dbts 0.6.0

dbt environment runner with Snowflake zero-copy clone sandboxes

dbts

dbt environment runner with Snowflake zero-copy clone sandboxes.

dbts lets you run dbt against a private, per-developer Snowflake clone of your staging or live database — without managing a separate set of credentials, profile files, or CLIs. It also acts as a single front for running dbt against your shared dev, staging, and live targets.

Install

uv tool install dbts
# or run ad hoc:
uvx dbts ...

Quick start

1. Add a sandbox: target to ~/.dbt/profiles.yml (alongside your existing dev, staging, live). The database name must match the pattern <PREFIX>_SANDBOX_<USER>:

   <your_profile_name>:           # whatever your dbt_project.yml's profile: field says
     outputs:
       sandbox:
         type: snowflake
         account: <same as the other targets>
         user: <same as the other targets>
         role: <same as the other targets>
         authenticator: externalbrowser
         database: <prefix>_sandbox_<your_username>
         warehouse: <same as the other targets>
         schema: <same as the other targets>
   

2. Create your clone:

   dbts up --from staging
   

3. Run dbt against it:

   dbts build my_model
   dbts test  +my_model+
   

4. Refresh or drop when done:

   dbts refresh --from staging
   dbts drop
   

Commands

Sandbox — manage the per-developer zero-copy clone:

dbts up        --from staging|live         create the clone
dbts refresh   --from staging|live         drop and re-create the clone
dbts status                                show clone DB, source, age, owner
dbts drop                                  drop the clone

Inspect — preview / audit before (or after) a build:

dbts plan      [selectors...]              preview the build set
  --cost                                     estimate credits + runtime from QUERY_HISTORY
  --days N                                   QUERY_HISTORY lookback window (default 7, max 365)
dbts freshness [selectors...]              audit lineage; flag stale tables, suggest a rebuild
  --since                                    explicit threshold (24h, 7d, 1w, or ISO datetime)

dbt pass-through — forwarded to dbt with --target sandbox by default:

dbts run|build|test|compile|seed|snapshot|ls|show
dbts debug|deps|source|docs|parse|clean
  --target sandbox|staging|live|dev          choose target (-t)

Meta:

dbts version                               print installed version

Global flags: -v / --verbose (debug logging, including DDL), -q / --quiet (warnings only). -h is a shorthand for --help on every command. Run dbts <command> --help for the full option list.

Selectors

Bare positional model selectors work the same as in dbt:

dbts build my_model+              # forwarded as `--select my_model+`
dbts test +my_model+              # ancestors and descendants
dbts run a b c+                   # multi-selector union
dbts build my_model+ --exclude experiments
dbts build --select a b           # `--select` + bare positional are merged

Internally, bare positional args on run / build / test / compile / seed / snapshot / ls / show are promoted to a --select value before being forwarded to dbt. Other subcommands (debug, deps, docs, parse, clean, source) pass arguments through verbatim.

Previewing a build

dbts plan lists exactly which models a dbts build (or run/test/...) with the same selectors would touch. Useful when a build against --target live blows up halfway and you need to add --exclude rules.

dbts plan my_model+ another_model+ --target live           # offline, fast
dbts plan --select tag:slow --exclude path:models/intermediate
dbts plan my_model+ --target live --cost                   # + Snowflake cost breakdown

By default the command is offline — it parses the dbt project but never connects to Snowflake. Output groups models by directory and shows materialization, tags, and parent count per model. The footer prints suggested --exclude path:<dir> and --exclude tag:<name> snippets sized by how many models each would prune.

Pass --cost to also estimate Snowflake credits and runtime from SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY. With cost on, each per-model row gets median run and last seen columns (median execution time across recent runs of that model, whatever mode they happened to be in), and the footer adds total credits + USD for an incremental run vs a full refresh, plus a top-5 most expensive list. The default lookback is 7 days; widen with --days 30 (max 365).

Cost estimates require your dbt project to set a structured query_tag containing a model field (HelloFresh's set_query_tag macro is one example) and the connecting role to read SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY. USD figures use $3.00 per credit by default; override with $DBTS_CREDIT_RATE. If access is missing, dbts plan prints a hint and falls back to the offline output.

Auditing freshness after an incident

dbts freshness answers "did data flow correctly through this lineage?" by reading INFORMATION_SCHEMA.TABLES.LAST_ALTERED for every table in the selected build set, sorting topologically (parents before children), and highlighting stale links in red.

# Did the catch-up run reach everything downstream of these models? (defaults to --target live)
dbts freshness base__recipe__cps+ base__recipe__ingredient__cps+

# Audit the full lineage in both directions
dbts freshness +base__recipe__cps+

# Compare against an explicit incident timestamp
dbts freshness base__recipe__cps+ --since '2026-05-09 17:00'

# Audit your sandbox clone instead
dbts freshness base__recipe__cps+ --target sandbox

Default target: live. Unlike dbts build / dbts plan (which default to sandbox), dbts freshness defaults to live because the typical post-incident question is "did production catch up?". Pass --target sandbox|staging|dev to audit a different environment. The command translates the target into the actual physical database name (<DB>_SANDBOX_<USER>, <DB>_STAGING, <DB>_DEV, or unsuffixed for live), mirroring the project's generate_database_name macro convention.

How "stale" is decided

Two values drive the colored output:

• Baseline — the most recent LAST_ALTERED across all tables in the build set. It's the "freshest thing you have right now," shown in the header for context.
• Threshold — anything older than this is flagged as stale (red row).

Without --since, the threshold is adaptive: baseline − 6 hours. The threshold drifts with whatever's currently fresh, so you don't have to know what "fresh" means today.

dbts freshness recipe+
# baseline = 14:02 (freshest table)
# threshold = 08:02   (anything older is stale)

With --since, the threshold is explicit and the adaptive window is ignored. Useful when you know exactly when something broke:

dbts freshness recipe+ --since '2026-05-09 17:00'   # absolute (ISO datetime or date)
dbts freshness recipe+ --since 24h                  # relative (s, m, h, d, w)

The footer prints a copy-pasteable dbts build --select X+ line that covers the minimum set of stale-roots needed to catch the chain back up — building any model that's already fresh upstream is avoided.

What LAST_ALTERED actually means

The signal is Snowflake's INFORMATION_SCHEMA.TABLES.LAST_ALTERED, which dbt bumps on every INSERT, MERGE, UPDATE, or CREATE OR REPLACE — so "fresh" means "dbt touched it recently," not necessarily "new rows arrived." For the post-incident "did the chain re-execute?" question, that's exactly the right semantic. Requires the connecting role to read INFORMATION_SCHEMA.TABLES.

Project-side coupling

dbts assumes the dbt project's generate_database_name macro recognises ENV=sandbox and routes models into a _SANDBOX_<USER> suffixed database. See the dbt project's README for the macro snippet.

Profile resolution

dbts resolves the dbt profile name in this order:

1. $DBTS_PROFILE if set.
2. The profile: field in dbt_project.yml at the project root.

Jinja {{ env_var('NAME', 'default') }} calls in the profile: field are rendered against the current environment, so projects whose profile name is templated (e.g. tardis_{{ env_var('warehouse', 'snowflake') }}) work out of the box.

Development

uv sync --group dev
uv run pytest
prek install   # one-time, runs ruff + ty on every commit

prek (Astral's Rust port of pre-commit) is the recommended runner for the hooks defined in .pre-commit-config.yaml. Install it via brew install prek or uv tool install prek. The standard pre-commit binary works just as well if you'd rather use it.

Cutting a release (after moving [Unreleased] entries under a [X.Y.Z] heading in CHANGELOG.md):

./scripts/release.sh 0.6.0

The script bumps pyproject.toml, syncs the lockfile, runs the full check suite, commits, tags, pushes, and creates the GitHub release with notes pulled from CHANGELOG.md. See scripts/README.md for both supported workflows and recovery tips.