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BigData / Apache Iceberg Interview questions

What are the key features of Apache Iceberg?

Iceberg combines a specific set of capabilities aimed at bringing warehouse-like reliability to data lake tables, several of which are difficult or impossible to achieve with older, Hive-style table management.

FeatureWhat it Provides
ACID transactionsAtomic, isolated commits via snapshot-based metadata
Schema evolutionAdd, drop, rename, or reorder columns without rewriting data
Hidden partitioningAutomatic partition pruning without users writing partition-aware filters
Partition evolutionChange partitioning strategy going forward, without rewriting existing data
Time travelQuery the table as of a past snapshot or timestamp
Multi-engine supportSpark, Flink, Trino, Presto, Hive, and others read/write the same tables

Together these features are what let Iceberg tables serve as a shared, reliable foundation for a lakehouse architecture — multiple teams and tools operating on the same underlying data without the correctness and operational headaches that made large-scale, evolving Hive tables notoriously painful to manage over time.

What does partition evolution let you do?
What does hidden partitioning eliminate the need for?

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What is Apache Iceberg? What is the purpose of Apache Iceberg? What is a table format, and how does it differ from a file format? What are the key features of Apache Iceberg? What is the architecture of an Iceberg table? What is a snapshot in Apache Iceberg? What is a manifest file? What is a manifest list? What is the table metadata file? What is an Iceberg catalog? What is hidden partitioning? What are partition transforms in Iceberg? What is schema evolution in Iceberg? What is time travel in Apache Iceberg? Which query engines support Apache Iceberg? What file formats does Iceberg use to store data? How do you create an Iceberg table? What is the difference between Iceberg and a Hive table? What are field IDs in Iceberg, and why do they matter? What is ACID compliance in the context of Apache Iceberg? What is the difference between Apache Iceberg and Delta Lake? What is the difference between Apache Iceberg and Apache Hudi? Explain how hidden partitioning differs from Hive-style partitioning? What is a lakehouse, and how does Iceberg enable it? What is partition evolution, and how does it work internally? What is the difference between copy-on-write and merge-on-read in Iceberg? What are positional deletes versus equality deletes? Explain the internal working of Iceberg's snapshot isolation mechanism? What is the REST catalog, and why has it become important? What is the difference between a Hive catalog and a REST catalog? Explain the execution flow of a query against an Iceberg table? How does Iceberg achieve schema evolution without rewriting data? Explain the internal working of manifest-level partition pruning? What is compaction in Iceberg, and why is it needed? How do you perform time travel queries in Iceberg? What is the difference between a snapshot rollback and time travel? Explain how Iceberg handles concurrent writes? What is the role of sequence numbers in Iceberg snapshots? Explain the lifecycle of a write operation (commit) in Apache Iceberg? What are branches and tags in Apache Iceberg? How does Iceberg support upserts via MERGE INTO? What is the small file problem, and how does Iceberg address it? Explain the internal working of column-level statistics in manifest files? What is the difference between Iceberg V1, V2, and V3 table specs? How do you migrate an existing Hive table to Iceberg? What are deletion vectors, and how do they improve on positional delete files? Explain how Iceberg integrates with Apache Spark for reading and writing? What is metadata table querying in Iceberg? How do you troubleshoot slow query planning on a large Iceberg table? Explain the execution flow of building a streaming lakehouse pipeline with Iceberg and Flink?
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