There are 84 repositories under pyspark topic.
the portable Python dataframe library
State of the Art Natural Language Processing
Apache Linkis builds a computation middleware layer to facilitate connection, governance and orchestration between the upper applications and the underlying data engines.
Implementing best practices for PySpark ETL jobs and applications.
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.
A curated list of awesome Apache Spark packages and resources.
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.
:truck: Agile Data Preparation Workflows madeย easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Jupyter magics and kernels for working with remote Spark clusters
Lightweight and extensible compatibility layer between dataframe libraries!
Hopsworks - Data-Intensive AI platform with a Feature Store
PySpark-Tutorial provides basic algorithms using PySpark
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
MapReduce, Spark, Java, and Scala for Data Algorithms Book
LakeSail's computation framework with a mission to unify batch processing, stream processing, and compute-intensive AI workloads.
Sparkling Water provides H2O functionality inside Spark cluster
Scriptis is for interactive data analysis with script development(SQL, Pyspark, HiveQL), task submission(Spark, Hive), UDF, function, resource management and intelligent diagnosis.
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.
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
A comprehensive Spark guide collated from multiple sources that can be referred to learn more about Spark or as an interview refresher.
pyspark methods to enhance developer productivity ๐ฃ ๐ฏ ๐
Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.
๐ Quick reference guide to common patterns & functions in PySpark.
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Learn Apache Spark in Scala, Python (PySpark) and R (SparkR) by building your own cluster with a JupyterLab interface on Docker. :zap:
PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster
This is a repo documenting the best practices in PySpark.
Process Common Crawl data with Python and Spark
Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines
t-Digest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark
A boilerplate for writing PySpark Jobs
Build reliable AI and agentic applications with DataFrames