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Amazon Quicksight vs Looker vs Superset: What are the differences?
- Pricing Model: Amazon Quicksight follows a pay-as-you-go pricing model based on usage, whereas Looker and Superset typically have a per-user licensing model, which may not be as cost-effective for larger organizations with numerous users.
- Data Source Connectivity: Looker provides a wide range of connectors to various data sources like databases, APIs, and flat files, making it easier to access and analyze data from different sources compared to Amazon Quicksight and Superset, which have limited native connectors.
- Customization and Extensibility: Looker offers a robust set of customization and extensibility features, including the ability to write custom SQL queries, create custom visualizations, and integrate with third-party tools easily. On the other hand, Amazon Quicksight and Superset may have limitations in terms of customization capabilities.
- Collaboration and Sharing: Looker provides advanced collaboration features, such as data sharing, commenting, and annotations, which make it easier for teams to work together on analyzing and interpreting data compared to Amazon Quicksight and Superset, which may have limited collaboration functionalities.
- Scalability and Performance: Looker is known for its scalability and performance, particularly in handling large datasets and complex queries efficiently, while Amazon Quicksight and Superset may encounter performance issues when dealing with massive amounts of data or complex analytics demands.
- Learning Curve: Superset is considered more user-friendly and easier to learn for beginners due to its simple interface and intuitive design, while Looker and Amazon Quicksight may have a steeper learning curve for new users, especially for those with no prior experience in data visualization tools.
In Summary, Looker, Amazon Quicksight, and Superset differ in terms of pricing model, data source connectivity, customization, collaboration features, scalability, and learning curve.
We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.
Hello Mohan,
To be honest, I don't have experience working with analytics on apps and also I don't have experience with Looker, so I cannot say I will suggest that one. I know that Amplitude is a known product analytics tool for apps. I know that in the #GoPractice course, Oleg (CEO GoPractice) was using Amplitude in all his experience with mobile game apps, so I guess apps could work well too. I have experience using Amplitude for SaaS solutions and it is great to create all kinds of analytics for the product. Then Google Datastudio is the classic solution to create dashboards and reports connect it with any data source. Also, some people, instead of Amplitude are using the new Google Analytics, @GoogleAnalytics #GA4 or Mixpanel. However, my suggestion is to use Amplitude and if there are reports that you cannot answer with Amplitude, use Google Data Studio.
I hope that could help you.
Cheers,
Very easy-to-use UI. Good way to make data available inside the company for analysis.
Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.
Can be embedded into product to provide reporting functions.
Support team are helpful.
The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.
Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.
And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.
Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.
Pros of Amazon Quicksight
- Dataset versionning1
- Good integration with aws Glue ETL services1
- More features (table calculations, functions, insights)1
- Better integration with aws1
- Super cheap1
Pros of Looker
- Real time in app customer chat support4
- GitHub integration4
- Reduces the barrier of entry to utilizing data1
Pros of Superset
- Awesome interactive filtering13
- Free9
- Wide SQL database support6
- Shareable & editable dashboards6
- Great for data collaborating on data exploration5
- User & Role Management3
- Easy to share charts & dasboards3
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Cons of Amazon Quicksight
- Very basic BI tool1
- Only works in AWS environments (not GCP, Azure)1
Cons of Looker
- Price3
Cons of Superset
- Link diff db together "Data Modeling "4
- It is difficult to install on the server3
- Ugly GUI3
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What is Amazon Quicksight?
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