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.
What is Google Data Studio?
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
- 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.
- 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.
- 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.
- 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.
- 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.
To learn more about Google Data Studio, check out our blog.
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What is Amazon Redshift?
Image credit: AWS
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It enables fast query performance using columnar storage technology and parallel processing to handle large volumes of data efficiently.
Key Features of Amazon Redshift
- Scalable Performance: Redshift scales to handle petabytes of data with high performance, using massively parallel processing (MPP) and columnar storage.
- Managed Service: It is a fully managed service that automates administrative tasks like backups, patching, and monitoring, reducing operational overhead.
- SQL-Based Queries: Redshift supports standard SQL queries and integrates with popular BI tools and SQL clients, making it accessible for users familiar with SQL.
- Cost-Effective Pricing: Offers pay-as-you-go pricing with options for reserved instances to optimize costs based on your usage needs.
- Data Integration: Easily integrates with AWS services like S3, DynamoDB, and data lakes, as well as other ETL tools, facilitating data ingestion and management.helps make your data warehouse clusters more reliable and fault-tolerant.
How to Connect 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.
Step 1: Sign in to Google Data Studio.
Step 2: In the top left, click on the Create tab and then select Data Source.
Step 3: Select the Amazon Redshift connector, as shown in the below image.
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.
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Step 4: After clicking on the Amazon Redshift connector for the Amazon Redshift Data Studio connection, it will show the below window.
Step 5: 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.
Step 6: 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.
jdbc:redshift://endpoint:port/database
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.
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.
What are the 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.
- Limited Data Source Support: The connector is primarily designed to work with Amazon Redshift, which limits its integration with other databases or data warehouses directly without additional configuration.
- Performance Bottlenecks for Large Queries: When running complex or large queries through the connector, users may experience performance delays due to data transfer between Redshift and the Data Studio environment.
- Restricted Functionality for Advanced Queries: Some advanced Redshift-specific functions and optimizations, such as Redshift Spectrum or materialized views, may not be fully supported or may require manual adjustments when used via the connector.
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Conclusion
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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Hevo can help you integrate your data from numerous sources and load them into a destination to Analyze real-time data with a BI tool such as Tableau. It will make your life easier and data migration hassle-free. It is user-friendly, reliable, and secure.
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FAQ on Redshift Data Studio Deployment
Is Amazon Redshift a ETL tool?
No, Amazon Redshift is not an ETL (Extract, Transform, Load) tool. Redshift is a data warehouse solution, although it plays a crucial role in the ETL process. ETL tools are used to extract data from various sources, transform it into a suitable format, and load it into a data warehouse like Redshift.
Is Amazon Redshift a SQL database?
Yes, Amazon Redshift can be considered a SQL database because it uses SQL (Structured Query Language) to interact with the data. However, it is specifically designed for large-scale data warehousing and analytics rather than transactional processing, which is typical for traditional SQL databases like MySQL, PostgreSQL, or SQL Server.
Is Redshift a database or data warehouse?
Amazon Redshift is a data warehouse. It is designed to store and analyze large amounts of structured data efficiently. Unlike traditional databases, which are optimized for transactional workloads (OLTP), data warehouses like Redshift are optimized for analytical workloads (OLAP).
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.
