JSON Data Engineering in Python: Handling Semi-Structured Data

In modern data pipelines, JSON is the standard format for exchanging semi-structured data across APIs, cloud services, and databases. To build robust data systems, you must know how to efficiently parse, manipulate, and validate this data using Python. This text-based course takes you from JSON fundamentals to advanced manipulation techniques used by data engineers. You will learn how to handle nested structures, validate schemas, and prepare semi-structured data for analytical databases, all through clear written explanations and practical code examples. What you'll learn: - Understand JSON syntax, data types, and how they map directly to Python dictionaries and lists. - Parse and serialize complex, deeply nested JSON payloads using Python's built-in libraries. - Validate JSON schemas using modern Python tools like Pydantic and type hints to ensure data quality. - Transform raw JSON data into clean, structured formats ready for relational databases or data lakes. - Handle common data engineering challenges, including large file parsing and error handling during ingestion. You will start with core JSON concepts and basic Python parsing before advancing to complex nested transformations, schema validation, and pipeline integration patterns. This course is designed for aspiring data engineers, data analysts, and Python beginners who want to build a solid foundation in handling semi-structured data. No prior experience with JSON is required, though a basic understanding of Python variables and loops is helpful. Start learning today and unlock the power of semi-structured data in your Python workflows.