BigData / Apache Parquet Interview Questions
When would you choose Avro over Parquet?
Parquet and Avro serve different access patterns. The table below summarises when to prefer each:
| Criterion | Choose Parquet | Choose Avro |
|---|---|---|
| Query pattern | OLAP — aggregate few columns over many rows | Row-based access — read/write entire records |
| Streaming | Batch / micro-batch | Streaming (Kafka, Flink event records) |
| Write frequency | Write-once, read-many | Frequent writes / appends |
| Schema evolution | Column adds; limited | Full backward/forward compatibility |
| Compression | Higher for analytics | Moderate; better for write throughput |
A common pattern: ingest events as Avro in Kafka, then compact and convert to Parquet in a data lake for batch analytics.
For a Kafka streaming pipeline where every field of each event is consumed, which format is the better fit?
Which workload is Parquet best suited for?
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