Iceberg & Open Tables interview questions

1. A.Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
2. B.An open table format adding ACID transactions, schema and partition evolution, and time travel over data lake files, decoupled from any single engine.
3. C.Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
4. D.Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.

Answer + AI explanation with Pro

1. A.copy-on-write vs merge-on-read
2. B.Apache Iceberg
3. C.Iceberg REST catalog
4. D.schema evolution

Answer + AI explanation with Pro

1. A.Apache Iceberg — A metadata file listing data files and their statistics for a snapshot, enabling fast file pruning without listing object storage.
2. B.Apache Iceberg — Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.
3. C.Apache Iceberg — Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
4. D.Apache Iceberg — An open table format adding ACID transactions, schema and partition evolution, and time travel over data lake files, decoupled from any single engine.

Answer + AI explanation with Pro

1. A.Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
2. B.A metadata file listing data files and their statistics for a snapshot, enabling fast file pruning without listing object storage.
3. C.A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.
4. D.Iceberg's ability to change a table's partitioning scheme over time without rewriting historical data, applying the new spec only to new data.

Answer + AI explanation with Pro

1. A.VACUUM
2. B.Iceberg manifest
3. C.partition evolution
4. D.Z-ordering

Answer + AI explanation with Pro

1. A.Iceberg manifest — Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.
2. B.Iceberg manifest — A metadata file listing data files and their statistics for a snapshot, enabling fast file pruning without listing object storage.
3. C.Iceberg manifest — An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
4. D.Iceberg manifest — A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.

Answer + AI explanation with Pro

1. A.Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.
2. B.Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
3. C.An open table format adding ACID transactions, schema and partition evolution, and time travel over data lake files, decoupled from any single engine.
4. D.A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.

Answer + AI explanation with Pro

1. A.Polaris Catalog
2. B.partition evolution
3. C.time travel
4. D.hidden partitioning

Answer + AI explanation with Pro

1. A.hidden partitioning — A maintenance command that removes data files no longer referenced by retained snapshots, reclaiming storage in Delta and similar formats.
2. B.hidden partitioning — Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.
3. C.hidden partitioning — Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
4. D.hidden partitioning — Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.

Answer + AI explanation with Pro

1. A.A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.
2. B.Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
3. C.The top-level Iceberg JSON file pointing to the current snapshot, schema, and partition spec; atomically swapped to commit a new table version.
4. D.Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.

Answer + AI explanation with Pro

1. A.Iceberg REST catalog
2. B.hidden partitioning
3. C.copy-on-write vs merge-on-read
4. D.Apache Iceberg

Answer + AI explanation with Pro

1. A.Iceberg REST catalog — Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.
2. B.Iceberg REST catalog — An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
3. C.Iceberg REST catalog — A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.
4. D.Iceberg REST catalog — Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.

Answer + AI explanation with Pro

1. A.Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
2. B.A maintenance command that removes data files no longer referenced by retained snapshots, reclaiming storage in Delta and similar formats.
3. C.Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.
4. D.Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.

Answer + AI explanation with Pro

1. A.Iceberg REST catalog
2. B.Iceberg manifest
3. C.time travel
4. D.Delta Lake

Answer + AI explanation with Pro

1. A.Delta Lake — Iceberg's ability to change a table's partitioning scheme over time without rewriting historical data, applying the new spec only to new data.
2. B.Delta Lake — Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.
3. C.Delta Lake — A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.
4. D.Delta Lake — Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.

Answer + AI explanation with Pro

1. A.An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
2. B.An open table format adding ACID transactions, schema and partition evolution, and time travel over data lake files, decoupled from any single engine.
3. C.A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.
4. D.Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.

Answer + AI explanation with Pro

1. A.Polaris Catalog
2. B.partition evolution
3. C.Iceberg manifest
4. D.Apache Hudi

Answer + AI explanation with Pro

1. A.Apache Hudi — Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
2. B.Apache Hudi — Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
3. C.Apache Hudi — Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.
4. D.Apache Hudi — An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.

Answer + AI explanation with Pro

1. A.An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
2. B.A metadata file listing data files and their statistics for a snapshot, enabling fast file pruning without listing object storage.
3. C.The ability of open table formats to add, drop, rename, or reorder columns safely without rewriting existing data files.
4. D.Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.

Answer + AI explanation with Pro

1. A.schema evolution
2. B.metadata file
3. C.copy-on-write vs merge-on-read
4. D.Apache Iceberg

Answer + AI explanation with Pro

1. A.copy-on-write vs merge-on-read — Iceberg's ability to change a table's partitioning scheme over time without rewriting historical data, applying the new spec only to new data.
2. B.copy-on-write vs merge-on-read — Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
3. C.copy-on-write vs merge-on-read — Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.
4. D.copy-on-write vs merge-on-read — Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.

Answer + AI explanation with Pro

1. A.An open table format adding ACID transactions, schema and partition evolution, and time travel over data lake files, decoupled from any single engine.
2. B.Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
3. C.A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.
4. D.A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.

Answer + AI explanation with Pro

1. A.Iceberg REST catalog
2. B.copy-on-write vs merge-on-read
3. C.schema evolution
4. D.Polaris Catalog

Answer + AI explanation with Pro

1. A.Polaris Catalog — Iceberg's ability to change a table's partitioning scheme over time without rewriting historical data, applying the new spec only to new data.
2. B.Polaris Catalog — Iceberg feature that derives partition values from a column via a transform so queries prune partitions without users writing partition predicates.
3. C.Polaris Catalog — Snowflake's open-source, Iceberg-REST-compatible catalog (Apache Polaris) for cross-engine governance of Iceberg tables.
4. D.Polaris Catalog — A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.

Answer + AI explanation with Pro

1. A.A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.
2. B.Databricks' open table format providing ACID transactions and time travel via a JSON and Parquet transaction log over data files.
3. C.The ability of open table formats to add, drop, rename, or reorder columns safely without rewriting existing data files.
4. D.Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.

Answer + AI explanation with Pro

1. A.time travel
2. B.schema evolution
3. C.Z-ordering
4. D.Apache Iceberg

Answer + AI explanation with Pro

1. A.schema evolution — A maintenance command that removes data files no longer referenced by retained snapshots, reclaiming storage in Delta and similar formats.
2. B.schema evolution — Iceberg's ability to change a table's partitioning scheme over time without rewriting historical data, applying the new spec only to new data.
3. C.schema evolution — A multi-dimensional clustering technique that co-locates related values across several columns to improve data skipping on multi-column filters.
4. D.schema evolution — The ability of open table formats to add, drop, rename, or reorder columns safely without rewriting existing data files.

Answer + AI explanation with Pro

1. A.An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
2. B.The ability of open table formats to add, drop, rename, or reorder columns safely without rewriting existing data files.
3. C.The top-level Iceberg JSON file pointing to the current snapshot, schema, and partition spec; atomically swapped to commit a new table version.
4. D.Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.

Answer + AI explanation with Pro

1. A.partition evolution
2. B.time travel
3. C.Iceberg REST catalog
4. D.schema evolution

Answer + AI explanation with Pro

1. A.time travel — An open table format optimized for upserts and incremental processing, offering copy-on-write and merge-on-read storage modes.
2. B.time travel — Querying a table as of a past snapshot or timestamp, supported by Iceberg, Delta, and warehouses like Snowflake for audit and recovery.
3. C.time travel — Two update strategies: copy-on-write rewrites whole files on update for fast reads, merge-on-read writes delta files for fast writes but slower reads.
4. D.time travel — A standardized REST API for Iceberg metadata operations, letting many engines share one catalog; adopted by Polaris, BigLake metastore, and Unity Catalog.

Answer + AI explanation with Pro