Which tools does this skill support?

It provides patterns and code generation for common data engineering tools including Apache Spark, Airflow, and various ETL frameworks.

Does it provide production-ready code?

Yes, it generates production-grade configurations and code snippets that adhere to industry standards for data engineering and workflow orchestration.

How is the Data Partitioner skill triggered?

The skill activates automatically when you mention 'data partitioner' or ask for help with data pipeline patterns and best practices.

What is the Data Partitioner skill for?

It automates the design and implementation of data partitioning strategies to improve the performance and scalability of data pipelines.

Data Partitioner

Name: Data Partitioner
Author: jeremylongshore

byjeremylongshore

•

1,030

Database Management

Optimizes data pipeline performance by automating the creation and management of efficient data partitioning strategies.

About

The Data Partitioner skill provides automated guidance and implementation support for data partitioning tasks within complex data engineering workflows. It helps users design and implement scalable partitioning patterns for technologies like Spark and Airflow, ensuring that ETL processes and streaming data pipelines are optimized for high performance, cost-efficiency, and industry-standard best practices.

Key Features

Validates pipeline outputs against common data engineering standards
Automates partition strategy design for large-scale datasets
Generates production-ready code for Spark, Airflow, and ETL workflows
Implements industry-standard patterns for data sharding and distribution
Provides step-by-step guidance for batch and streaming data processing
1,030 GitHub stars

Use Cases

Setting up time-based partitioning for log files in a Spark pipeline
Refactoring monolithic database tables into optimized partitions for better query performance
Orchestrating complex data transformation steps within Airflow or other workflow tools

About

Key Features

Validates pipeline outputs against common data engineering standards
Automates partition strategy design for large-scale datasets
Generates production-ready code for Spark, Airflow, and ETL workflows
Implements industry-standard patterns for data sharding and distribution
Provides step-by-step guidance for batch and streaming data processing
1,030 GitHub stars

Use Cases

Setting up time-based partitioning for log files in a Spark pipeline
Refactoring monolithic database tables into optimized partitions for better query performance
Orchestrating complex data transformation steps within Airflow or other workflow tools