pyspark

Simple and Distributed Machine Learning

State of the Art Natural Language Processing

Linkis helps easily connect to various back-end computation/storage engines(Spark, Python, TiDB...), exposes various interfaces(REST, JDBC, Java ...), with multi-tenancy, high performance, and resource control.

Expressive analytics in Python at any scale.

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

A curated list of awesome Apache Spark packages and resources.

🚚 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

MapReduce, Spark, Java, and Scala for Data Algorithms Book

Example project implementing best practices for PySpark ETL jobs and applications.

Feathr – An Enterprise-Grade, High Performance Feature Store

Hopsworks - Data-Intensive AI platform with a Feature Store

Scriptis is for interactive data analysis with script development(SQL, Pyspark, HiveQL), task submission(Spark, Hive), UDF, function, resource management and intelligent diagnosis.

Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times

80+ DevOps & Data CLI Tools - AWS, GCP, GCF Python Cloud Functions, Log Anonymizer, Spark, Hadoop, HBase, Hive, Impala, Linux, Docker, Spark Data Converters & Validators (Avro/Parquet/JSON/CSV/INI/XML/YAML), Travis CI, AWS CloudFormation, Elasticsearch, Solr etc.

A comprehensive Spark guide collated from multiple sources that can be referred to learn more about Spark or as an interview refresher.

pyspark🍒🥭 is delicious，just eat it!😋😋

This is a repo documenting the best practices in PySpark.

A boilerplate for writing PySpark Jobs