Apache Iceberg vs DuckDB
No clear leader: DuckDB (62.4) and Apache Iceberg (62.2) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for data warehouse software. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Apache Iceberg
Apache Iceberg is a table format that enables reliable, ACID-compliant SQL operations on big data. It allows multiple query engines—such as Spark, Trino, Flink, Hive, and Impala—to safely read and write the same tables concurrently, bringing traditional database semantics to analytics workloads.
DuckDB
An in-process SQL analytical database that runs natively in applications across operating systems and environments. Supports querying files and cloud data directly with a SQL dialect and includes vector search capabilities.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- Pandas
- S3
Apache Iceberg
- Spark
- Trino
- Presto
- Flink
- Hive
- Impala
- PyArrow
- DuckDB
- Polars
- Ray
- Datafusion
- BigQuery
- SQLAlchemy
- GCS
- Azure Data Lake Storage
- Hadoop
- Kerberos
- Thrift
DuckDB
- Postgres
- AWS
- Azure
- Google Cloud
- Hugging Face
- SQLite
- MySQL
- Iceberg
- Delta Lake
- Parquet
- JSON
- Arrow
- Avro
- dplyr
- Jupyter
- Marimo
- Claude
- Cloudflare
- ODBC
- HTTP
- Excel
- Vortex
- DuckLake
- Lance
- +1 more
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.