Apache Superset Reviews

My major use case for Apache Superset is anything related to analytical dashboards.

The biggest advantage that Apache Superset offers for me is the flexibility of creating different types of charts in dashboarding, and beyond that, the alerting mechanisms are tightly integrated with Slack for all engineering alerting.

I don't see any impact on performance using the SQL Lab in Apache Superset as it is much better; the caching is efficient.

We use a drag-and-drop interface for visualizations.

I definitely see some disadvantages in Apache Superset, particularly in the tagging feature which is not up to the mark, creating a little bit of mess for the administrators.

We work with a Role-Based Access Control feature in the product.

I assess this feature as good; they have permissions which are in more plain English, but there is a little bit of convenience missed out because if I have to create a super user apart from admin, I need to add all the permissions manually.

I have been using this product for more than four years now.

It is stable for my use case, although migrating from one version to another requires a database migration, which is not seamless compared to most other SaaS tools.

The solution is easy to scale because it has client and server-side cache, and we have run it on an XL machine without seeing any downtime.

Apache has very good software documentation, and most issues are usually answered through a simple Google search.

I would rate tech support an eight, primarily because of the UX improvements that need to be made.

Positive

The installation of Apache Superset is straightforward; it has an operate config file with Python that brings more flexibility.

I see savings from using Apache Superset in both time and money, particularly with the cost reduced by roughly 99% compared to Tableau where we previously paid almost 50k annually.

Apache Superset, as a self-hosted instance, is much cheaper than Tableau, which cost almost 50k for our use case compared to the maximum $1,000 a year for Apache Superset.

In terms of dashboarding tools, I work with them on a daily basis and can help with Apache Superset.

We use Apache Superset for dashboarding. I always use it for read purposes, and we don't manage anything on the data lake per se since data lakes are powering the dashboards.

For example, with Snowflake metadata, we have created a custom dashboard in Apache Superset where we can track the usage in terms of credits and remaining credits.

On a scale of 1-10, I rate Apache Superset an 8.

I am an end user of Apache Superset.

My team and I use Apache Superset for querying API requests from our external partners or any other data we need to support our clients. We utilize a tool in Apache Superset called SQL Lab, where we can write SQL queries to find specific information in tables or databases that our partners request or that we need for troubleshooting specific case issues. This is the main purpose of it. Additionally, we connect Apache Superset with our Salesforce instance to check metrics regarding charts and SLA cases, such as the SLA performance from the previous week. For instance, we might find that last week we closed 200 cases and we can analyze how many of those cases met the SLA time frame or how many failed.

Apache Superset is used broadly within teams at Airbnb for different purposes; we also build tools to quickly search necessary logs. For example, if we troubleshoot why a reservation didn't enter the system, we can enter the reservation ID and see all the necessary logs, including when the reservation was created, whether it was canceled, and the details surrounding it. With the timestamps available in Apache Superset, we can easily track all specific steps regarding that reservation.

I leverage Apache Superset's SQL editor capabilities for data querying. My team and I have a collection of pre-written queries that facilitate our work, allowing us to not rely heavily on the SQL editor for creating new queries. For instance, if someone starts in a new team and isn't familiar with Apache Superset, they will depend more on this tool as they learn; however, we are already accustomed to using pre-written queries, so we just need to modify a date or a few variables.

The most valuable features of Apache Superset include its cloud accessibility, allowing us to access it from anywhere. We can set up different databases such as PostgreSQL, MySQL, SQLite, and Oracle depending on our needs. We can build various types of charts such as bar charts, line graphs, and maps, and customize these visualizations to communicate insights effectively to team members who might not be familiar with how Apache Superset functions or to external clients.

For example, we can show them integration health metrics for the last month, indicating what percentage of reservations were received or what the failure rates are. Moreover, Apache Superset allows us to set up alerts; for instance, if the failure rate exceeds 1.5%, we receive an alert to address the issue with our integration partners. We can also connect seamlessly to multiple data sources, including Minerva SQL, Trino, or StarRocks.

My impression of Apache Superset's customizable dashboard features is quite positive. For instance, if we track the integration health of our partner's system, we send them a webhook that needs to be responded to within 10 seconds. These webhooks are crucial for entering reservations into their system, as failing to do so could mean losing revenue. If a partner's system fails for a significant number of reservations, Apache Superset can visually display these metrics, alerting us when the failure rate exceeds a certain threshold. We can use this information to discuss with the partner what went wrong during a specific period, such as from July 26th to August 1st, highlighting the spike in failed notifications. This visual representation is helpful, especially for non-technical staff, as they can easily understand the stats and follow along with a presentation showing how we want the metrics to perform.

Areas of Apache Superset that could be improved include enhancing the data visualization features and addressing the overall data sourcing limitations, such as the 800 kilobyte limit per query that sometimes restricts our analysis, especially if we need to access data from many years back. I am unsure about the tier my company subscribes to, whether it's a platinum or golden tier, but this is something I would want to see improved in the future. Everything else has been quite helpful for me.

I have been working with Apache Superset for roughly two and a half years.

Regarding the stability and reliability of Apache Superset, I would rate it a 7. In my first year, we experienced a few short incidents where Apache Superset did not load properly, even though the data was available. This might have been due to integration issues with my company. Last year, I encountered this frequently while changing Wi-Fi networks, including on the company VPN. However, I didn't experience these issues this year after changing networks and routers, which is a positive update.

I would rate the scalability of Apache Superset an 8 on a scale from 1 to 10. The scalability rating of Apache Superset is influenced by the data sourcing limitation; this is the primary concern that comes to mind.

I would rate Apache Superset a 9 out of 10. I definitely find it very reliable and capable for my needs. I have access to all the charts I require for my work, and I can source data quickly while saving my queries in history for future use. There are no issues with changing network providers, and I can load from anywhere, which contributes to a very positive experience overall.

We build nonprofit organization analytics using Apache Superset in this system.

I am working only with an on-premises setup of Apache Superset, having set it up locally in Docker Compose. I don't use the cloud version because all data is stored locally.

I trust Apache, which is why security is critical for us, making Apache Superset a trustable open-source product. It's easy for clients and for people who haven't used BI tools; it's good for them to start with Apache Superset.

It's very easy, reliable, and trustable because it's an Apache open-source product. In general, I find Apache Superset easy to customize; one case is that I needed to add the ClickHouse plugin. Apache Superset is very useful because you can install everything you need using Python and different libraries, making it very useful in this area.

I appreciate the capability in Apache Superset that allows you to separate queries and dashboards and reuse them.

Apache Superset is a very easy tool to set up and start working with, so I find its UI/UX very useful, enabling me to get started quickly.

Apache Superset has good features for supporting multidimensional analysis, but I need more ability to customize. This depends on different tools used for different customers or users; Superset is good for users with limited experience as for technical people and analysts.

It's a good tool, but sometimes you need more customization. Building custom graphs was hard compared to Grafana, which is much easier. Generally speaking, it's a different level of customization, so if you need super customization, you might consider using other tools. But for starting out, it's a good tool.

One thing I would add is folders because having 1,000 queries makes it horrible to understand which query was written and for which dashboard, with graphs. I would want to see more features such as filters and folders, and improvements in how to organize queries and dashboards overall.

I had issues when trying to export and import dashboards; I could not do it on the first attempt. I spent a whole day trying to fix it because I built the dashboard on one laptop, exported it from the web version, and then struggled to import it on another laptop. I believe this process is critical and fixable, but in my case, I could not do it. This led us to consider other tools for continuing our work. It would be great to improve the reliability of the import and export features.

For our tasks, Apache Superset's integration with authentication protocols and role-based access control was sufficient, but it would be great if we could create teams and organizations. It actually depends on the size of the company; for small companies, I think it's more than enough. For enterprise or large companies, I would say it may need more advanced roles and organization features.

I have been working with Apache Superset for around one year, a little bit less than one year.

In general, I have not experienced stability issues or crashes with Apache Superset, except for the import/export concerns which are critical. Everything else is good.

I can't provide feedback on the scalability of Apache Superset since we don't have a lot of data. Comparing it with Grafana, I am not ready to comment on performance under load because I haven't tested it with large datasets. But based on basic data, I can say that it uses about half the memory compared to Metabase.

The implementation process for Apache Superset was very easy and straightforward.

I am looking into Grafana and Metabase as alternatives to Apache Superset. I appreciate Metabase for its tab feature, which helps organize graphs better.

Metabase has some AI capabilities. However, if you're building something enterprise-level, I think AI might not help you a lot; it could assist a little with simple queries but not with complex custom queries, making me unsure about its utility.

I am more using SQL for data querying within Apache Superset.

I read a very good book that gave me a simple piece of advice for organizations considering Apache Superset: it is essential to understand who the users will be for your tool.

When you start working with any tools such as Apache Superset, you should first understand the features and the goals and challenges you will face. You need to know who will be using it and interacting with your graphs.

If you have experienced users with a lot of data, you may consider other tools. But if you want a quick start for your analytics platform or to build something fast, Apache Superset is a good solution.

My rating for Apache Superset as a solution depends on the company size. For small companies, if they fix the import/export issues, I would rate it a 10. For large companies, it may not be suitable, and for medium-sized companies, it depends on their experience with analytics. In general, I rate Apache Superset an 8 out of 10, providing a 10 for small companies and an 8 for large ones.

In my experience with Apache Superset, I don't find myself using it as frequently as intended because most projects require dashboard capabilities alongside additional functionalities. There was one customer project for an automobile rental provider where it was specifically suited for their needs. They had their database in place, and we could connect Apache Superset to their existing database. They needed to track vehicle locations through map views, monitor rental economics such as monthly kilometer usage, revenue generation, and identify the best-performing vehicles. Apache Superset could handle all these requirements, and we embedded the dashboard into their website. While their website maintained rental functionality, managers and vehicle providers could access the dashboard to monitor vehicle performance.

For most other use cases, implementation wasn't as straightforward. Dashboard embedding from Apache Superset was the core feature that allowed it to function alongside other systems. While I always wanted to utilize it more, sometimes it proved challenging when data manipulation was required beyond visualization. Additionally, there are some limitations regarding the data sources that Apache Superset can access.

The filtering capabilities in Apache Superset worked as expected and were straightforward to use. Users can have numerous customizable dashboards and different types of graphs. The dependent filtering feature is particularly powerful, allowing business people to see interconnected information across different visualizations.

Being able to quickly build standard dashboards around relational data in Apache Superset provides tremendous visibility for everyone on the team. It can significantly benefit any organization whose data exists in relational form. Creating insights with Apache Superset is theoretically very appealing for any company with a relational database, offering tremendous advantages.

Customizing dashboards in Apache Superset presents challenges, particularly with embedding them. Enabling feature flags, such as the embedding feature or Jinja templates, can be cumbersome. The process of figuring out how to enable these features and testing Jinja templates can be challenging. While there are some rough edges around certain features, the overall complaints are minimal.

I deployed Apache Superset using Docker Compose, and in my previous position, my supervisor deployed it using Helm charts and Kubernetes. The deployment process was straightforward without significant difficulties.

Neutral

My advice for organizations considering Apache Superset is to try it. Organizations should understand that Apache Superset will be somewhat slow and prototypical. It can provide a good foundation for showing their data and understanding their visualization needs. Later, they could develop a visualization dashboard from scratch, but Apache Superset offers a rapid start. It's accessible and easy to use, even for those without SQL knowledge, provided they understand the database they need to visualize.

On a scale of 1-10, I rate Apache Superset a 7 out of 10.

We are not paying for Apache Superset because it is free, so we are a customer.

We use Apache Superset internally in our company.

Our usual use cases for Apache Superset are as our BI tool; we use it for dashboards, charts, ad hoc analysis, and SQL queries. We use it as the front end of our data stack.

The features of Apache Superset that I have found the most valuable so far include its flexibility, allowing users to do whatever they want. Since I work in SQL extensively, it is very easy to go from SQL to dashboard and dashboard to SQL, which is also a useful feature. Every dashboard allows you to look up the SQL query for each dashboard, so you can easily rebuild it based on the automatically shown structure.

It is Python and Flask-based, so it is pretty handy to be able to reprogram your own BI tool depending on your needs, instead of waiting for updates from a bigger company such as Microsoft where you do not have ownership of the code or Power BI.

Apache Superset has definitely helped me in visualizing business trends and patterns and has contributed to improvements.

I have not upgraded to the newest version of Apache Superset, so I do not know exactly what improvements have been made. They improved SQL error alerts, but I would also like to see how they are handling canvas objects or CSS objects in text form. Dark mode would be the main thing I would like; it does not really work because the chart text cannot be white in a dark mode setup, so it is not feasible.

I think it is fine to leave my company name, but I would like my personal name to remain anonymous.

I have been working with Apache Superset for about two or three years; I do not know exactly in my head, but it is about three years.

Overall, I assess Apache Superset's stability as super stable—I would rate it an 8 out of 10. Stability depends on your setup more than Superset itself since it is a browser front end, and while it has some issues, they are not anything major.

I cannot rate the scalability level of Apache Superset from 1 to 10 concerning user scalability because I am not part of the team managing that, but in terms of cost scalability, it is definitely a 10. The costs are insanely cheaper since we run it on our own machines, and even with a significant increase in size, the costs would not approach what it would cost with other services such as Tableau.

I do not think Apache Superset has technical support, though they may have it through Preset. I have communicated with Preset's sales team for a managed solution, but I have not reached out to their customer support since I am not a paying customer.

Neutral

Before using Apache Superset, we used Power BI and Snowflake for these use cases.

We decided to switch from Power BI and Snowflake to Apache Superset because there were issues we had, and we could not get in contact with anyone who could fix them. For Snowflake, we were moving our data warehouse into AWS and did not want a double data warehouse solution.

I looked at the code, but I did not participate in the deployment and initial setup of Apache Superset.

Before choosing Apache Superset, we evaluated other options and vendors, including Tableau and Power BI, partly sticking with Power BI but on a different setup, and also looking at some other tools.

We chose Apache Superset over other options mainly because the SQL editor was a big factor, as Tableau has something similar, but it is not quite a SQL editor. We also wanted to manage users ourselves, so user management is fully ours, avoiding reliance on a third party. The cost per user was a significant reason for our choice, as we have an infinite number of users at no cost, which offers scalability we own.

Apache Superset is very good at slicing, dicing, and fencing users so they can also do their own slicing and dicing. It works similarly to other tools; I do not see how Apache Superset differs from Power BI, Tableau, or others in that respect.

I leverage Apache Superset's SQL editor capabilities for data querying extensively. We can write queries in the back end, which takes a lot of testing time, while a lot of quick ad hoc stuff can be done in SQL. You can perform ad hoc queries that would have taken one hour in another tool because of the need for setup, and then you can visualize them directly from the SQL query if you need to look at something visually.

Apache Superset's integration with authentication protocols and role-based access control has not influenced our organization's data security strategies. We were allowed to use it because of how the role-based structure is set up.

The main benefit I have seen from working with Apache Superset so far is the speed from discovery and SQL to making charts and dashboards, along with its customizability. We are planning on building a chatbot inside of Superset, which we can build ourselves in-house and plug in directly as a plugin without needing to consult a company.

Superset's deployment options are pretty flexible; it is not fully agnostic, but it is agnostic to how you deploy it. We deploy it locally, and the databases themselves are flexible in SQL terminology since it is using SQLAlchemy. However, it does not allow NoSQL or more advanced SQL languages, but it works with our current setup.

My impression of the documentation is that it is usually quite good for the simple tasks, but it can easily become outdated. Most things you can do in Superset are based on SQL, so you do not really need much guidance.

I would rate Apache Superset a 10 out of 10 for my job, but for a more normal user, I think it would be more of a 7 or 8 out of 10 because it has some drawbacks compared to Power BI, particularly in cross-filtering and user-friendly filter handling, which can be a bit daunting for first-time users.

At that moment, I was working in a Bangladeshi company named OrangeBD as a tech lead for a product named Muktopaath, which is an e-learning platform. For that reason, we needed to create a search for our e-learning platform courses, so I used Apache Superset for this purpose.

What I appreciate most about Apache Superset is its text search feature, including searching with small and capital letters. When searching directly from our database, it is very efficient, which is why we are using Solar Search, as it puts no pressure on the database. This searcher provides advanced features for database queries, offers recommendations, and it also corrects spelling mistakes to suggest appropriate courses.

When comparing Apache Superset with Elasticsearch and Solar Search, it lacks some features that come with Elasticsearch, such as Kibana. To improve it, I would suggest adding features similar to Elasticsearch and Kibana. While Solar Search is more powerful, it doesn't have as many features, so we can enhance its usability by connecting it to other databases; it can also be used for log search. Elasticsearch is popular because it connects to Kibana, which has a very good visual presentation. I believe if Apache Superset added features found in Elasticsearch, it would be more popular and usable.

I have been using the solution, Apache Superset, for about three years.

The initial deployment of Apache Superset was easy. Its integration is very straightforward. I found that when I chose Elasticsearch, its installation, settings, and integration with my application were also very easy; I could implement everything in just two or three days.

It took me only one day to deploy it for the first time because I encountered complexities with the version differences, between version one and version two of Muktopaath. When I upgraded my version, the previous version did not support my new server installation, making it necessary for me to find and implement my previous versions' plugins. Although I could resolve it, I found it easy, aside from the version problem that took additional time.

Apache Superset is stable, with good plugins and settings; the server setup is also stable, and after configuration, it works fine without issues.

In terms of scalability, while I don't have much experience scaling it, I can handle substantial data from about 23 lakh users on our Muktopaath platform using Solar Search. I believe it is powerful and can support a significant amount of text search.

I haven't contacted their technical support or customer support because I only needed search and recommendation features. The free version meets all my needs, so there was no further requirement to contact customer support. I haven't even explored the pro version of Apache Solar; I've only used the free version. However, I do use the pro version of Elasticsearch, as I have the opportunity to do so currently at Rakuten.

Neutral

I have used similar solutions.

Elasticsearch is one of the similar solutions I have used.

At this moment, I think Elasticsearch has very good functionality and visualization, and it works perfectly. However, when I think about only search capabilities, Apache Superset is fine, but overall functionality, Elasticsearch is superior.

At this moment, I don't have any partnerships with Apache; I am just a developer using the free version for developing any necessary search or recommendation tools. I haven't explored the pro version support yet.

I am using the free version of Apache Superset.

Apache Superset does suggest upgrades, but I don't upgrade because all my code is based on the old version; I prefer to stick with that.

On a scale of 1-10, I rate Apache Superset a 7.

Apache Superset is used for data visualization for reporting and dashboarding.

Apache Superset is a lightweight reporting tool with a lot of functions and flexibility, where you can build the dataset, build charts, and dashboards from the user interface. That is the most interesting feature wanted, and while other reporting tools such as SAP can do that, they are too heavyweight. Apache Superset is easy to use, easy to install, and very lightweight.

The drag-and-drop interface for visualization is very satisfactory.

The analytic part of Apache Superset has not been explored at this time, and the system also does not require that. Going forward, if some kind of analytic capability is needed, another tool such as an AI solution may be considered.

One potential area for improvement in Apache Superset is that it must be installed in a dedicated Docker image, which could be a limitation since the goal is to embed Apache Superset in a product offering. If it could be embedded into a product's Docker image, that would be beneficial. Apache Superset should consider making integration easier so it can be embedded in other applications.

The functionality is satisfactory, and it would be beneficial if it were easier for integration into third-party applications.

Apache Superset has been used for one year.

The stability of Apache Superset is rated at eight out of ten.

Scalability regarding Apache Superset has not been considered.

No technical support from Apache has been accessed, so there is no experience with that.

Negative

Open-source solutions such as Apache Superset will be used since a heavyweight solution is not needed, only for reporting purposes.

The initial setup process was not personally completed; it was handled by one of the developers.

There is very limited knowledge about competitors such as BIRT or Grafana, and a technical evaluation was not conducted, so there is not much commentary available.

Apache Superset is primarily used for work purposes.

Role-based access control features in Apache Superset are not needed since there is no requirement for that. Apache Superset is currently not integrated with a data lake solution, as the use case is very simple, involving only connecting to a database.

Regarding Apache Superset's SQL Lab, there is no awareness of obvious performance problems, but the hardware requirement may be high.

Work is mostly done with not large datasets, receiving data from several databases and then using SQL to connect with all these different database tables.

Apache Superset is open-source software that can be obtained from a public Docker repository.

Apache Superset can definitely be recommended to other users. This review has been rated eight out of ten.

For business reporting, Apache Superset is used for visualizations that we want to use, and it is also for the operations team. We work in advertising, so there are many logs that come out of our systems, which then run through our ETL process and eventually land that data for reporting in different data warehouses or databases. We use Apache Superset to connect to those different data sources to create different views.

What I find best about Apache Superset is that it's easier to use the drag-and-drop option when selecting the visualizations. One of the major reasons we chose Apache Superset was due to its open source technology. I was working at a startup and we wanted to stay lean on the cost, so that's why we were looking for a visualization tool and Apache Superset was the top contender.

The benefits I have seen from using Apache Superset for the business team are that they have been able to look at the numbers easily, log into the portal, and look at different dashboards, which are at different granularities. The reporting numbers are easy to view, and selecting the filters and then drilling down into the specific KPI metrics is straightforward.

Apache Superset can be improved in visualization, as there are certain visualizations that our team needs. It requires a mix of a time series graph along with the ability of a CSV type of export, which our business consistently requests.

I have been using Apache Superset since 2019 or 2020, making it approximately five or six years.

With the deployment of Apache Superset, keeping it in-house has not been easy. While the recent version might have been easier, we are still using the older version 1.3, and one of the factors has been the deployment itself. Getting the DevOps involved and then testing it in the Dev environment before propagating it to different environments has been one of the challenges because we're not able to keep up with the new releases.

The stability of Apache Superset has been good, with no issues unless we change something in the system configuration. It has been stable overall.

The scalability of Apache Superset has been one of the challenges for us. We deployed it onto a bigger machine because as the users grew, we experienced a delay in data retrieval. Additionally, the users were trying to pull a lot of data, which was another contributing factor.

I have never used the customer support team for Apache Superset. Since we are using the open source version, there isn't an option for customer support.

Negative

Before choosing Apache Superset, I was using an in-house visualization tool.

Apache Superset is self-explanatory, and most of the features do not require a significant learning curve. There is obviously a small learning curve, but if you're familiar with a different visualization tool, this should be a quick read for you.

My advice is that Apache Superset is relatively simple to use, has many connectors, and it's open source. With the features that it provides and it being open source, I think this is the best in the market so far.

I would recommend Apache Superset to other people. On a scale of one to ten, with ten being the highest, I rate Apache Superset an eight or nine.

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.