apache/spark   3.5.3

Apache Spark - A unified analytics engine for large-scale data processing

apache/flink   1.20.0

Apache Flink

combust/mleap   0.23.1

MLeap: Deploy ML Pipelines to Production

bigdatagenomics/adam   0.23.0

ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.

apache/sedona   1.6.1

A cluster computing framework for processing large-scale geospatial data

lucacanali/sparkmeasure   0.24

This is the development repository for sparkMeasure, a tool and library designed for efficient analysis and troubleshooting of Apache Spark jobs. It focuses on easing the collection and examination of Spark metrics, making it a practical choice for both developers and data engineers.

scalapy/scalapy   0.5.2

Use the world of Python from the comfort of Scala!

g-research/spark-extension   1.0.0

A library that provides useful extensions to Apache Spark and PySpark.

locationtech-labs/geopyspark   0.3.0

GeoTrellis for PySpark

shadaj/scalapy-tensorflow   0.1.0

Static facades for using TensorFlow in ScalaPy

isarn/isarn-sketches-spark   0.6.0-sp3.2

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

fqaiser94/mse   0.2.4

Make Structs Easy (MSE)

scalapy/python-native-libs   0.2.4

Helpers for setting up an embedded Python interpreter

ozancicek/artan   0.5.1

Online latent state estimation with Spark

diana-hep/spark-root   0.1.16

Apache Spark Data Source for ROOT File Format

timvw/adobe-analytics-datafeed-datasource   0.1.0

Apache Spark data source for Adobe Analytics Data Feed

ctongfei/kingscross   0.1.0

Portal from the magical world of Scala to dangerous external interpreters outside JVM

liquidsvm/liquidsvm   0.6.0

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.

catboost/catboost   1.2.7

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.