Semi-Structured Data Analysis
Semi-structured data analysis is the process of extracting insights from 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 involves handling formats like JSON, XML, CSV, and log files, which are common in web applications, IoT devices, and big data environments. Techniques include parsing, transformation, and querying to make the data usable for analytics, machine learning, or reporting.
Developers should learn semi-structured data analysis to work with modern data sources like APIs, sensor data, and web logs, where flexibility in data structure is essential. It is crucial for roles in data engineering, backend development, and data science, enabling integration of diverse data streams in applications such as real-time analytics, ETL pipelines, and data warehousing. Mastery improves efficiency in handling unstructured data in NoSQL databases and big data platforms.