Apache Superset is a Data Visualization and Data Exploration Platform
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Apache Superset is a Data Visualization and Data Exploration Platform
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Translated🔥 An open-source BI tool anyone can use, a powerful data visualization tool. An open-source BI tool alternative to Tableau.
A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Koalas: pandas API on Apache Spark
App to easily query, script, and visualize data from every database, file, and API.
Elyra extends JupyterLab with an AI centric approach.
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
A software library of stochastic streaming algorithms, a.k.a. sketches.
Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams.
Mirror of Apache Toree (Incubating)
Haskell on Apache Spark.
A pure Python implementation of Apache Spark's RDD and DStream interfaces.
JQuick-Pdf is an enterprise-grade Java PDF generation framework. It provides HTML-like declarative syntax and supports more than 30 kinds of graphs, greatly simplifying complex PDF report creation while retaining full native pdfbox(since version 4.0.0) APIs.
Visual Integration Designer for Apache Camel
RHive is an R extension facilitating distributed computing via Apache Hive.
Tango is a command-line tool for analyzing access logs 💃
A fast & lightweight approach to logging in R based on the widely-emulated Apache Log4j project.
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Apache Druid client for Elixir
ETL scripts for EOS.
Modern, interactive charts for Jupyter notebooks with Apache ECharts