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BigData / Apache Parquet Interview Questions

What is Apache Parquet and why is it used?

Apache Parquet is an open-source, columnar storage file format designed for the Hadoop ecosystem and modern big-data platforms. Unlike row-based formats (CSV, JSON), Parquet stores each column's data contiguously on disk. This layout allows query engines to read only the columns they need, dramatically reducing I/O.

Key reasons Parquet is widely used:

  • Columnar layout — queries touch only the relevant columns, not full rows.
  • Efficient compression — columns hold homogeneous types, so codecs like Snappy and GZIP achieve higher ratios.
  • Predicate pushdown — embedded min/max statistics let engines skip entire row groups that cannot satisfy a WHERE clause.
  • Schema embedding — the schema is stored inside the file, eliminating external schema management.
  • Broad ecosystem support — Spark, Hive, Presto, Flink, DuckDB, Pandas, AWS Athena, and Snowflake all read/write Parquet natively.
Which storage layout does Apache Parquet use?
What enables Parquet to skip irrelevant row groups during a query?

More Related questions...

What is Apache Parquet and why is it used? What are the advantages of Parquet over CSV? How are Parquet files structured? (Row Groups, Column Chunks, Pages)? What is Schema Evolution in Parquet? What is Column Pruning and Projection Pushdown in Parquet? When would you choose Avro over Parquet? How does Parquet handle compression and encoding? What is the Vectorized Reader in Spark and how does it improve Parquet performance? How do you handle schema mismatches when merging multiple Parquet files? If a Spark query on Parquet is slow, what optimisation steps would you take? How do you load Parquet files into Snowflake? What are the supported data types in Parquet? How do you read and write Parquet files in PySpark? How do you read and write Parquet files in Python with PyArrow? What is partitioning in Parquet and how does it improve query performance? What are Bloom Filters in Parquet and when should you use them? What is the difference between Parquet, ORC, and Avro? What is Z-ordering (Z-order clustering) and how does it help Parquet queries? What is Apache Iceberg and how does it use Parquet? How does DuckDB query Parquet files and what makes it fast? What is the Parquet file footer and why does the reader fetch it first? How does Parquet support nested data (structs, lists, maps)? What is small file problem in Parquet-based data lakes and how do you solve it? What is the difference between repartition and coalesce when writing Parquet files? How does AWS Athena query Parquet files in S3? What is predicate pushdown in Parquet and how does it work end-to-end? What are best practices for writing Parquet files in production? How does Google BigQuery use Parquet-style columnar storage internally? What is Delta Lake and how does it extend Parquet for ACID transactions? How do you perform upserts (MERGE INTO) on Parquet-based tables in Delta Lake?
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