On a late Friday evening, just when you’re about to call your day at the office, you have received an email stating a priority task. The Marketing Head has requested you to help him build an ETL pipeline for moving data from Google Analytics to Databricks. 

What would you do? You know that you can’t delay it because the end of the quarter is approaching, and the marketing team is working on tight deadlines for developing the marketing funnel. 

Don’t worry; you’re in the right place. This article will give you the methods for building an ETL pipeline for replicating data from Google Analytics to Databricks. Enough talk! Let’s get to it.

How to Replicate data From Google Analytics to Databricks?

For replicating data from Google Analytics to Databricks, you can follow any of the 2 methods based on your needs and use cases.

Replicate data from Google Analytics to Databricks Using CSV Files

Follow along to replicate data from Google Analytics to Databricks in CSV format:

Step 1: Export CSV Files from Google Analytics 

  • Open the Google Analytics report from which you want to export data.
  • Click on the “Export” button in the top-right corner. Then, choose the “CSV” format.

The GA report will be downloaded. However, you can only download a maximum of 5000 rows at a time.

Step 2: Import CSV Files into Databricks

  • In the Databricks UI, go to the side navigation bar. Click on the “Data” option. 
  • Now, you need to click on the “Create Table” option.
  • Then drag the required CSV files to the drop zone. Otherwise, you can browse the files in your local system and then upload them.

After uploading the CSV files, your file path will look like this: /FileStore/tables/<fileName>-<integer>.<fileType>

Creating table in Databricks
Image Source

Step 3: Modify & Access the Data

  • Click on the “Create Table with UI” button.
  • Now, the data that has been uploaded in Databricks can also be accessed via the Import & Explore Data section on the landing page.
Import & Explore data section
Image Source
  • To modify the data, select a cluster and click on the “Preview Table” option.
  • Then change the attributes accordingly and select the “Create Table” option.

This 3-step process using CSV files is a great way to replicate data from Google Analytics to Databricks effectively. It is optimal for the following scenarios:

  • Less Amount of Data: This method is appropriate for you when the number of reports is less. Even the number of rows in each row is not huge.
  • One-Time Data Replication: This method is helpful when your business teams need Google Analytics reports only once in a long time. This can be quarterly, yearly, or just this one time, etc.
  • No Data Transformation Required: This approach has limited options in terms of data transformation. Hence, it is ideal if the data in your spreadsheets is clean, standardized, and present in an analysis-ready form. 
Solve your data replication problems with Hevo’s reliable, no-code, automated pipelines with 150+ connectors.
Get your free trial right away!

However, regularly replicating data from Google Analytics to Databricks makes the repeated import of CSV files a cumbersome process. And fetching large number of reports packed with massive amount of data makes the process burdensome. Along with that, checking for errors and cleaning the data every time would eat up a major chunk of your time. 

We know you’d much rather focus on more productive tasks than repeatedly downloading, cleaning and uploading CSV files.

This is where you can leverage the power of an automated ETL/ELT solution that would cut all your repetitive tasks and help increase productivity multifold.

Replicate data from Google Analytics to Databricks Using Hevo

Repetitively downloading CSV files from Google Analytics and uploading to Databricks is cumbersome. Data errors, Frequent breakages, and lack of data flow monitoring make scaling such a system a nightmare.

An automated tool is an efficient and economical choice that takes away months of manual work. It has the following benefits:

  • Allows you to focus on core engineering objectives while your business teams can jump on to reporting without any delays or data dependency on you.
  • Your marketers can effortlessly enrich, filter, aggregate, and segment raw Google Analytics data with just a few clicks.
  • The beginner-friendly UI saves the engineering teams’ bandwidth from tedious data preparation tasks.
  • Without technical knowledge, your analysts can seamlessly standardize timezones, convert currencies, or simply aggregate campaign data for faster analysis. 

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

Step 1: Configure Google Analytics as your Source

Configure Google Analytics as the source.

Configure Google Analytics as your source for replicating data from Google Analytics to Databricks
Image Source

Note: You can select from the sync duration according to your requirements, where the default is 6 months. You can enable the Pivot Report option if you want to create an aggregated report based on the dimensions and metrics selected.

Step 2: Configure Databricks as your Destination

Now, you need to configure Databricks as the destination.

Configure Databricks as the destination in Hevo
Image Source

All Done to Setup Your ETL Pipeline

After implementing the 2 simple steps, Hevo will take care of building the pipeline for replicating data from Google Analytics to Databricks based on the inputs given by you while configuring the source and the destination.

The pipeline will automatically replicate new and updated data from Google Analytics to Databricks every 1 hour (by default). However, you can also adjust the data replication frequency as per your requirements.

Data Replication Frequency

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
1 Hr15 Mins12 Hrs1-12

For in-depth knowledge, you can also visit the official documentation of Hevo for Google Analytics as a source and Databricks as a destination.

Hevo’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. Moreover, the in-built transformation capabilities and the intuitive UI means that even non-engineers can set up pipelines and achieve analytics-ready data in minutes. 

Here’s what makes Hevo stand out from the crowd:

  • 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 Google Analytics 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 Google Analytics to Databricks, does your use case make the list?

  • Which demographic region contributes to the highest fraction of users of a particular Product Feature? 
  • How do Paid Sessions and Goal Conversion Rates vary with Marketing Spend and Cash in-flow?
  • How to identify your most valuable customer segments?
  • What should be your budget allocation for different marketing channels?

Summing It All Together 

This article has provided an in-depth guide for replicating data for Google Analytics to Databricks. It has stated 2 methods for undergoing the replication. One is focused on downloading CSV files from Google Analytics and uploading them to Databricks. The other one uses an automated no-code data pipeline solution, Hevo. It has also mentioned how your key stakeholders will be benefitted on making the replication.

With a no-code data pipeline solution at your service, companies will spend less time calling APIs, referencing data, building pipelines, and more time gaining insights from their data.

You can try Hevo’s no-code data pipeline solution. Hevo offers a 14-day free trial of its product. You can build a data pipeline from Google Analytics to Databricks and try out the experience.

 You can check out the following video to have an idea about how Hevo works and get started.

Hevo, being fully automated along with 150+ plug-and-play sources, will accommodate a variety of your use cases. Worried about the onboarding? Its incredible support team will be available around the clock to help you at every step of your journey with Hevo.

Feel free to catch up and let us know about your experience of employing a data pipeline from Google Analytics to Databricks using Hevo.

Manisha Jena
Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.

No-code Data Pipeline for Databricks