Google Data Studio helps organizations create accessible, interactive dashboards and reports to make better business decisions. However, with Google Data Studio, you can not only create reports and dashboards but also seamlessly share them with your teams, friends, or colleagues. To create reports and dashboards, Google Data Studio can access data from several data sources like Google Sheets, Google Docs, Google Analytics, PostgreSQL, SQL, and MySQL. Besides, Amazon Redshift Data Studio connection can also be performed.

Amazon Redshift is an online data warehousing service that allows users to create clusters and perform queries. Google Data Studio can access data from Amazon Redshift using Amazon Redshift connectors. With the Amazon Redshift connector, you can connect to Google Data Studio through a custom query or a single table in the Amazon cluster.

In this tutorial, you will learn how to connect Amazon Redshift Data Studio together.


Fundamental knowledge of integration

What is Google Data Studio?

amazon redshift data studio: data studio logo
Image credit: Data Studio

Google Data Studio is an open-source tool designed to convert your data into interactive reports and dashboards. With Google Data Studio, you can share your dashboards and reports with your friends, colleagues, and team. However, to enhance collaboration, you can also invite your team members to edit or view reports by sending email links. Users can also embed their reports to other platforms like Google sites, blogs, marketing articles, and social media. 

Features of Google Data Studio

  1. Many widgets options

With Google Data Studio, you can include any number of widget options like heat graphs (regions, state, or country), pie charts, time-series graphs, and more. Google Data Studio also allows users to modify these widgets using a variety of metrics.

  1. Multiple data sources

One of the unique features of Google Data Studio is that it can access data from multiple data sources to create interactive reports. With Google Data Studio, users can connect with data sources like Google Analytics, Google Sheets, Google Ads, Youtube, Search Console, and more.

  1. Customizable reports

With Google Data Studio, you can create reports and dashboards using different styles, graphs, designs, and formatting. You can also customize the Page layout, Text, Graphs, Metrics, and Style elements.

  1. Share reports easily

When working in teams, you are often required to get your reports reviewed by your colleagues in order to enhance accuracy. With Google Data, you can provide access to your reports with your team or colleges just like Google Sheets and Google Docs. Your entire team can access reports and make changes simultaneously with this feature.

  1. Free templates

In Google Data Studio, there are free templates available for Google Analytics, Youtube, Google Ads, and more. Google Data also consists of templates for e-commerce, SEO reports, Content Marketing, Data Analysis, Rank Tracking, and more.

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What is Amazon Redshift?

amazon redshift data studio: amazon logo

Image credit: AWS

Amazon Redshift is a fully scalable and reliable data warehousing service provided by Amazon. It follows a column-oriented database designed to connect SQL-based clients for business analytics. With Amazon Redshift, you can scale your database from gigabytes to petabytes which enables users to acquire new business insights for their ever-growing data.

Amazon Redshift handles analytics workload with clusters made of nodes. Clusters in Amazon Redshift can be managed using Amazon Redshift Console or the Amazon Command Line Interface. However, applications developers can use Amazon Redshift Query API or the AWS Software Development Kit libraries to manage clusters programmatically.

In addition, Amazon Redshift provides performance metrics that can track the health of your clusters and databases. It uses Amazon CloudWatch metrics to monitor different aspects of your clusters like CPU utilization, latency, and throughput. Amazon Redshift also provides query and load performance data for users to monitor the database activities in their clusters. 

Features of Amazon Redshift

  1. Massively Parallel Processing

Amazon Redshift is built as a Massively Parallel Processing database. It performs implementations of columnar storage algorithms and data partitioning techniques, which gives excellent performance.

  1. Machine learning

Machine learning is used in Amazon Redshift to deliver high throughput regardless of the heavy workloads or concurrent usage. Amazon Redshift uses superior algorithms to predict incoming queries at run times and assign them to the optimal queue for faster processing. For example, queries like dashboards and reports with high concurrency requirements are sent to the express queue for immediate processing.

Amazon Redshift predicts when the queuing may begin as the concurrency increases and automatically deploy transient resources. Through the Concurrency Scaling feature, you can support virtually unlimited concurrent users and concurrent queries with fast performance.

  1. Result Caching

Amazon Redshift can deliver a sub-second response time for repeat queries with the Result Caching feature. Dashboards, visualizations, or business intelligence tools that execute repeat queries always have high performance. In Redshift, whenever a query is executed, it will search the cache to get any search result from the previous queries. If Amazon Redshift gets any cache result and its data is not changed, then instead of re-running the query, the cached result is returned.

  1. Fault tolerance

Fault tolerance refers to the system’s working when some of its components fail. Amazon Redshift continuously monitors the health of the clusters. It also replicates data from failed nodes and replaces nodes automatically. Therefore, Amazon Redshift helps make your data warehouse clusters more reliable and fault-tolerant.

Connecting Amazon Redshift Data Studio

With Amazon Redshift connectors for Amazon Redshift Data Studio connection, you can connect data sources based on Amazon Redshift and visualize the data in your Google Data Studio reports. This is the connecter used for the Amazon Redshift Data Studio connection.

The Amazon Redshift Data Studio connection Google Data Studio data source can connect to Amazon Redshift with a single table or custom query.

Follow the below steps to connect Amazon Redshift Data Studio together.

  • Sign in to Google Data Studio.
  • In the top left, click on the Create tab and then select Data Source.
  • Select the Amazon Redshift connector, as shown in the below image.
amazon redshift data studio: amazon redshift connector
Image credit: Data Studio

With the Amazon Redshift connector for Amazon Redshift Data Studio connection, you can access data from Amazon Redshift in Google Data Studio. This connector uses the Amazon Redshift JDBC driver to connect the Google Data Studio data source with the Amazon Redshift database table.

After clicking on the Amazon Redshift connector for the Amazon Redshift Data Studio connection, it will show the below window.

amazon redshift data studio: JDBC parameters for connector
Image credit: Data Studio
  • Set the Amazon Redshift Data Studio connection to your database using hostname or IP address.
    • Select the basic tab on the left.
    • Enter the connection details like hostname or IP address, Port, Database, Username, and Password. 
  • Or, just below the basic tab, there is a JDBC URL tab; click on the JDBC URL tab.
    • Enter the JDBC URL, Username, and Password.

For example:

jdbc:redshift://<hostname or IP address>[:<port>]/<database>

Whenever you connect your Amazon Redshift cluster from a SQL client, you need to know the JDBC URL of your cluster.

The JDBC URL has the below format.


The URL consists of:

  • jdbc: It is the protocol of the connection.
  • redshift: It is the subprotocol that specifies the use of Amazon Redshift Driver for connecting with the database.
  • endpoint: It is the endpoint of the Amazon Redshift cluster.
  • port: It is the port number you had specified when you launched the cluster. 
  • database: It is the database that you have created for your cluster.

To get your JDBC connection, visit the link.

Note: Google Data Studio cannot connect to localhost. You need to have a public IP address or hostname.

  • Enable SSL (Optional)

Google Data Studio provides secure connections to the servers using the Transport Layer Security Protocol (TLS). TLS is also referred to as SSL (Secure Sockets Layer). For a secured connection, you need to enable SSL and provide SSL configuration files.

  • Click on AUTHENTICATE.
  • You can select the table from the list or enter a custom query.
  • Select the custom query option to provide SQL query. 
  • Click on CONNECT.

It will open the data source field list page. Click on CREATE REPORT or EXPLORE and start visualizing your data.

Limitations of Amazon Redshift connector for Amazon Redshift Data Studio

With the Amazon Redshift connector for Amazon Redshift Data Studio connection, you can query up to a maximum of 150K rows. However, you cannot use non-ASCII characters as column names of field names as they are not supported. 


In this tutorial, the Amazon Redshift connector was used to connect Amazon Redshift Data Studio together. Besides the Amazon Redshift connector for Amazon Redshift Data Studio connection, users can also use third-party applications like Supermetrics, CData Connect, Panoply, and Onlizer to connect Amazon Redshift Data Studio together. Other connectors like BigQuery, Cloud Spanner, Google Ad Manager 360, and Campaign Manager 360 can be used in Google Data Studio to integrate with third-party applications for accessing external data.

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Share your experience of learning about the Amazon Redshift Data Studio connection in the comments section below.

Manjiri Gaikwad
Technical Content Writer, Hevo Data

Manjiri is a proficient technical writer and a data science enthusiast. She holds an M.Tech degree and leverages the knowledge acquired through that to write insightful content on AI, ML, and data engineering concepts. She enjoys breaking down the complex topics of data integration and other challenges in data engineering to help data professionals solve their everyday problems.

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