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Google·Software Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Google data engineer screen, just one technical question about file formats. Pretty short session, felt more like a warm-up than a full round.

Questions Asked (1)

Q1

What are the key differences between Parquet and Avro as data storage formats?

Technical Trade-offsSystem DesignData Modeling
Author's notes

I knew the basics but fumbled a bit on when you'd actually pick one over the other in practice.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the fundamental architectural difference — columnar vs. row-based storage — and then derive the practical trade-offs from that foundation. Use concrete use cases (e.g., analytics queries vs. streaming pipelines) to ground your comparison, showing you understand not just what the formats are, but when to choose each one.

Pro tip: Mention that at scale, the choice between Parquet and Avro often comes down to read vs. write patterns, and that many production systems at companies like Google use both together — Avro for ingestion/streaming (Kafka, Dataflow) and Parquet for analytical storage (BigQuery external tables, GCS) — demonstrating you think in terms of data pipelines, not isolated tools.

1. Establish the Core Architectural Difference

Open by clearly stating that Parquet is a columnar storage format while Avro is a row-based format. Explain that this single distinction drives nearly all other trade-offs between the two.

2. Compare Read/Write Performance Characteristics

Discuss how Parquet's columnar layout enables efficient analytical reads by scanning only relevant columns, while Avro's row layout is optimized for fast sequential writes and full-record reads. Tie this to I/O efficiency and compression ratios.

3. Address Schema Evolution and Serialization

Highlight that Avro has a robust, built-in schema evolution model with a self-describing schema embedded in the file, making it ideal for streaming and RPC use cases. Parquet also supports schema evolution but is more complex to manage for frequent schema changes.

4. Map Each Format to Ideal Use Cases

Concretely state that Avro excels in data ingestion pipelines, Kafka messaging, and Hadoop MapReduce, while Parquet shines in OLAP workloads, data warehousing, and tools like Spark, Hive, and BigQuery. This shows practical engineering judgment.

5. Discuss Ecosystem and Tooling Support

Briefly mention compression codec support (Snappy, GZIP, Zstandard), interoperability with the Apache ecosystem, and how both formats are supported in Google Cloud (Dataflow, BigQuery, GCS), tying the answer back to the Google context.

Key Points to Mention

  • Columnar (Parquet) vs. row-oriented (Avro) storage layout and its impact on query performance and compression
  • Avro's superior schema evolution capabilities using reader/writer schema resolution, making it ideal for long-lived streaming pipelines
  • Parquet's predicate pushdown and column pruning enabling significant I/O reduction in analytical queries
  • Avro's self-describing nature and compact binary serialization making it well-suited for Kafka, Dataflow, and inter-service communication
  • Compression efficiency: Parquet typically achieves higher compression ratios on columnar data due to similar data types being stored together
  • Real-world hybrid usage: Avro for write-heavy ingestion layers and Parquet for read-optimized analytical storage layers in the same pipeline

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.