Advanced Data Wrangling

Data wrangling involves transforming raw data into a structured format valid for analysis. The process typically begins with profiling to identify missing values, outliers, or inconsistencies, followed by data cleaning steps such as normalization, transformation, and deduplication.

Common challenges include aligning different schemas, such as mismatched column names, formats, or data types across systems. Managing time series alignment often involves reconciling data captured at different time intervals, dealing with timezone differences (which is always a pain), or interpolating missing timestamps to maintain continuity. Ensuring consistency across multiple data sources requires careful validation of business rules, consistent definitions, and strategies to resolve discrepancies in values or classifications between systems.