Many businesses worldwide use Google Analytics to collect valuable data on website traffic, signups, purchases, customer behavior, and more. Given the humongous amount of data that is present on Google Analytics, the need to deeply analyze it has also become acute. Naturally, organizations are turning towards Amazon Redshift, one of the widely adopted Data Warehouses of today, to host this data and power the analysis. In this post, you will learn how to move data from Google Analytics to Redshift.

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Methods to move data from Google Analytics to Redshift

There are two ways of loading your data from Google Analytics to Redshift:

Method 1: Using Hand Coding to Connect Google Analytics to Redshift

The activities of extracting data from Google Analytics, transforming that data to a usable form, and loading said data onto the target Redshift database would have to be carried out by custom scripts. The scripts would have to be written by members of your data management or business intelligence team. This data pipeline would then have to be managed and maintained over time.

Method 2: Using Hevo Data to Connect Google Analytics to Redshift

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Google Analytics comes free pre-built “out of the box” integration in Hevo. You can easily move data with minimal setup, configuration from your end. Given Hevo is a fully managed platform, no coding help or engineering bandwidth would be needed. Hevo will ensure that your data is in the warehouse, ready for analysis in a matter of just a few minutes.

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Methods to Connect Google Analytics to Redshift

Here are the methods you can use to connect Google Analytics to Redshift in a seamless fashion:

Method 1: Using Hand Coding to Connect Google Analytics to Redshift

Pre-Migration Steps

  • Audit of Source Data: Before data migration begins, Google Analytics event samples should be reviewed to ensure that the engineering team is completely aware of the schema. Business teams should coordinate with engineering to clearly define the data that needs to be made available. This will reduce the possibility of errors due to expectation mismatch between business and engineering teams
  • Backup of all Data: In the case of a failed replication, it is necessary to ensure that all your GA data may be retrieved with zero (or minimal) data loss. Also, plans should be made to ensure that sensitive data is protected at all stages of the migration.

Manual Migration Steps

  • Step 1: Google Analytics provides an API, the Google Core Reporting API, that allows engineers to pull data. As such, most of the data that is returned is combined into a consolidated JSON format, which is incompatible with Redshift. 
  • Step 2: The scripts would need to pull data from GA to a separate object, such as a CSV file. Meanwhile, to prepare the Redshift data warehouse, SQL commands must be run to create the necessary tables that define the database structure. The aforementioned CSV file must then be loaded to a resource that Redshift can access.
  • Step 3: Amazon S3 cloud storage service is a good option. There is some amount of preparation involved in configuring S3 for this purpose. The CSV file must then be loaded into the S3 that you configured. The COPY command must be invoked to load the data from the CSV file and into the Redshift database. 
  • Step 4: Once the transfer is complete queries should be run on the newly populated database to test if the data is accurate and complete. This would re-ensure that the data load was successful. Having been verified, a cron job should be set up to run with reasonable frequency, ensuring that the Redshift database stays up to date. Say you have different Google Analytics views set up for Website, App, etc. You would have to end up repeating the above process for each of these.

This concludes this method of manually coding the migration from Google Analytics to Redshift.

Limitations of using Hand Coding to Connect Google Analytics to Redshift

Manual coding for data replication between diverse technologies, while not impossible, does come with its fair share of challenges. Immediate consideration is one of time and cost. While the value of the information to be gleaned from the data is definitely worth the cost of implementation, it is still a considerable cost.

The second concern of using Hand Coding to connect Google Analytics to Redshift is of accuracy and effectiveness. How good is the code? How many iterations will it take to get it right? Have effective tests been developed to ensure the accuracy of the migrated data? Have effective process management policies been put in place to ensure correctness and consistency?

For instance, how would you identify if GA Reporting API JSON format has been altered? The questions never end. 

Should the data load process be mismanaged, serious knock-on effects may result. These may include issues such as inaccurate data being loaded in the form of redundancies and unknowns, missed deadlines, and exceeded budgets as a result of multiple tests and script rewrites and more.  

However, loading data from Google Analytics to Redshift may also be handled by much easily in a hassle-free manner with platforms such as Hevo.

Method 2: Using Hevo Data to Connect Google Analytics to Redshift

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.

Hevo takes care of all your data preprocessing to set up migration from Google Analytics to Redshift and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

Using Hevo Data Integration Platform, you can seamlessly replicate data from Google Analytics to Redshift with 2 simple steps:

  • Step 1: Connect Hevo to Google Analytics to set it up as your source by filling in the Pipeline Name, Account Name, Property Name, View Name, Metrics, Dimensions, and the Historical Import Duration.
Google Analytics to Redshift: Source Config
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  • Step 2: Load data from Google Analytics to Redshift by providing your Redshift databases credentials like Database Port, Username, Password, Name, Schema, and Cluster Identifier along with the Destination Name.
Google Analytics to Redshift: Destination Config
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Hevo takes up all the grind work ensuring that consistent and reliable data is available for Google Analytics to Redshift setup.

What Can You Achieve By Replicating Data from Google Analytics to Redshift?

  • Which Demographic contributes to the highest fraction of users of a particular Product Feature? 
  • How are Paid Sessions and Goal Conversion Rates varying with Marketing Spend and Cash in-flow?
  • How to identify your most valuable customer segments?

Conclusion

This blog talks about the two methods you can use to connect Google Analytics to Redshift in a seamless fashion. Data and insights are the keys to success in business, and good insights can only come from correct, accurate, and relevant data. Hevo, a 100% fault-tolerant, easy-to-use Data Pipeline Platform ensures that your valuable data is moved from Google Analytics to Redshift with care and precision. 

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Hevo Data provides its users with a simpler platform for integrating data from 150+ sources like Google Analytics. It is a No-code Data Pipeline that can help you combine data from multiple sources. You can use it to transfer data from multiple data sources into your Data Warehouses, Databases, Data Lakes, or a destination of your choice. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have Analysis-ready data in your desired destination.

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Vernon DaCosta
Freelance Technical Content Writer, Hevo Data

Vernon is enthusiastic about data science and loves to write on diverse topics related to data, software architecture, and integration.

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