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BigData / Apache Parquet Interview Questions

What is Apache Iceberg and how does it use Parquet?

Apache Iceberg is a high-performance open table format for huge analytic datasets, designed to replace traditional Hive table management. Iceberg stores actual data in Parquet (or ORC/Avro) files and adds a metadata layer on top.

Iceberg adds to raw Parquet:

  • ACID transactions — snapshot isolation for concurrent reads and writes.
  • Hidden partitioning — partition transforms (bucket, truncate, year/month/day) applied transparently without user-specified partition paths.
  • Full schema evolution — rename, reorder, drop columns tracked via field IDs.
  • Time travel — query any historical snapshot: SELECT * FROM events VERSION AS OF 12345;
  • Metadata statistics — manifest files track per-file min/max/null counts for fast planning.

Parquet is the default data format in Iceberg deployments (Spark, Flink, Trino, Hive). Iceberg handles all the metadata overhead so Parquet files can remain immutable and easily compactable.

What does Apache Iceberg add on top of raw Parquet files?
Which Iceberg SQL feature allows querying a table as it existed at a specific past snapshot?

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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