**Why This Pattern:** Multi-file concat + transform + filter is the core ETL pattern. At JP Morgan, regulatory data often comes as daily files—concat, validate, filter, load.
**Scalability:** pd.concat([df1,df2,df3]) loads all into memory. For 3 large files: read each with chunksize, concat chunks, process incrementally. Or use Dask for out-of-core....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like JP Morgan. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.