A large number of businesses use platforms like Google Sheets to store their data. Although it is easy to perform simple analysis on Google Sheets, it is not possible to perform complex analysis on the platform. Businesses might also be looking to create a Single Source of Truth for all their data which might not be easy to do on Google Sheets. Hence, businesses feel the need to set up Google Sheets to Redshift Migration and this article will provide you with two easy methods that can help you do that.

Methods to Set up Google Sheets to Redshift Migration

Businesses can set up Google Sheets to Redshift Migration by implementing one of the two following methods:

Method 1: Google Sheets to Redshift Migration Using Hevo Data

Hevo helps you directly transfer data from Google Sheets and various other sources to Amazon Redshift, Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. 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 needs required to set up Google Sheets to Redshift Migration 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.

Get Started with Hevo for free

The following steps can be implemented to set up Google Sheets to Redshift Migration using Hevo:

  • Configure Source: Connect Hevo Data with Google Sheets by providing a unique name for your Pipeline, along with details about your authorized Google account and the list of Google Sheets files you wish to load data from.
  • Integrate Data: Complete Google Sheets to Redshift migration by providing information about your Redshift database and its credentials such as database name, username, and password, along with information about port number associated with your Redshift database. You’ll also need to need to provide the schema name for your database, and its cluster, along with a unique name for your destination.

Let’s look at Some Salient Features of Hevo:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is 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.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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Method 2: Manual Google Sheets to Redshift Migration

Users can set up manual Google Sheets to Redshift Migration by implementing the following steps:

Step 1: Extracting Google Sheets Data as CSV

  • Open the Google Sheets file you wish to load into Amazon Redshift.
  • Click on File in the upper left corner.
Google Sheets File Button
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  • Click on Download As and select Comma-Separated Values (.csv).
Google Sheets Download As CSV
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  • The data will then be exported to CSV and will be downloaded to your local system. The same process can be followed if you wish to import data from multiple Google Sheets to Redshift.

Step 2: Loading Data to Amazon Redshift

Users can load their Google Sheets data to Redshift by implementing the following steps:

Amazon S3 Create Bucket
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  • Pick a suitable unique name for your AWS S3 Bucket, select a region as per requirement, and click on Create.
Amazon S3 Bucket Name
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  • Open the AWS S3 Bucket that you just created, click on Create Folder, provide a suitable unique name for it, and click on Save.
Amazon S3 Create Folder
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  • Upload the Google Sheets CSV data exported previously to the newly created folder by clicking on Upload selecting the necessary files in the Upload Wizard.
  • The data in Amazon S3 can be imported into Amazon Redshift Cluster using the COPY Command.
  • Connect to the Cluster using a SQL Workbench tool of your choice and run the following query:
COPY table_name 
FROM 's3://<your-bucket-name>/load/file_name.csv' 
credentials 'aws_access_key_id=<Your-Access-Key-ID>' 
  • If you wish to ignore the file header rows in the CSV files, then you may also specify that by running the following query:
COPY table_name 
FROM 's3://<your-bucket-name>/load/file_name.csv' 
credentials 'aws_access_key_id=<Your-Access-Key-ID>' 

Your data should now be accessible and queriable in your Amazon Redshift database.

Limitations of Manual Google Sheets to Redshift Migration

The limitations of setting up manual Google Sheets to Redshift Migration are as follows:

  • Manual Google Sheets to Redshift Migration is a complex process that might be tough to perform for someone who does not have enough technical knowledge of Amazon Redshift.
  • The process of exporting the data from Google Sheets and importing it into Amazon Redshift has to be done manually every time the data has to be updated in the Cluster.
  • Every time the data is exported from Google Sheets, it will also include the data that was imported into Amazon Redshift previously. Hence, the existing records either have to be removed manually from the exported data before they are imported into Amazon Redshift, or duplicates have to be removed from Amazon Redshift once the data has been imported.

What Can You Achieve By Replicating Data From Google Sheets to Redshift?

By migrating your data from Google Sheets to Redshift, you will be able to help your business stakeholders with the following:

  • Aggregate the data of individual interaction of the product for any event. 
  • Finding the customer journey within the product (website/application).
  • Integrating transactional data from different functional groups (Sales, marketing, product, Human Resources) and finding answers. For example:
    • Which Development features were responsible for an App Outage in a given duration?
    • Which product categories on your website were most profitable?
    • How does Failure Rate in individual assembly units affect Inventory Turnover?


This article provided you with a step-by-step guide on how you can set up Google Sheets to Redshift Migration manually or using Hevo. However, there are certain limitations associated with the manual method. If those limitations are not a concern to your operations, then using it is the best option but if it is, then you should consider using automated Data Integration platforms like Hevo.

Hevo helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence, or desired destination in a fully automated and secure manner without having to write the code. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

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Share your experience of connecting Google Sheets to Redshift in the comments section below!

Vernon D'Costa
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. He holds a degree in B.A. in Computer Sciences graduate and several years of experience within the Customer Service, Technical Support, Team Management, and writing content on various technical topics related to data engineering. His experience in the field drives him to produce accurate, assistive technical documentation.

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