Business Intelligence tools like Looker and Google Data Studio are crucial in today’s data-driven business world. BI tools are software programs that help organizations make better choices by collecting, analyzing, and presenting data. In an age where there is a lot of data and it’s important for business success, BI tools are now a must-have. They help businesses find insights, spot trends, and visualize data, which improves decision-making and operational efficiency as a whole.
Looker is a powerful BI tool that is known for its advanced data research and analysis features. It lets users access and examine data from different sources, giving them a single, powerful platform for making decisions based on data. On the other hand, Google Data Studio is good at making data easy to understand and share. It works well with Google’s suite of tools and makes it easy to make reports and dashboards that are both dynamic and visually appealing.
Both Looker and Google Data Studio are good at different parts of BI. Looker is great at analyzing and exploring data, while Google Data Studio works on making data presentations that are easy to understand and interesting to look at. These tools are a must for modern businesses that want to use data to gain strategic insights and improve their performance.
Looker vs Google Data Studio Comparison table
The differences between Looker and Google Data Studio are shown in the table below: Looker is great at advanced data modeling and customization, while Google Data Studio gives easy-to-use, Google-integrated data visualization that meets different users’ needs in data analytics.
| Feature | Looker | Google Data Studio |
|---|---|---|
| Ownership | Owned by Google | Owned by Google |
| Integration | Integrates with various data sources, including Google BigQuery, Redshift, and more. | Integrates with Google products and other data connectors. |
| Data Modeling | Offers powerful data modeling and transformation capabilities. | Limited data modeling capabilities; relies on connected data sources. |
| Customization | Highly customizable dashboards and reports with LookML (Looker Modeling Language). | Customizable dashboards with a user-friendly drag-and-drop interface. |
| Visualization | Limited built-in visualization options; encourages integration with third-party tools. | Provides a wide range of native visualization options, including charts, graphs, and maps. |
| Ease of Use | Steeper learning curve due to advanced modeling features. | User-friendly, intuitive interface suitable for beginners. |
| Collaboration | Supports collaboration with data sharing, scheduling, and commenting. | Collaborative features for sharing and commenting on reports. |
| Cost | Pricing is typically based on usage and data volume. Can be expensive for large enterprises. | Free to use with Google accounts; additional costs for premium connectors and increased data usage. |
| Mobile Access | Mobile-friendly dashboards for on-the-go access. | Mobile responsiveness for viewing reports on smartphones and tablets. |
| Data Refresh | Offers automatic data refreshing and scheduling options. | Supports scheduled data refresh, but may not be as flexible as Looker. |
| Security | Strong data security and role-based access control. | Utilizes Google Cloud security standards and offers role-based access control. |
| Data Transformation | Robust ETL (Extract, Transform, Load) capabilities. | Limited data transformation features; may require preprocessing in other tools. |
| Community | Active community and developer ecosystem. | Strong Google user community and extensive online resources. |
| APIs and Extensions | Provides APIs for integration and extensions. | Supports custom connectors and third-party extensions. |
| Scalability | Scalable for enterprise-level data needs. | Scalable for small to large data sets. |
| Customer Support | Offers various support plans, including premium support. | Basic support with paid options for Google Cloud customers. |
| Visit Website | Visit Website |
Looker vs Google Data Studio: User Interface and Ease of Use
Looker is proud of its design, which is easy to understand and use. It can be used by people with different levels of technical knowledge because it works with drag-and-drop and looks good. In Looker’s setting, it’s easy to move through data sets, make reports, and build dashboards. Also, Looker’s “LookML” language lets more skilled users customize things in a way that is more like writing code.
Google Data Studio, on the other hand, is known for being easy to use. It works well with other Google products, which makes it a great choice for businesses that already use a lot of Google goods. Its drag-and-drop report builder makes it easy for even newbies to make data visualizations. But compared to Looker, it might not have as many advanced options, which could turn off some users. In short, Looker is powerful and easy to use, but it is a bit more complicated, while Google Data Studio is easy to use and works well with Google’s other tools.
Data Integration and Connectivity
Both Looker and Google Data Studio have a lot of ways to connect, so users can pull data from many different apps, databases, and file types. Looker, for example, has a wide range of data connections and APIs that make it easy to connect to SQL, NoSQL, and cloud-based databases. Google Data Studio, on the other hand, uses the power of Google’s environment to easily connect with Google Analytics, Google Sheets, and third-party data sources through connectors.
The ease of use and effectiveness of these tools depend a lot on how well data integration and connectivity work. This makes sure that users can work with their chosen data sources and formats, which helps them make decisions based on data.
Customization and Visualization
Looker has a lot of ways to change things, so users can make highly personalized reports with their own logos, styles, and layouts. Its modeling language, LookML, lets skilled users make complex data models that give them full control.
On the other hand, Google Data Studio is great at making it easy to use drag-and-drop to make reports and screens that look great. It may not be as customizable as Looker, but it makes up for it with a wide range of ways to display data, such as charts, graphs, and dynamic elements. Both tools focus on updating data in real time, so users can always get the most up-to-date information.
Collaboration and Sharing
Both tools have different features that make it easier to work as a team and share ideas. Looker lets team members explore data together by sharing dashboards that they can all view and change at the same time. It encourages openness in making decisions based on facts. Also, Looker’s easy-to-use interface makes it easy to create and share reports, making sure that ideas get to the right people quickly.
Google Data Studio, on the other hand, works well because it fits right into the Google world. This makes it easy for people to work together in a place they already know. Users can make changes and add notes to reports and screens while working together in real time. The tool also makes it easy for stakeholders to access data and connect with it by letting them share it through links or email invitations. Whether you prefer the structured collaboration of Looker or the Google-centric approach of Google Data Studio, both choices make it easier for teams to work together and make data-driven decisions more accessible and doable.
Looker vs Google Data Studio: Performance and Scalability
Looker’s speed is very good. It can easily handle large datasets and respond quickly to queries. Its in-database architecture speeds up the way queries are run, making it a good choice for companies that deal with a lot of data. Also, Looker’s caching features improve response times even more for reports that are used often.
On the other hand, Google Data Studio is known for being able to grow with its users. It works well for everyone from individuals to big businesses. It uses the power of Google’s system, so even with a lot of data sources, it works very well. The cloud-native design of the tool makes scaling easy, so companies can grow without sacrificing speed.
Looker: Pros and Cons
Pros
- Advanced data modeling capabilities.
- Strong data transformation features.
- Robust ETL (Extract, Transform, Load) capabilities.
- Scalable for enterprise-level data needs.
Cons
- Steeper learning curve.
- Limited built-in visualization options.
Google Data Studio: Pros and Cons
Pros
- User-friendly, intuitive interface.
- Seamless integration with Google products.
- Extensive range of native visualization options.
- Cost-effective with free usage for Google account holders.
Cons
- Limited data modeling capabilities.
- May require preprocessing for complex data transformations.
Which one should you consider?
Your needs will help you decide between Looker and Google Data Studio. Looker’s performance may be perfect for you if you value robust data analysis with complicated queries and have a well-established infrastructure. It does a great job of handling large datasets quickly.
On the other hand, companies that care most about scalability should choose Google Data Studio’s cloud-native design. It works well with growth and uses Google’s technology.
You should also think about how the user likes to interact with the system, how easy it is to integrate, and how much money you have. By comparing these things to the goals and resources of your company, you can figure out which platform is best for your data visualization and reporting needs.
FAQs
After buying Looker in February 2021, Google changed the name of “Google Data Studio” to “Looker Studio” in December 2021. Both Google tools, Looker and Looker Data Studio, have a lot in common.
We know that Google Data Studio was used to display data and that Looker Studio was a tool for data scientists. In other words, that’s how Looker Studio came to be. Obviously, the new Looker Studio works with the old Looker Studio. This means that you can now use your semantic models in your reports.
