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.

Introduction to Google Analytics

Google Analytics to Redshift: Google Analytics logo
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Google Analytics helps companies keep track of user details and advertising campaigns using a simple javascript plugin. It provides an excellent reporting dashboard and the ability to run various analyses on the user activity data. It even supports advanced machine learning capabilities and helps organizations to deploy this capability to make better decisions without having to know the concepts or implementation details of machine learning algorithms.

Google Analytics also makes raw user activity data available to the companies that want to run custom algorithms and reports. This requires the raw clickstream data to be loaded into the organization’s own data store. 

Here are a few key benefits of Google Analytics:

  • Understanding User Behavior: The key advantage of having behavioral metrics is that it provides you valuable information on what pages get the most engagement and traction. By having a better understanding of User Behavior, you can change the way you can interact with your users in the most optimized way.
  • Easy to Find Your Target Audience: With the help of Google Analytics, you can easily define your target audience. The audience can then be used to optimize the content of your website along with its offerings to increase engagement. Thus, an engaged audience is one of the best ways to improve a website’s potential.
  • Data Reports and Customization: Google Analytics allows you to customize dashboards, alerts, and reports to analyze data that can fit every company’s different needs. Google Analytics provides an extensive library of user-generated reports and dashboards that can help you make data-driven decisions to improve efficiency.
  • Track Online Traffic: Google Analytics allows you to track traffic from all sources since having an understanding of where your audience comes from is a crucial aspect of running a business online. Pinpointing different traffic sources and understanding why and how much traffic comes to your website allows you to track the gains of your strategies.
  • Improved Search Engine Optimization and Content Marketing: With Google Analytics, you can identify the best-performing pages of your website to gain insights on the type of content to invest in. Google Analytics allows you to improve the tracking of the success of your Content Marketing and Search Engine Optimization strategy. By analyzing every part of your content strategy, you can create a stable and solid plan with clearly defined steps to reproduce successful pages.
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Introduction to Redshift

Google Analytics to Redshift: Redshift Logo
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Amazon Redshift is essentially a storage system that allows companies to store petabytes of data across easily accessible “Clusters” that you can query in parallel. Every Amazon Redshift Data Warehouse is fully managed which means that the administrative tasks like maintenance backups, configuration, and security are completely automated. 

Amazon Redshift is primarily designed to work with Big Data and is easily scalable due to its modular node design. It also allows users to gain more granular insight into datasets, owing to the ability of Amazon Redshift Clusters to be further divided into slices. Amazon Redshift’s multi-layered architecture allows multiple queries to be processed simultaneously thus cutting down on waiting times. Apart from these, there are a few more benefits of Amazon Redshift that are covered in the following section.

Key Features of Amazon Redshift

  • Enhanced Scalability: Amazon Redshift is known for providing consistently fast performance, even in the face of thousands of concurrent queries. Amazon Redshift Concurrency Scaling supports nearly unlimited concurrent queries and users. By leveraging Redshift’s managed storage, capacity is added to support workloads of up to 8 PB of compressed data. Scaling is just a simple API call, or a few clicks in the console away.    
  • Easy Management: Amazon Redshift automates oft-repeated maintenance tasks so that you can focus on gathering actionable insights from your data. It is fairly simple to set up and operate. A new Data Warehouse can be deployed with just a few clicks in the AWS console. Key administrative tasks like backup and replication are automated. Data in Amazon Redshift is automatically backed up to Amazon S3. Amazon Redshift can replicate your snapshots to Amazon S3 asynchronously in a different region for disaster recovery. The Automatic Table Optimization selects the best distribution keys and sort method to enhance the performance efficacy for the cluster’s workload. Amazon Redshift also gives you the flexibility to work with queries in the console, or Business Intelligence tools, libraries, and SQL client tools.  Also check out Redshift Sortkeys article.
  • Robust Security: Amazon Redshift is known for providing robust data security features at no extra cost. Amazon Redshift allows you to configure firewall rules to take control of network access to a specific Data Warehouse Cluster. Amazon Redshift also specializes in granular column and row-level security controls that ensure that users can only view data with the right type of access. Apart from these, Amazon Redshift also delivers on its promise of reliability and compliance through tokenization, end-to-end encryption, network isolation, and auditing.  
  • Data Lake and AWS Integrated: Amazon Redshift allows you to work with data in various open formats that can easily integrate with the AWS ecosystem. Amazon Redshift makes it exceptionally easy to query and write data to your Data Lake in open formats such as JSON, ORC, CSV, Avro to name a few. The federated query capability allows you to query live data across multiple Aurora PostgreSQL and Amazon RDS databases to get enhanced visibility into the business operations. This is carried out without the need for any undesired data movement. The AWS Analytics ecosystem allows you to handle end-to-end analytics workflows without any hiccups. You can also bring in data from various applications like Google Analytics, Facebook Ads, Salesforce to an Amazon Redshift Data Warehouse in a streamlined manner.     
  • Flexible Performance: Amazon Redshift distinguishes itself by offering swift, industry-leading performance with a keen focus on flexibility. This is made possible through result caching, materialized views, efficient storage, RA3 instances, and high-performance query processing to name a few. Result Caching is used to deliver sub-second response times for repeat queries. Business Intelligence tools, dashboards, visualizations leveraging repeat queries experience a significant performance boost. At the time of execution, Amazon Redshift looks through the cache to see if there is a cached result for repeat queries. Amazon Redshift also uses sophisticated algorithms to classify and predict the incoming queries based on their run times and resource requirements to manage concurrency and performance dynamically. This helps users prioritize business-critical workloads.
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

Google Analytics to Redshift: Hevo Logo
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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.

Advantages of using Hevo

The relative simplicity of using Hevo as a data integration platform, coupled with its accuracy takes the difficulty out of your data analysis projects. Here are the advantages:

  • Low time to implementation – You can connect to Google Analytics in minutes and move data to Redshift in real-time
  • Fully Managed – No code, hassle-free data replication
  • Complete Data – Hevo’s unique architecture ensures that the data is ingested to the warehouse without any data loss
  • Alerts and Notification – Whenever there are unresolved errors, you will be notified over either slack or email in real-time
  • Scalability – Hevo can scale as your business grows. You can move data from more data sources 100+ Sources(40+ Free Sources) like just Google Analytics whenever needed. Additionally, Hevo is built to handle the increasing amount of data your business would generate
  • Automatic schema detection, mapping, and evolution – Hevo detects Google Analytics schema it receives data for replication. When the sources schema changes in Google Analytics, Hevo makes changes on Amazon Redshift, thereby ensuring that the data is moved reliably
  • Exceptional Support: Hevo will always have your back by providing 24×7 priority support over Slack and email

Simplify your Data Analysis with Hevo today! 


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. 


Hevo Data provides its users with a simpler platform for integrating data from 100+ 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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