Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
- redash
- python
- visualization
- analytics
Repository Index
47,133 repositories indexed
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The interactive graphing library for Python :sparkles:
the portable Python dataframe library
Koalas: pandas API on Apache Spark
Lightweight and extensible compatibility layer between dataframe libraries!
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Jupyter magics and kernels for working with remote Spark clusters
Hopsworks - Data-Intensive AI platform with a Feature Store
- feature-store
- aws
- azure
- data-science
📊 Data visualization library for React. Maintained by @goodcodeus.
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
- apache-spark
- big-data
- connected-components
- dataframe
ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.
Project SnappyData - memory optimized analytics database, based on Apache Spark™ and Apache Geode™. Stream, Transact, Analyze, Predict in one cluster
R interface for Apache Spark
[DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Benchmarks for Low Latency (Streaming) solutions including Apache Storm, Apache Spark, Apache Flink, ...
- benchmarks
- low-latency
- streaming
R frontend for Spark
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
- pandas
- pandas-summary
- dataframes
- data-science
Haskell on Apache Spark.
- apache-spark
- spark
- haskell
- analytics
A pure Python implementation of Apache Spark's RDD and DStream interfaces.
- data-science
- data-processing
- python
- apache-spark
Docker container for Kafka - Spark Streaming - Cassandra
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.
GG.Net Data Visualization
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.
- svm
- regression
- classification
- machine-learning