As a data engineer, you hold all the cards to make data easily accessible to your business teams. Your team just requested a Mixpanel to Databricks connection on priority. We know you don’t wanna keep your data scientists and business analysts waiting to get critical business insights. As the most direct approach, you can go straight for the Mixpanel APIs. Or, hunt for a no-code tool that fully automates & manages data integration for you while you focus on your core objectives.

Well, look no further. With this article, get a step-by-step guide to connecting Mixpanel to Databricks effectively and quickly, delivering data to your marketing team. 

Replicate Data from Mixpanel to Databricks Using Mixpanel APIs

To start replicating data from Mixpanel to Databricks, you need to use one of the Mixpanel APIs, that is, Event Export API:

  • Step 1: Data in Mixpanel is stored as JSON data. Mixpanel provides the Event Export API to retrieve the data. The following command is generated by Mixpanel for you after providing your basic authentication details: 
$ python -m pip install requests

import requests
url = ""

headers = {
    "accept": "text/plain",
    "authorization": "Basic aGFyc2g6MTIzNDU2"

response = requests.get(url, headers=headers)

You need to modify a bit to store your response as JSON file: 

import requests
url = ""
headers = {
    "accept": "text/plain",
    "authorization": "Basic aGFyc2g6MTIzNDU2"
response = requests.get(url, headers=headers).json()
import json
with open('personal.json', 'w') as json_file:
    json.dump(json_data, json_file)
  • Step 2: You can read the JSON files in single-line or multi-line format in Databricks. In a single-line manner, a file may be split into multiple parts and expressed in parallel. In multi-line style, a file is capsulated in one body and cannot be split.

To read the JSON file in a single-line format, you can use the following command in Scala: 

val df ="json").load("your-file-name.json")

To read the JSON file in a multi-line format, you can use the following command in Scala: 

val mdf ="multiline", "true").format("json").load("/tmp/your-file-name.json")

Although the charset is detected automatically in Databricks, but you can also provide it using the Charset option:"charset","UTF-16BE").format("json").load("your-file-name.json")

This process using the Mixpanel APIs is a great way to replicate data from Mixpanel to Databricks effectively. It is optimal for the following scenarios:

  • APIs can be programmed as customized scripts that can be deployed with detailed instructions on completing each workflow stage.
  • Data workflows can be automated with APIs, like Mixpanel APIs in this scenario. These scripts can be reused by anyone for repetitive processes.

In the following scenarios, using the Mixpanel APIs might be cumbersome and not a wise choice:

  • Using this method requires you to make API calls and code custom workflows. Hence it requires strong technical knowledge. 
  • Updating the existing API calls and managing workflows requires immense engineering bandwidth and hence can be a pain point for many users. Maintaining APIs is costly in terms of development, support, and updating.

When the frequency of replicating data from Mixpanel increases, this process becomes highly monotonous. It adds to your misery when you have to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

How about you focus on more productive tasks than repeatedly writing custom ETL scripts? This sounds good, right?

In these cases, you can.. 

Automate the Data Replication process using a No-Code Tool

You can use automated pipelines to avoid such challenges. Here, are the benefits of leveraging a no-code tool:

  • Automated pipelines allow you to focus on core engineering objectives while your business teams can directly work on reporting without any delays or data dependency on you.
  • Automated pipelines provide a beginner-friendly UI that saves the engineering teams’ bandwidth from tedious data preparation tasks.

For instance, here’s how Hevo, a cloud-based ETL tool, makes Mixpanel to Databricks data replication ridiculously easy:

Step 1: Configure Mixpanel as a Source

Authenticate and Configure your Mixpanel Source.

Mixpanel to Databricks: Mixpanel as a source
Image Source

Step 2: Configure Databricks as a Destination

In the next step, we will configure Databricks as the destination.

Mixpanel to Databricks: Configure Databricks as Destination
Image Source

Step 3: All Done to Setup Your ETL Pipeline

Once your Mixpanel to Databricks ETL Pipeline is configured, Hevo will collect new and updated data from Mixpanel every five minutes (the default pipeline frequency) and duplicate it into Databricks. Depending on your needs, you can adjust the pipeline frequency from 5 minutes to an hour.

Data Replication Frequency

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
1 Hr15 Mins24 Hrs1-24

In a matter of minutes, you can complete this No-Code & automated approach of connecting Mixpanel to Databricks using Hevo and start analyzing your data.

Hevo offers 150+ plug-and-play connectors(Including 40+ free sources). It efficiently replicates your data from Mixpanel to Databricks, databases, data warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. Here’s what allows Hevo to stand out in the marketplace:

  • Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
  • Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
  • Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision-making. 
  • Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
  • Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
  • Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
  • Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What Can You Achieve by Migrating Your Data from Mixpanel to Databricks?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Mixpanel to Databricks. Does your use case make the list?

  • 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?

Summing It Up

Mixpanel APIs are the right path for you when your team needs data from Mixpanel once in a while. However, a custom ETL solution becomes necessary for the increasing data demands of your product or marketing channel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo’s 150+ plug-and-play integrations.

Visit our Website to Explore Hevo

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

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

Share your experience of replicating data from Mixpanel to Databricks! Let us know in the comments section below!

Harsh Varshney
Research Analyst, Hevo Data

Harsh is a data enthusiast with over 2.5 years of experience in research analysis and software development. He is passionate about translating complex technical concepts into clear and engaging content. His expertise in data integration and infrastructure shines through his 100+ published articles, helping data practitioners solve challenges related to data engineering.

No-code Data Pipeline for Databricks