Spark based ETL
Project description
Introduction
This Spark package is designed to process data from various sources, perform transformations, and write the results to different sinks. It follows the pipeline design pattern to provide a flexible and modular approach to data processing.
Install
The package is published for use on pypi. Install directly from pypi
pip install pysparkify
Design
The package is structured as follows:
Source, Sink and Transformer Abstraction
The package defines abstract classes Source
, Sink
and Transformer
to represent data sources, sinks and transformers. It also provides concrete classes, including CsvSource
, CsvSink
and SQLTransformer
, which inherit from the abstract classes. This design allows you to add new source and sink types with ease.
Configuration via recipe.yml
The package reads its configuration from a recipe.yml
file. This YAML file specifies the source, sink, and transformation configurations. It allows you to define different data sources, sinks, and transformation queries.
Transformation Queries
Transformations are performed by SQLTransformer
using Spark SQL queries defined in the configuration. These queries are executed on the data from the source before writing it to the sink. New transformers can be implemented by extending Transformer
abstract class that can take spark dataframes from sources to process and send dataframes to sinks to save.
Pipeline Execution
The package reads data from the specified source, performs transformations based on the configured SQL queries, and then writes the results to the specified sink. You can configure multiple sources and sinks within the same package.
Setup
The project is built using python-3.12.0, spark-3.5.0 (and other dependencies in requirements.txt).
Deployment
... Environment specific documentation
Project details
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