How to query Iceberg locally using Spark, PyIceberg, or duckdb

The text discusses the use of Apache Iceberg, a distributed data storage format, emphasizing its advantages over traditional file-based systems when working with distributed networks of readers and writers, especially in cloud environments. It details how to set up and use Iceberg clients locally, focusing on Spark, PyIceberg, and duckdb, each with its own strengths and limitations. Spark, often run locally with PySpark, is highlighted for its comprehensive support for Iceberg and its ability to perform SQL queries and data manipulation. PyIceberg, a Python implementation, lacks direct SQL support but offers a simpler setup without Java dependencies, making it suitable for managing Iceberg tables. Duckdb, known for its data analysis capabilities, currently has limited Iceberg support but can be effectively combined with PyIceberg for querying. The text underscores the rapidly evolving nature of the Iceberg ecosystem, noting the ongoing improvements by platforms like Databricks and Snowflake, and suggests that users report any issues to facilitate further development.