Metadata management encompasses all practices and tools for collecting, organizing, governing, and making accessible the metadata that describes data assets in a lakehouse. Metadata takes many forms: technical metadata (schema, data types, partition layouts, file counts, row counts), operational metadata (when was it last updated, how long did the last pipeline run take), business metadata (what does this column mean, who is the data owner, what business process generated this data), and quality metadata (what percentage of values are null, what are the min/max values, has the distribution shifted).
Iceberg's Native Metadata Layer
Apache Iceberg's architecture is fundamentally metadata-rich. Column-level statistics (null count, NaN count, min value, max value, distinct count) are stored in every manifest file for every data file. This enables query engines to perform aggressive pruning without reading data files. This same metadata is invaluable for data catalog and observability tools: instead of running expensive column profiling queries, they can read statistics directly from Iceberg manifest files. The Iceberg REST Catalog API exposes this metadata programmatically, enabling metadata management platforms to ingest comprehensive technical metadata without touching the underlying Parquet files.
