Apache Superset Reviews

We are a service company that creates solutions for customers and we deployed this solution for one of our clients last year. Its main use case is for dashboards for customers wanting access to data. Primarily this was genomics data and the aim was to create a report in the dashboard. Dashboards are then embedded in the web application. We are customers of Apache. 

Superset has great features including the FSO, mapping roles, and ensuring that dashboards can be embedded in the web application. The solution also supports a rich set of charts that is very helpful. Superset also has a myriad of writing queries, allowing users to write queries for the data sets that they need access to. Users can create dashboards on their own. This is a self-service kind of toolset without reliance on developers to create dashboards.

Being an open-source solution, a lot of documentation is not available so you have to go through tutorials on the internet; sometimes the documentation is outdated and therefore misleading. Finding the updated information requires a lot of investigation. There is some bridging documentation available, but you need to go through it and figure out which one fits your use case.

The basic to medium-level documentation is okay but when it comes to advanced things like authentication, integrating with identity provider roles and mappings, things can definitely be improved. The automation in terms of APIs for automatically creating roles, and giving privileges to the user can also be improved.

I've been using this solution for two years. 

The solution is stable. 

The load we required was not that large, somewhere between 50-100 users, but based on the design the product is pretty scalable because the entire load is put onto the underlying data source. Superset only acquires the query and gets the data. It does not provide a highly parallel engine similar to Power BI or  QuickSight.

Superset does not offer commercial support. There is community support but I've never received a response to anything I've posted. In that respect, it's a challenge. 

This was a large project that ran for about eight months. We were able to use templates for the deployment  but a lot of effort went into customizing the security. We had one DevOps person working on it. We provide maintenance support for our customers. 

It's difficult to quantify but the ROI is good. 

Anyone considering this solution needs to look at the pricing in two ways. The first is licensing cost and the second is the operational cost. To run Superset, for instance, requires a fixed container. The customer has to carry out a cost analysis as to whether Superset or a commercial tool is better. 

I think that Power BI, Tableau, and Superset are the top tools in this market. Amazon's QuickSight is much less mature and when you ask about new features they claim that it's on the roadmap but it never eventuates.

I rate this solution eight out of 10 because there could be some improvements in the commercials similar to what Tableau or Power BI offers. The advanced use cases aren't documented well and it does not have an intermediate cashing layer that can split up queries. Superset puts that entire responsibility on the domain user. I recommend this solution if you have a well-versed development team in Python.

Our main use for Apache Superset is data visualization. We use it daily to manage KPIs and data metrics through customized dashboards, aiding in decision-making processes within our organization.

Apache Superset has streamlined our data visualization and analysis processes, enabling less technical team members to handle data-driven tasks independently, thus speeding up our operations.

The no-code interface is the most valuable as it allows us to operate without constant support from the data engineering team, fostering a self-service environment.

Apache Superset could be improved by enhancing its interactivity and engagement capabilities. While it serves well for data visualization, it lacks more dynamic features that allow users to interact with data directly through the dashboard.

For the next release, including interactive widgets and reporting tools that allow actions on digital assets directly from the dashboard would be beneficial. This functionality would make Superset a more comprehensive digital analytics platform suitable for interactive data exploration and operational management. Implementing actionable buttons for security controls and dashboard management could significantly enhance user interaction, making it more suitable for real-time data manipulation and decision-making.

I have been working with Apache Superset for four years.

The platform has proven stable in our use; I rate its stability at eight out of ten.

While we have not needed to scale it, its open-source nature suggests that scalability should be manageable.

The initial setup was straightforward and completed in a couple of hours by our data engineering team. I rate the initial setup a eight out of ten.

We implemented it in-house without the need for external vendor support.

While exact returns are difficult to quantify, the time saved and the autonomy it provides our team undoubtedly add significant value.

Apache Superset is open-source and free.

Overall, I rate the solution an eight out of ten. 

We utilize Apache Superset to observe our service performance and determine its efficiency. This involves identifying areas where we may deviate from our targets, such as profitability or agent productivity, and analyzing trends within historical data to gain insights.

The most valuable feature of Apache Superset is the easy way to configure dashboards as reports or analyses and it's easy to use and intuitive. Users do not need a lot of training to use the solution.

Additionally, you can easily build and define a KPI, and reuse them in different ways. The component logic is highly beneficial for us as it enables us to reuse the same information in various reports. This is particularly useful when multiple individuals are creating dashboards or monitoring the same KPI, as it ensures that everyone is using consistent data from the same source. This approach helps us maintain data integrity and ensures that we all start from the same foundation.

Not only is it intuitive and user-friendly, but it is also easy to integrate into our existing dashboard and cockpit. This allows us to seamlessly incorporate it into our management dashboard.

Dynamic dashboarding could improve to enable smooth navigation when transitioning from a higher to a lower view, allowing for easy accessibility.

I have been using Apache Superset for approximately one year.

The solution is stable.

Apache Superset is scalable because it is on the cloud. If we add new users it is simple. For example, we went from 30 to 300 users easily.

I have not used the support from Apache Superset.

We previously used QlikView and QlikSense. The cost of using this solution was not sustainable at the time and we switched to Apache Superset. 

When using QlikSense we needed a specialized worker to configure the dashboards for our needs. However, with Apache Superset is it an easier process and the person creating the dashboard does not need to be a specialist.

The deployment of the solution was easy.

To implement Apache Superset, we initially set up a test environment with basic configurations and initial dashboards. After testing, we deploy it in the production environment and enable user access. We provide users with training, including a brief one-hour handbook covering the basics. We leverage the user-friendly features of Apache Superset, allowing them to easily and quickly create their own reports, dashboards, and analysis.

We spent approximately one to one and a half months testing to ensure that the implementation was smooth and that the data was accurate. We wanted to ensure that everything was working seamlessly before rolling it out. The implementation was rolled out in June.

We did the deployment of the solution in-house. Data analysts and engineers and the training and KPIs departments were involved in the implementation process.

The price of Apache Superset is less than some of its competitors.

We did not evaluate other options before we selected Apache Superset.

The solution does not require a lot of maintenance.

I rate Apache Superset an eight out of ten.

We used the solution to help people make decisions based on real numbers and statistics.

By introducing this tool with a semantic layer and a huge amount of charts, lines, tables, and other items, all of that helps to show the KPIs you want to.

Cross-Filters when you click on any chart, you can apply the filter without any effort on the charts you need. 

This is useful when we don't have to put filters on a specific section or segment of the page.

With Apache Superset, we had some problems with the permissions when we had too many users. Some permissions were not really clear even after reading the documentation.

I have been using Apache Superset for 11 months.

I haven’t faced any issues with the solution’s stability.

I rate the solution’s scalability a nine out of ten.

Apache Superset is an open-source solution.

The SQL editor (SQL-Lab) is good to use when we don't have our own editor or when you want to try something fast.

Apache Superset is easy to use because you can visualize everything with wizards.

Overall, I rate the solution a nine out of ten.