Apache Superset Reviews

I have been using Apache Superset in my career for about three years, back in around 2018, because I was building an online gambling platform and we needed a cheap alternative to Tableau. We were using it because it was free, open source, and we could build all sorts of analytics on top of that. We did a lot of insights into game history and what kind of bets there were. Now, I am building another blockchain platform and I'm making the data useful from different sources, such as different blockchains and contracts.

What I appreciate the most about Apache Superset is that it's free and easy to set up. That's probably the best feature. It connects effectively with ClickHouse, and that's the killer feature because it helps me leverage the data in the fastest way. It's flexible in terms of SQL, converting queries into something meaningful.

For this deployment, I didn't need a team; I could have done it alone because it's a Django application as far as I can remember.

Apache Superset is open source, allowing for extensions if someone is familiar with Python. Especially now, in the age of live coding, someone could build a plugin or authentication easily, which would fit into any infrastructure quite straightforwardly if someone knows how to do it for cheap. Some companies and teams may not have the budget for Tableau or Power BI, making Superset a perfect fit.

Apache Superset needs more rich charting capabilities. Second, it could benefit from more real-time capabilities, such as real-time streams over WebSockets. Also, permissions-wise, it should be less confusing because the permissions are a bit misleading. I don't know how to ensure that particular categories or roles have specific permissions while others don't.

I understand that there has to be some balance between flexibility and simplicity, but I think it's skewed too much towards flexible and less user-friendly. For example, if I want to set up some dashboards that everyone can use online without authentication, it's really hard to understand. There's this public user, but you need to dig deeper into Python code to understand how to make it secure to let people use it and at the same time hide something that you don't want to show.

I have been using Apache Superset in my career for about three years.

The initial deployment of Apache Superset is quite straightforward, especially with Docker containers, but I think it could be simplified to some degree. For me, having dealt with this for over a decade, it wasn't that hard.

The first time I deployed Apache Superset, it probably took around one day to fully deploy it. I think it would be a bit hard to remember if I set it up with Docker or without at that time.

Regarding the stability and performance of Apache Superset, its stability is good, as there hasn't been very intense use of it because we were limited in terms of providing dashboards and business intelligence, so it worked as expected with no issues. Performance-wise, since all the heavy-duty work was done by the databases in the underlying layer, there's not much else to report.

About the scalability of Apache Superset, I can't really say much because we were limited; we had one instance of ClickHouse and just used it in connection to this, so we didn't reach a point where we needed to scale it in some way.

I have never contacted any sort of customer support or technical support regarding Apache Superset. I don't recall needing to because it had quite substantial documentation and a GitHub page. While I did encounter some issues, I didn't reach the point where I had to seek support.

Positive

I have used alternatives to Apache Superset such as Metabase to some extent, but it was very limited and from what I understood, it's a NoSQL kind of tool, because I think you could use it with Mongo. However, I'm more comfortable with SQL databases, which is why I decided to use Apache Superset. Also, there's Redash, or I think the Red Hat one, but I haven't used it so far.

I would rate Apache Superset a very hard eight out of ten. There's some room for improvement, but for indie developers and small startups who need some kind of intelligence, it's probably the best on the market. In terms of value provided, flexibility, and ease of setup and operation, Apache Superset excels. I think Redash might be close enough, but they have constraints regarding what's available for free and what is paid. In that sense, Superset wins.

We use Apache Superset to analyze the telecom data for our customers. We can create reports by directly accessing the data source.

The product needs improvement in terms of DECT filters. At present, we have only a range or time stamp filter. They could provide more options.

The platform's reporting feature needs enhancement. There could be an automatic alignment of headings similar to Tableau, ensuring that if the length exceeds a certain limit, it should transition to the next line while maintaining alignment.

We have been using Apache Superset for two years.

The platform's stability depends on how we deploy the application.

It is a scalable platform. Some of our customers manage 50 users, some manage 100, whereas others manage 200 users for Apache Superset.

The scalability for growing datasets depends on the database performance. Various factors can affect performance, including how we configure the database, the purpose of installation, and the use of appropriate memory.

I rate the scalability an eight and a half out of ten.

We receive good technical support services from the GitHub community. Whenever we raise any concerns, they respond. Even if they take time, they come back with resolutions.

The initial setup involves VPN, VM, and cloud deployment. However, deploying microservices is currently in the research and development phase. We have yet to conduct the implementation.

Apache Superset has a three-year licensing model. A free version is available from Microsoft. There is a chargeable version for the cloud as well.

It is a good visual solution tool in an open-source category. Our customers want to improve the business into a SaaS model. They analyze the telecom-based transaction data with SaaS, including the number of subscribers, usage of 4G and 5G networks, etc.

The platform improved data analysis for our customers by providing a visualization library. We can drag visualization graphs to create weekly sessions. There is no need to implement any extra coding. It has no code interface allowing us to track the dimensions and measure the canvas. It automatically generates the chat once we select the graph.

The most efficient features are data set creation and data manipulation. We can directly use the raw data table and summarize it dynamically by processing the data manipulation window.

SQL Editor enhances the data scoring process, helping us write queries directly during dashboard discrepancy issues. We can store the query for future analysis as well. It enables a customizable integration with other data sources.

The main benefit of using the product is the ability to access the data source without using any coding. Any user can create reports easily with minimal training.

I recommend Apache Superset for customers who are considering open-source vendors. I rate it an eight out of ten.

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.