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Tools in this category display analytics results in interactive way, so they can facilitate the understanding of difficult concepts and support decision makers for researchers and data scientists. There are many data visualization packages in various levels in R or Python e.g. Matplotlib, Plotly, Seaborn, ggplot, Bokeh, and so on.
In recent years, web-based notebooks/applications have been increasing in popularity. They are integrated with data analytic environments to create and share documents that contain data-driven live code, equations, visualisations and narrative text. The most well-known are Jupyter notebook (formerly iPython notebook) and Zeppelin.
Jupyter notebook [Jupyter] is the open-source application supporting e.g. creation and sharing documents ("notebooks"), code, source equations, visualisation and text descriptions for data transformation, numerical simulations, statistical modelling, data visualisation and ML.
Zeppelin is an interactive notebook designed for the processing, analysis and visualization of large data sets [Zeppelin], providing native support for Apache Spark distributed computing. Zeppelin allows to extend their functionality through various interpreters e.g. Spark, SparkSQL, Scala, Python, shell from Apache Spark analytics.
The next popular tools belong to open source data analytics, reporting and integration platforms such as Kibana, Grafana and Tableau.
Kibana is the data visualisation front end for the Elastic Stack, complementing the rest of the stack that includes Beats, Logstash and Elasticsearch [Kibana]. With the version 5.x release of the Elastic Stack, Kibana now includes Timelion for interactive time series charts.
Grafana is the chosen DevOps tool for many real time monitoring dashboards of time series metrics [Grafana]. It has powerful visualisations and supports multiple backend data sources including InfluxDB, Graphite, Elasticsearch and many others which can be added via plugins.
Tableau is a universal analytics tool, which can extract data from different small data sources like csv, excel, and SQL as well as from enterprise resources or connect Big Data frameworks and cloud based sources [Tableau].
In conclusion, there are also rich options of interactive tools, which are designed for many different purposes.
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