Principle:ClickHouse ClickHouse Data Lake Testing
| Knowledge Sources | |
|---|---|
| Domains | Testing, Data_Lakes |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Utilities for generating test data in data lake formats (Iceberg, Delta, Hudi) for integration testing.
Description
Testing data lake integrations requires data in specific formats. Conversion utilities use frameworks like PySpark to convert standard formats (Parquet) into data lake formats with correct metadata and file structures. This enables automated testing of data lake readers without manually creating test data or depending on external data sources.
Usage
Use for integration testing of data lake connectors, validating data lake reading logic, or generating benchmark datasets.
Theoretical Basis
Data Lake Formats: Iceberg, Delta, Hudi add transaction logs and metadata over Parquet/ORC for ACID properties.
PySpark: Industry-standard framework for working with data lakes, providing APIs for all major formats.
Test Isolation: Generating test data ensures tests don't depend on external systems or specific dataset versions.
Format Conversion: Converting from simple Parquet enables testing format-specific features (time travel, schema evolution).