Data Warehouse vs Data Lakehouse - Architecting the Modern Stack
Data warehouse vs data lakehouse compared: core architectural differences, feature capabilities, and when to choose each approach for your data stack.
Thought Leadership
Explainers and opinions on Apache Iceberg, CDC and the lakehouse.
Data Lake vs. Data Lakehouse – Architecting the Modern Stack
Data lake vs data lakehouse: how a lakehouse adds ACID transactions, schema enforcement and governance to object storage and ends dual-tier systems.
Parquet vs Iceberg: File Format vs Table Format
Parquet is a columnar file format and Iceberg is a table format built on top of it. See how they differ on schema evolution, transactions and partitioning.
Iceberg Metadata Explained: Catalogs to Manifests
How Iceberg metadata works: the catalog, metadata.json, snapshots, manifest lists and manifest files, and how they enable time travel, pruning and ACID.
Hive vs Iceberg: Key Differences and Migration Tips
Hive vs Iceberg compared on metadata, schema evolution, partitioning, ACID, time travel and engine support, with a decision matrix and migration steps.
Iceberg vs Delta Lake Delete Methods Compared
How Iceberg and Delta Lake handle deletes: equality and position deletes, deletion vectors, and when copy-on-write or merge-on-read fits your lakehouse.