This article presents a practical guide for data engineers who want to manage Apache Iceberg tables with PyIceberg without needing large JVM clusters
PyIceberg is a Python library designed to create flexible schemas and perform CRUD operations easily, as well as offering efficient queries supported by DuckDB, ideal for small and medium-sized data environments
The initial setup does not require heavy infrastructure; you just need to install PyIceberg and PyArrow, define the catalog and mount point to start creating and versioning tables with advanced schemas without complications
To create schemas, a schema object is defined indicating fields, types, and metadata, then the create table method is used to establish the physical structure in the data lakehouse
CRUD operations allow inserting records, updating partitions, and deleting obsolete versions, maintaining history and traceability in each change
Integration with DuckDB enables ad hoc SQL queries directly on Iceberg tables, taking advantage of vectorization optimizations and in-memory processing
This simplified workflow accelerates open data lakehouse projects and is complemented by tools such as PyArrow, DuckDB, and visualization libraries for exploration and analysis
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