|Apache Superset Reaches Top Level Project Status|
|Written by Kay Ewbank|
|Thursday, 28 January 2021|
The Apache Software Foundation has announced that Superset has reached top level project status, having been in the Apache Incubator range since 2017. Superset is an open-source data exploration and visualization application that was originally developed by Airbnb.
Superset has been designed to be accessible to all users, with a choice of ways to explore and show data. Superset is written in Python and uses Flask as its web framework library. It supports multiple dashboard, graph and chart types with filters, and can be used with any data source that support SQL Alchemy and has a Python DB-API driver.
Data selection is based on SQL queries, and Superset comes with a SQL editor/IDE and metadata browser, along with a workflow creator to put together visualizations from the result set. The IDE lets users select a database, schema and table, run an interactive query, preview the data and save the query history. A semantic layer lets users define fields and metrics. Python modules are also available within SQL, via Jinja.
An extensible, high granularity security model can be used to create detailed rules on who can access which product features and datasets.There's a lightweight semantic layer that can be used to control how data sources are exposed to the user by defining which fields should show up in which drop-down and which aggregation and function metrics are made available to the user. Superset has close integration with Druid to ensure good performance while working with large, realtime datasets, and configurable caching means dashboards load quickly.
Superset was created to be cloud-native and highly available, and to scale to large, distributed environments. It also works well with containers. Developers can add custom visualization plugins, and there's an API for developers who want to customize it further.
Superset can be used with services such as NewRelic, StatsD and DataDog, and has the ability to run analytic workloads against most popular database technologies.
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|Last Updated ( Thursday, 28 January 2021 )|