In fear of fettering development and innovation, companies often allow engineers free reign to generate and analyze datasets at will. This often leads to unorganized data lakes: a ragtag collection of datasets from a diverse set of sources. Google Dataset Search (Goods) is a system which uses unobstructive post-hoc metadata extraction and inference to organize Google's unorganized datasets and present curated dataset information, such as metadata and provenance, to engineers. Building a system like Goods at Google scale presents many challenges. - Scale. There are 26 billion datasets. 26 billion (with a b)! - Variety. Data comes from a diverse set of sources (e.g. BigTable, Spanner, logs). - Churn. Roughly 5% of the datasets are deleted everyday, and datasets are created roughly as quickly as they are deleted. - Uncertainty. Some metadata inference is approximate and speculative. - Ranking. To facilitate useful dataset search, datasets have to be ranked by importance: a difficult heuristic-driven process. - Semantics. Extracting the semantic content of a dataset is useful but challenging. For example consider a file of protos that doesn't reference the type of proto being stored. The Goods catalog is a BigTable keyed by dataset name where each row contains metadata including - basic metatdata like timestamp, owners, and access permissions; - provenance showing the lineage of each dataset; - schema; - data summaries extracted from source code; and - user provided annotations. Moreover, similar datasets or multiple versions of the same logical dataset are grouped together to form clusters. Metadata for one element of a cluster can be used as metadata for other elements of the cluster, greatly reducing the amount of metadata that needs to be computed. Data is clustered by timestamp, data center, machine, version, and UID, all of which is extracted from dataset paths (e.g. /foo/bar/montana/August01/foo.txt). In addition to storing dataset metadata, each row also stores status metadata: information about the completion status of various jobs which operate on the catalog. The numerous concurrently executing batch jobs use status metadata as a weak form of synchronization and dependency resolution, potentially deferring the processing of a row until another job has processed it. The fault tolerance of these jobs is provided by a mix of job retries, BigTable's idempotent update semantics, and a watchdog that terminates divergent programs. Finally, a two-phase garbage collector tombstones rows that satisfy a garbage collection predicate and removes them one day later if they still match the predicate. Batch jobs do not process tombstoned rows. The Goods frontend includes dataset profile pages, dataset search driven by a handful of heuristics to rank datasets by importance, and teams dashboard.