Google Analytics To Data Warehouse: Load Data Instantly

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google analytics to data warehouse

Are you looking for an easy way to move your data from Google Analytics to a data warehouse? If yes, then you are in the right place. This blog post aims to show you steps that can help you load your data from Google Analytics to a data warehouse. The blog will also highlight any limitations that you may encounter. This will enable you to make an informed decision after evaluating the methods. 

Table of contents

Moving Data From Google Analytics To Data Warehouse

Two quick ways to move your data from Google Analytics to a data warehouse are listed below:

Method 1: Google Analytics To Data Warehouse: Manually Writing ETL Scripts

This method involves manually writing the script to identify/access the data, extract the data, transform the data, create a repository in your data warehouse, and finally load the data in the data warehouse. Using this method for moving your data from Google Analytics to a data warehouse is tiring and time-consuming.

Method 2: Google Analytics To Data Warehouse: Using Hevo

Hevo is a No-code Data Pipeline. It automatically maps your Google Analytics data to its relevant tables in your data warehouse for free, giving you access to the data in real-time. This method is tireless as Hevo offers a fully-managed service.

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Method 1: Google Analytics To Data Warehouse: Manually Writing ETL Scripts

Prerequisites

  1. An understanding of how APIs work.
  2. Google Analytics account.
  3. Data warehouse of your choice.

Steps To Create Custom Scripts

The steps involved in loading the data from Google Analytics to a data warehouse are as follows:

Step 1: Identify Your Data

The first step is to identify/access the data in Google Analytics. You can do this through the Google Analytics API. Google Analytics provides a rich API that exposes a number of endpoints with which you can programmatically interact. The data from Google Analytics is in the form of reports which can be narrowed down to a specific time period of your choice. More information on the Google Analytics API can be found here.

Step 2: Extract Your Data

You can use the API to extract data after identifying the data and timelines you want to see. The dashboards and reports you generate with the API can also be used in your Google Analytics account, in addition to exporting to your data warehouse.

Step 3: Transform And Prepare Your Data

You have to first transform your data to ensure that it is in a format that can be accepted by your data warehouse. For example, it will be easy to use a JSON format for Google BigQuery but you may have to choose to convert to a CSV or SQL format for more traditional relational databases like Microsoft SQL Server. You also have to ensure that the data types in Google Analytics map to the data types of your chosen data warehouse. Information on data types of some popular data warehouses can be found through the following links:

Step 4: Create A Data Receiving Repository In Your Data Warehouse

Creating a data stage for your data could make your data transformation easier to perform before it is finally ingested for analysis/reporting. This is easy to create in data warehouses like Google BigQuery or Snowflake.

Step 5: Load Your Data

It is advisable to design a schema for your chosen data warehouse and then map it to your Google Analytics data. In this way, you are almost ready to load your data from Google Analytics to a data warehouse after making sure that all the aforementioned steps are completed to suit your needs. The specifics of this step depend on your chosen data warehouse. For example, in Snowflake you can use the COPY INTO SQL command. Alternatively, you might have to use a command-line tool in other data warehouses like Google BigQuery.

Limitations Of Manual Method

Limitations of using the manual method for loading the data from Google Analytics to a data warehouse are as follows:

  • Time-Consuming: Manually loading your data from Google Analytics to a data warehouse requires a lot of code to complete simple tasks. This is very problematic in fast-paced organizations where tight deadlines have to be consistently met. 
  • Knowledge And Resource-Intensive: The manual method of moving your data from Google Analytics to a data warehouse requires a lot of commitment from your engineering team. This could be particularly taxing on small organizations.
  • Real-Time Limitations: Configuring cron jobs is a necessity to even achieve limited real-time functionality under this method.
  • Error-Handling: Undiscovered errors could potentially sabotage the ETL process and require more time to isolate/fix the issue.

Method 2: Google Analytics To Data Warehouse: Using Hevo

Hevo is a No-code Data Pipeline. You can use this zero-code data platform to load your data. Hevo is a fully managed service and so requires very little effort in setting it up. With Hevo, you can replicate your Google Analytics data for free through two easy steps: 

  • Authenticate and connect to your Google Analytics account on the Hevo platform.
  • Connect your data warehouse to Hevo and transfer your data.

Hevo ensures that your data is transferred from Google Analytics to your chosen data warehouse in a consistent, reliable, and secure manner. Furthermore, Hevo can also be used to move data from a variety of sources like SDKs, databases, etc. Hevo is a wise choice for moving your data from Google Analytics to a data warehouse.

Additionally, Hevo automatically maps your Google Analytics data to its relevant tables in your data warehouse, giving you access to the data in real-time. Hevo also supports data integration from more than 150+ sources (including 30+ free sources) such as databases, SDKs, etc.

Advantages Of Hevo

Various advantages of Hevo are listed below:

  • Scalability: Hevo perfectly handles data from a wide variety of sources like databases, analytics applications, and more at any scale. Thus, Hevo is able to help you scale, to meet your accelerating growth demands.
  • Reliable Data Load: Hevo ensures that your data loads are reliable, consistent, and done with minimal loss through its fault-tolerant architecture.
  • Real-Time: Hevo has a real-time streaming architecture that enables you to instantly move your data and thus gain real-time insights. 
  • Simplicity: Hevo is very easy to use and intuitive. Using Hevo for your data transfer ensures that it is done in a few clicks.
  • Minimal Setup: Hevo is automated and fully-managed. This ensures that it requires minimal effort on your part to set it up. 
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Introduction To Google Analytics

Google Analytics is a cloud-based web analytics platform provided by Google. Google Analytics enables you to track data on your website through dimensions that allow you to sort through your website’s visitors and also metrics that monitor website activity, among other features. Exporting this data into a data warehouse gives you the opportunity to blend with other sources, thus enabling you to gain more nuanced insights into your organization.   

Features of Google Analytics

  • Google Analytics helps in Website Traffic measurement
  • You monitor user activity and website conversion using Google Analytics
  • You can know your user better with Audience report
  • The Flow Visualisation Report gives you the behavior pattern of the user
  • With Google Analytics, you can get custom reports of the Analytics data

Conclusion

In this blog post, you have learned how to load your data from Google Analytics to a data warehouse. While manually writing ETL scripts can be tiring and time-consuming, Hevo Data provides a plug-and-play platform for your data movement.

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Using a data transfer tool like Hevo can help your organization develop a more robust and reliable method for transferring its data from Google Analytics to a Data Warehouse for Free.

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Let’s know about your thoughts and experience of moving data from Google Analytics to a data warehouse in the comment section given below.

Rashid Y
Technical Content Writer, Hevo Data

Rashid is a technical content writer with a passion for the data industry. Leveraging his problem-solving skills, he delivers informative and engaging content on data science. With a deep understanding of complex data concepts and a talent for clear, compelling communication, Rashid creates content that informs and captivates his audience.

No-code Data Pipeline for Google Analytics