Semi-Structured Data Processing
Semi-structured data processing involves handling data that does not conform to a rigid schema like traditional relational databases but has some organizational properties, such as tags, markers, or hierarchies. It deals with formats like JSON, XML, CSV, and NoSQL databases, enabling flexible data storage and retrieval. This concept is crucial for modern applications that handle diverse and evolving data sources, such as web APIs, logs, and IoT streams.
Developers should learn semi-structured data processing when working with data that lacks a fixed structure, such as in big data analytics, web development, and machine learning pipelines. It is essential for parsing and transforming data from APIs, handling configuration files, and integrating with NoSQL databases like MongoDB or Elasticsearch, where schema flexibility is required.