Spark Dataframe Etl Pipeline

---
name: "Apache Spark DataFrame ETL Pipeline"
slug: "spark-dataframe-etl-pipeline"
description: "Automates PySpark DataFrame transformations including schema inference, partition pruning, and Delta Lake merge operations. Integrates with AWS Glue Data Catalog and Apache Iceberg table formats for lakehouse architectures."
github_stars: 43117
verification: "security_reviewed"
source: "https://github.com/apache/spark"
category: "Data Extraction & Transformation"
framework: "OpenClaw"
tool_ecosystem:
  github_repo: "apache/spark"
  github_stars: 43117
---

# Apache Spark DataFrame ETL Pipeline

Automates PySpark DataFrame transformations including schema inference, partition pruning, and Delta Lake merge operations. Integrates with AWS Glue Data Catalog and Apache Iceberg table formats for lakehouse architectures.

## Installation

Requirements and caveats from upstream:
- high-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that
- ## Interactive Python Shell
- Alternatively, if you prefer Python, you can use the Python shell:

Basic usage or getting-started notes:
- To build Spark and its example programs, run:
- And run the following command, which should also return 1,000,000,000:
- ## Example Programs

- Source: https://github.com/apache/spark
- Extracted from upstream docs: https://raw.githubusercontent.com/apache/spark/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/spark-dataframe-etl-pipeline/)