BigData / Apache Parquet Interview Questions
What are the advantages of Parquet over CSV?
CSV is simple but untyped and inefficient at scale. Parquet improves on it in every dimension that matters for analytics:
| Aspect | CSV | Parquet |
|---|---|---|
| Storage layout | Row-based | Columnar |
| Compression | Low (mixed types per row) | High (homogeneous per column) |
| Schema | None (inferred) | Embedded in file |
| Partial reads | Must scan full row | Read only needed columns |
| Predicate pushdown | No | Yes (min/max statistics) |
| Schema evolution | Manual | Built-in (add columns) |
| Ecosystem | Universal | Hadoop, Spark, cloud lakes |
For OLAP-style queries that aggregate a few columns across millions of rows, Parquet is typically 10–100× faster to query and 3–10× smaller than an equivalent CSV.
Why does Parquet compress data more efficiently than CSV?
Which CSV limitation does Parquet's embedded schema solve?
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