time?
Conclusion
In this blog, you have learned how to connect the Mixpanel to BigQuery manually. You also came across the various limitations of connecting Mixpanel to BigQuery manually. So, if you are looking for a fully-automated data pipeline, then try Hevo.
Visit our Website to Explore Hevo
Hevo is a No-code Data Pipeline and has awesome 150+ pre-built integrations (including 40+ free sources) that you can choose from. Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data with a BI tool. It will make your life easier and data migration hassle-free. It is user-friendly, reliable, and secure.
Want to take Hevo for a spin?
Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Check out the Hevo pricing details.
How do you load data from your data sources to your data warehouse? Which is your preferred method of loading data from Mixpanel to BigQuery? Let us know in the comments.
Eva loves learning about data science, with an intense passion for writing on data, software architecture, and related topics. She enjoys creating an impact through content tailored for data teams, aimed at resolving intricate business problems.