On BigQuery
AvailableLogical vs physical dataset billing, long-term storage candidates, CLUSTER BY recommendations, and large tables with neither partition nor cluster.
BigQuery productStorage is the quiet line item: the wrong billing model, tables nobody queries, and layouts that make every job read too much. We compare billing models and flag tables whose layout no longer fits.
Logical vs physical dataset billing, long-term storage candidates, CLUSTER BY recommendations, and large tables with neither partition nor cluster.
BigQuery productComing soon: Time Travel and Fail-safe bytes, clustering depth that never converges, and tables nobody has queried still sitting in TABLE_STORAGE_METRICS.
Snowflake productComing soon: ZORDER / liquid clustering gaps, unused Delta tables, and file-size debt that makes every scan open too many Parquet files.
Databricks productSizes, billing model, partition and cluster keys — catalog only.
A 4 TB table nobody queried is a different finding from one queried hourly.
Partition, cluster, or drop. You decide which, and who applies it.

system.query.history and system.compute.warehouses rank full scans, idle warehouses, and shuffle-heavy jobs. SlashPeak never needs SELECT on your tables.

Photon speeds up a shuffle join; it does not shrink it. Adaptive execution helps at the edges, but an unclustered join key still burns DBUs every run.

ZORDER and liquid clustering write min/max stats so Photon can skip files. The wrong columns, or a stale table, means it still reads everything.

A serverless warehouse with a 10-minute auto-stop bills 10 minutes after the last query. Oversizing is a separate leak, and system tables show both.
Understand, reduce and govern your cloud data spend with SlashPeak — starting today.