Organizations use Google Analytics 360 for analyzing their website and gaining meaningful insights. Google Analytics 360 performs better than Google Analytics due to its enhanced functionality and seamless integration with standard and 360 versions of Google products such as Google Ad Manager 360, Search Ads 360, Campaign Manager, and more.

One of the advantages of Google Analytics 360 over Google Analytics is that it allows organizations to analyze and gain insights into unsampled data. With Google Analytics 360, organizations can export tracking data to different data warehouses like Amazon Redshift with standard APIs, third-party ETL (extract, load, and transform) tools, and more.

In this article, you will learn to integrate Google Analytics 360 to Redshift using APIs and manual processes. It also gives an overview of Google Analytics and Amazon Redshift.


Basics understanding of integration

What is Google Analytics 360?

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Developed in March 2016, Google Analytics is a premium version of Google Analytics used for analyzing and collecting the traffic information of websites. Google Analytics 360 helps enterprises by providing access to more advanced features such as seamless integration, hit-level data, shared audience, reporting advancements, and more.

With the free version of Google Analytics, there were many limitations such as the monthly hit limit, data freshness, row export limit, sampled data export, custom metrics limit, custom dimensions limit, and more. But, Google Analytics 360 overcomes these limits and helps businesses detect traffic and seamlessly generate customizable reports.

Difference between Google Analytics and Google Analytics 360

  • Hit Limit: The hit limit is any interaction on the website that sends data to Google Analytics. The monthly hit limit for Google Analytics is 10 million, whereas, for Google Analytics 360, it can extend over 1 billion.
  • Data Freshness: Data freshness refers to the time Google Analytics takes to collect information and create reports from your websites. The data freshness in Google Analytics can be from 12 to 48 hrs, whereas in Google Analytics 360, it is guaranteed 10 minutes to 1 hour for most reports.
  • Sampled Data Extraction: Sampled data is the subset of your web traffic data, whereas unsampled data is the entire (traffic) data used for analysis. In Google Analytics, you will get only sampled data extraction, while in Google Analytics 360, you always get unsampled data extraction.
  • Custom Metrics and Custom Dimensions: Suppose you want to analyze specific data in Google Analytics. In that case, you need to define custom metrics and dimensions for specific reporting requirements.

What is Amazon Redshift?

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Developed in October 2012, Amazon Redshift is a fully scalable, fast, and reliable data warehousing service. Amazon Redshift is a column-oriented database mainly designed to connect SQL-based clients with business analytics tools and aims to provide data to different users in real-time. Many organizations use Amazon Redshift to integrate business analyses and make informed business decisions.

You can start using Amazon Redshift with the set of nodes called Amazon clusters, then upload your datasets and perform queries for analysis. Amazon Redshift clusters can be managed by Amazon Redshift Console or the Amazon Command Line interface.

Key Features of Amazon Redshift

  • Enhanced Scalability: Amazon Redshift provides superior performance with thousands of concurrent queries. With Amazon Redshift’s concurrency scaling, you can support unlimited concurrent users and concurrent queries with fast query performance.
  • Flexible Pricing: Despite the size, every organization can use Amazon Redshift, from small startups to large corporate organizations. Amazon Redshift provides a wide range of flexible pricing models for deployments such as pay-as-you-go, on-demand, and more.
  • Robust Security: Amazon Redshift provides security to your data warehouse without an extra price. With Amazon Redshift, you can configure firewalls to control network access with a specific data warehouse cluster.
  • Fault Tolerance: Fault tolerance is the working of the systems even when some of its components fail. Amazon Redshift continuously monitors the health of your clusters and makes your data warehouse clusters more fault-tolerant.
  • Result Caching: With the result caching feature, Amazon can deliver a sub-second response time for repeated queries. Dashboards, BI tools, and visualizations that execute these repeated queries have high performance. Whenever a query is executed in Amazon Redshift, it will search the cache to get any search results from the previous queries.
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Key Benefits of Amazon Redshift

  • Speed: With the use of MPP technology, the speed of outputting large amounts of data is unprecedented. The cost AWS provides for services is unmatched by other cloud service providers.
  • Data Encryption:  Amazon provides data encryption for all parts of your Redshift operation. The user can decide which processes need to be encrypted and which ones do not.
  • Smart Optimization: If your dataset is large, there are several ways to query the data with the same parameters. Different commands have different levels of data usage.
  • Automate Repetitive Tasks: Redshift can automate tasks that need to be repeated. This can be an administrative task such as creating daily, weekly, or monthly reports.
  • Simultaneous Scaling: AWS Redshift automatically scales up to support the growth of concurrent workloads.
  • Query Volume: MPP technology shines in this regard. You can send thousands of queries to your dataset at any time. Still, Redshift is never slowing down.
  • Redshift API: Redshift has a robust API with extensive documentation.
  • Safety: Cloud security is handled by Amazon, and application security in the cloud must be provided by the user. Amazon offers access control, data encryption, and virtual private clouds to provide an additional level of security.  
  • Machine Learning: Machine-learning concepts are used by Redshift to predict and analyze queries.
  • Easy Deployment: Redshift clusters can be deployed anywhere in the world from anywhere in minutes.
  • Consistent Backup: Amazon automatically backs up your data regularly. It can be used for recovery in the event of an error, failure, or damage. Backups are distributed in different locations.
  • Partner Ecosystem: AWS is one of the first cloud service providers that started the market of Cloud Data Warehouses. Many customers rely on Amazon for their infrastructure.
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Explore These Methods to Connect Google Analytics 360 to Redshift

In addition to all of the standard Analytics features and reports, Google Analytics 360 gives your team access to advanced tools such as Unsampled Reports, BigQuery Export, and Data-Driven Attribution.

Amazon Redshift provides lightning-fast performance and scalable data processing solutions. Redshift also offers a number of data analytics tools, as well as compliance features, and artificial intelligence and machine learning applications.

When integrated, moving data from Google Analytics 360 to Redshift could solve some of the biggest data problems for businesses. In this article, five methods to achieve this are discussed:

Google Analytics 360 to Redshift: Using Hevo Data

Hevo Data, an Automated Data Pipeline, provides you with a hassle-free solution to connect Google Analytics 360 to Redshift within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process of loading data from Google Analytics 360 to Redshift and enriching the data and transforming it into an analysis-ready form without having to write a single line of code.


Google Analytics 360 to Redshift: Manual Method

This method would be time-consuming and somewhat tedious to implement. Users will have to write custom codes to enable two processes, streaming data from Google Analytics 360 to Redshift. This method is suitable for users with a technical background.

Google Analytics 360 to Redshift: Using APIs

An application programming interface (API) is a messaging system that handles requests and ensures that enterprise systems run smoothly. APIs allow data, applications, and devices to communicate with one another. It transmits data and allows devices and programs to communicate with one another. This method is suitable if you have good knowledge of API integration.

Google Analytics 360 to Redshift Integration

There are several ways to export data from Google Analytics 360, such as manually integrating Google Analytics 360 with Amazon Redshift and using APIs like Unsampled API, BigQuery API, and Core Reporting API.

Google Analytics 360 to Redshift: Using Hevo Data

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Hevo provides an Automated No-code Data Pipeline that helps you move your Google Analytics 360 to Redshift. Hevo is fully-managed and completely automates the process of not only loading data from your 100+ data sources(including 40+ free sources)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.

Using Hevo Data, you can connect Google Analytics 360 to Redshift in the following 2 steps:

  • Step 1: Configure Google Analytics 360 as the Source in your Pipeline by following the steps below:
    • Step 1.1: In the Asset Palette, select PIPELINES.
    • Step 1.2: In the Pipelines List View, click + CREATE.
    • Step 1.3: Select Google Analytics 360 on the Select Source Type page.
    • Step 1.4: Click + ADD BIGQUERY ACCOUNT on the page Configure your Bigquery account linked to Google Analytics 360.
    • Step 1.5: To give Hevo read access to your analytics data, go to your linked Google account and select BigQuery DataViewer privilege. Then click Allow.
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  • Step 1.6: Set the following parameters on the Configure your Google Analytics 360 Source page:
    • Pipeline Name: A name for your Pipeline that is unique and does not exceed 255 characters.
    • Project ID: The BigQuery project ID that is linked to your GA 360 account.
    • Dataset ID: The name of the Google Analytics 360 dataset that contains your data. The dataset ID becomes available for selection in the drop-down after your GA 360 account is linked to your BigQuery project. Contact Hevo Support if you don’t see your dataset ID listed in the drop-down.
    • Historical Sync Duration: The amount of time that previous data must be ingested. All Available Data is the default value.
  • Step 1.7: TEST & CONTINUE is the button to click.
  • Step 1.8: Set up the Destination.
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  • Step 2: To set up Amazon Redshift as a destination in Hevo, follow these steps:
    • Step 2.1: In the Asset Palette, select DESTINATIONS.
    • Step 2.2: In the Destinations List View, click + CREATE.
    • Step 2.3: Select Amazon Redshift from the Add Destination page.
    • Step 2.4: Set the following parameters on the Configure your Amazon Redshift Destination page:
      • Destination Name: A unique name for your Destination.
      • Database Cluster Identifier: Amazon Redshift host’s IP address or DNS.
      • Database Port: The port on which your Amazon Redshift server listens for connections. Default value: 5439
      • Database User: A user with a non-administrative role in the Redshift database.
      • Database Password: The password of the user.
      • Database Name: The name of the Destination database where data will be loaded.
      • Database Schema: The name of the Destination database schema. Default value: public
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  • Step 2.5: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
  • Step 2.6: Once the test is successful, click SAVE DESTINATION.

Here are more reasons to try Hevo:

  • Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
  • Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
  • Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
  • Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Try Hevo Today!


Google Analytics 360 to Redshift: Manual Method

Exporting Google Analytics 360 report

Follow the below steps for exporting the report to Google Analytics 360.

  • Open the report which you want to export. 
  • Click on Export.
  • Select the format such as CSV, TSV, Excel, Google Sheets, PDF, and more for your export.
  • Your file is then generated and is available in the download directory.

Importing Data to Amazon Redshift

You can import any data files in Amazon Redshift, but in this article, you will be importing a csv file to Amazon Redshift.

The csv file can be imported to Amazon Redshift in one of the two ways:

  • Importing a csv file in Amazon Redshift by using the Amazon S3 bucket.
  • Importing a csv file in Amazon Redshift by using the AWS Data Pipeline.

In this article, you will use the first method of importing a csv file in Amazon Redshift using the Amazon S3 bucket.

This process consists of two stages. One is loading the csv file into the S3 bucket, while the other is loading the data from the S3 bucket to Amazon Redshift.

  • Create the csv file you want to import into Amazon Redshift, load it to the S3 bucket, and zip that file.
  • When the file is in the S3 bucket, you will need a COPY command to load it to the desired table. Your file will have the below structure if it is zipped.
COPY <schema-name>.<table-name> (<ordered-list-of-columns>) FROM '<manifest-file-s3-url>' 

CREDENTIALS'aws_access_key_id=<key>;aws_secret_access_key=<secret-key>' GZIP MANIFEST;

You need to use the ‘csv’ keyword in the COPY command to help Amazon Redshift identify the file format, as shown below.

COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

-- Ignore the first line
COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

Google Analytics 360 to Redshift: Using APIs

Using Unsampled API

Users can obtain the unsampled report in Google Analytics 360 by using the unsampled API.

The unsampled API is used for:

  • Retrieving configuration information about your existing unsampled reports. 
  • It only helps you in creating one-time unsampled reports. 
  • Once the unsampled reports processing is completed, it will help you get a link to the data file.
  • Deleting unsampled reports.

Using BigQuery API

BigQuery API allows you to run super-fast queries on Google Analytics 360 data. You can export sessions and hit data from Google Analytics 360 to BigQuery and then use SQL to query your Google Analytics 360 data faster.

The benefits of using BigQuery to query your Google Analytics 360 data are as follows:

  • Accessing hit-level data.
  • Querying unsampled data.
  • Creating fast and easy-to-share reports and dashboards.
  • Combining your Google Analytics 360 data with other sources. 

However, this API doesn’t directly help you to integrate Google Analytics 360 to Redshift. Nevertheless, you can bring data from Analytics 360 to BigQuery and transfer the information to Redshift.

Using Core Reporting API

The Google Analytics 360 Core Reporting API allows you to access most of the reports in Google Analytics. The Core Reporting API performs the following tasks.

  • Building custom dashboards for displaying Google Analytics data.
  • Saving time by automating complex reporting tasks.
  • Integrating your Google Analytics 360 data with the other business applications.

The Core Reporting API returns reports that include statistics from the data collected by the Google Analytics tracking code.

The Core Reporting API is used to pull out dimensions and metrics information to build customized reports.

Limitations of Integrating Google Analytics 360 to Redshift

  • Integrating Google Analytics 360 to Redshift using APIs is a complex and time-consuming process. But, if you have the required skills or technical expertise, you can integrate Google Analytics 360 to Redshift.
  • In addition, manually exporting data from Google Analytics 360 to Redshift is easy, but you cannot work with real-time data.
  • Therefore, to eliminate such challenges, you can use third-party ETL tools that allow seamless and automated integration between Google Analytics 360 to Redshift.


You learned to move Google Analytics 360 to Redshift in this article. Data insights play a significant role in business success, as these insights are used to map the customers’ journey on the business website. Google Analytics 360 enables you to provide essential insights by using advanced features such as building automation, real-time reporting, advertising workspace, data collection and management features, progressive data governance, seamless integrations, and more.

Visit our Website to Explore Hevo

Hevo Data offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc.

This platform allows you to transfer data from 100+ sources (including 40+ Free Sources) such as Google Analytics 360 and Cloud-based Data Warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will provide you with a hassle-free experience and make your work life much easier.

Want to take Hevo for a spin?

Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Manjiri Gaikwad
Freelance Technical Content Writer, Hevo Data

Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.

No-code Data Pipeline For Redshift