The high level of customizability is also appealing for us developers in the company.
We can’t emphasize enough how semantic layering contributes to the reusability of queries and the scalability of Superset.Also, it’s possible (and not even difficult) to change Superset SaaS providers or move the system fully in-house. Given its cloud-nativeness, we can choose between standards and abstractions of web servers, metadata engines, and caching strategies.Hence, the heavy-lifting is rather done in the connected database, making it again more scalable than its competitors when it comes to an ad-hoc analysis. Given that it’s a web-based stack application unlike Tableau and Power BI, it was tailored for the consumption layer as the data isn’t stored on the platform.Equipped with interactive querying, autocomplete, scheduling, query history, user-defined parameters (with JINJA templating through dbt CLI and dbt Cloud), etc.Supports long-running queries by persisting query results and dispatching handlers to workers ( Celery).Metadata browsing of tables, columns, indexes, and partitions.A multi-tab environment lets you work on multiple queries at once.A feature-rich SQL IDE written in React.Lets you view the SQL statement for each visualization.User-defined metrics with scalable semantic layering.
Multiple ways of sharing your dashboard/charts (JSON, email, URL).Seamless interaction with a wide range of tooltips.Superset essentially stands on three main layers: In addition, the growing community behind Superset further strengthens the argument that there is an undeniable potential in our sight. To reflect its increasing popularity, Airbnb, Twitter, Netflix, Amex, and many other companies have already started incubating Superset in their workflows, while Dropbox managed to successfully exploit its advantages at an enterprise level. It’s a modern, lightweight, cloud-native, free, and open-source BI web application with an advantageous SQLAlchemy python backend, making it scalable and compatible with almost any database technology speaking SQL. Well, as the name indicates, Superset started in the Apache incubator back in 2016 and quickly became one of its top priority projects coinciding with the recent stable release at the beginning of 2021. A good example for the former would be Preset (founded by the creator of Superset, Max Beauchemin), who offers a hassle-free and fully-managed cloud SaaS service to the platform. Conversely, open-source platforms such as Superset avoid vendor lock-ins since you can both switch between commercial open source (COSS) vendors, and host it yourself on-premise. Chart.io was downsized and shut down by Atlassian). Over the past few years, other commercial tools were acquired by companies such as Tableau by Salesforce, Looker by Google Cloud, or Periscope by Sisense, which can infer the risk of vendor lock-ins forcing customers to migrate and rebuild their assets (e.g. Given the maturity and dynamism of the BI market, consolidations are more frequent than ever. Among these promising technologies, Apache Superset is set to challenge its competitors in many ways, therefore, Hiflylabs closely monitors the roadmap of the project. These were considered as the mainstream solutions for quite a while, but now, emerging open-source alternatives are hungry enough to beat the market with their simplicity and cost-friendly approach. data streamer (Stitch, Fivetran, Airbyte) + centralized cloud storage (AWS, Azure, GCP, Snowflake, Databricks) + transformation tool (dbt) + BI platforms), proprietary tools pop into our head such as Tableau, Power BI, and more recently Looker. When we talk about interactive data visualization, especially in the context of modern data stacks (e.g. How we set up our infrastructure Introduction
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