![]() Other than that, this tool might be a combination of functions and a fancy look. Putting Tableau vs Power BI side by side will allow you to see the visual differences, big time. When it comes to a BI tool that comes up with dynamic and eye-catchy visualization displays then Tableau is one you should consider. However, some other things may make you want to check out some Microsoft Power BI competitors. Those things mentioned above are just a few things you will get from this amazing business analytic tool. Seamless integration with Office 365 along with other business tools.Creating dashboards and reports is easier with the drag-and-drop feature.The Power Query tool gathers and transforms the data and integrates all needed things for analysis. ![]() Incorporates natural language query tools so that users can make questions by using conversational tone.As an on-premise tool, it provides self-service data analysis for enterprise-level companies. In contrast though Zeppelin and Jupyter are certainly much more mature and extensible.Power BI is undoubtedly one of those brands that lead the business analytic field. And like a SQL gui there's builtin support for specifying (encrypted) credentials to your various databases and builtin support for querying them over SSH proxies. With DataStation the database query UI is separate from the programming UI. so you can build reports more easily across your services.Īlso, the notebook interface on its own has felt to me like it doesn't treat querying databases as a first class thing. And eventually my goal is to add high level connections to common APIs developers/managers use like Github, JIRA, Kubernetes controllers, etc. It can be run as a desktop app which makes it easier to get running than web-based notebooks, or as a web app where you can make dashboards and recurring email exports. So it comes built in with setup for every major database. In contrast my target audience is backend developers and hands-on engineering managers who want to build operational and business dashboards and recurring email exports by combining data from multiple different data sources. In both cases the audience for most traditional notebooks are data scientists. Thanks for asking! I hadn't seen it before but it looks pretty similar to Jupyter. Unless you are doing anything funky, all of these tools and tens of others will do the job. With all of this said, building simple reports and dashboards over aggregated data is a fairly commodity task nowadays. Tableau, Looker et al don't seem to be bring much to the table above these, and they are both quite difficult and expensive commercial organisations to deal with. I would look at both Metabase and Superset before the heavier weight commercial options if the choice was purely down to product features. I use a single binary vs (I think) a docker compose setup for Superset.īoth have a cloud offering, Preset.io is the Superset one which we are just using now and having a productive time with. It's also slightly more intimidating for end users too. I often find myself checking log files to see what actual SQL was issued from the front-end. It does however have a few extra rough edges and quirks where your query doesn't render as you would expect, especially with niche databases such as Druid and Clickhouse (via a third party driver). Superset is a great product and we are lucky to have this quality of BI tools for free. Simple to deploy and use, stable, easy for non techies and SQL analyst types. We ( ) use all of Metabase, Superset and heavier alternatives such as Tableau to build fairly advanced dashboards for customers.
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