SQLBuild

Verify early. Test properly. Deploy reversibly. SQL pipelines with the rigor of real software.

Valid isn't the same as correct. Your SQL compiles, runs, and returns rows; none of that means the number is right, and a silently-wrong number a stakeholder already trusted is the bug that actually hurts.

SQLBuild brings software-engineering rigor to SQL pipelines: catch errors before the warehouse runs them, test your logic locally, and opt into change-aware execution when you need it. It is a standalone, open-source framework for building SQL and Python data pipelines.

All state is persisted as append-only tables in the warehouse alongside your data: no external state database, no manifest files, no paid add-on. Start with straightforward SQL models, then add ingestion, Python nodes, and opt-in virtual environments as your project grows.

Key features

  • Test your logic, not just your columns. Multi-model SQL tests resolve every intermediate model from its real SQL, plus end-to-end scenarios with local DuckDB replay for fast CI with no warehouse. Catch wrong logic before it ships, not just nulls.
  • Verify early. Define models as SQL files with MODEL() headers. SQLBuild resolves references, validates SQL, infers columns, checks contracts, and computes column lineage before anything runs, all offline. It fails at compile, not halfway through a warehouse run.
  • Fast and open static analysis. SQL parsing, validation, column inference, lineage, and transpilation run on Polyglot, a Rust SQL engine (MIT, 32+ dialects), so compile stays fast on large projects. The analysis is part of the Apache-2.0 core: no proprietary engine, no login, no paid tier.
  • Audits that block bad data. Audits run before data reaches the target table. Full table builds materialize into a staging table and only promote if audits pass; incremental models validate each batch before DML.
  • Deploy reversibly (opt-in). Virtual environments add instant low-copy branching, partial promotion, rollback, checkpoints, and reconciliation. Opt-in, not a tax you pay upfront.
  • Opt-in change-aware execution. Models, seeds, UDFs, and Python nodes are fingerprinted, and source freshness is tracked. In virtual environments, pass --changes-only or set changes_only = true to skip work that is already current; commands otherwise run the full selected scope.
  • Warehouse-native state. All change-tracking state lives in append-only tables (_sqlbuild_fingerprints, _sqlbuild_source_freshness, _sqlbuild_node_results) in your warehouse schemas. No external state machine, no corruption risk.
  • Cursor-based incremental processing. Automatic gap detection and resume, with microbatch mode for large ranges. No external checkpoint to maintain.
  • Ingestion and Python nodes. Load external data with Python @loader functions, and run @task, @asset, and @check nodes as first-class members of the same DAG as your SQL models.

See the documentation for the full feature set, including providers, lifecycle hooks, Python macros, UDFs, custom materializations, data diffs, zero-copy cloning, and virtual environments. To coordinate dbt and SQLBuild projects, see the dbt compatibility guide.

Quick start

pip install sqlbuild
# or
uv pip install sqlbuild

Create and run the included playground project:

sqb playground waffle-shop
cd waffle-shop
sqb plan
sqb build
sqb test

Example

A model is a SQL file with a MODEL() header and a SELECT. References use __ref() and __source(), and configuration, schema, and audits are declared inline:

MODEL (
  materialized table,
  columns (
    order_id (audits [not_null, unique]),
  ),
  tags [marts],
);

SELECT
  o.order_id,
  o.customer_id,
  p.amount_cents AS total_cents
FROM __ref("stg_orders") o
JOIN __ref("stg_payments") p USING (order_id)

A unit test mocks sources and asserts on the model, resolving every intermediate model automatically:

TEST();

WITH
__source__raw__orders AS (
  @mock_orders()
),
__source__raw__payments AS (
  SELECT
    1 AS payment_id,
    1 AS order_id,
    1500 AS amount_cents,
    'credit_card' AS method
),
__expected__fact_orders AS (
  SELECT 1 AS order_id, 100 AS customer_id, 1500 AS total_cents
)
SELECT 1

See the documentation for incremental models, scenarios, loaders, and more.

Python project layout

Project-owned Python must live in a supported extension location such as factories/, libs/, macros/, providers/, or another documented Python resource root. Factory locations contain normal Python: constants, classes, undecorated helper functions, and modules such as _helpers.py are allowed, while decorators determine which functions become SQLBuild resources. Compilation rejects Python under invented project roots so indirectly importable modules cannot create an unofficial project structure. Keep repository pytest tests outside the SQLBuild project's tests/ directory, which is reserved for SQLBuild SQL tests and scenarios. Documented integration paths such as dagster/, rivers_pipeline/, and their definitions.py modules are also supported.

Kata SQL architecture checks

Kata is SQLBuild's opt-in, error-only SQL model shape checker. It runs offline over the compiled project, reports coded faults with remediations, and never rewrites SQL. Its built-in lifecycle is native: Rust resolves rule policy, parses each model, evaluates built-ins, applies suppressions, and owns the persistent cache and deterministic result ordering.

Kata is disabled until the project selects at least one rule. Select the complete built-in policy in sqlbuild_project.toml with its namespace prefix:

[kata]
select = ["SQBK"]

SQBK activates every built-in rule. Narrower prefixes such as SQBKS activate one family, exact codes select individual rules, and ignore removes matching rules. Audit, unit-test, and custom-rule test-case minimums each default to one and can be overridden under [kata.thresholds].

Kata also keeps model ownership shallow and explicit. Configured level paths separate warehouse layers from domain ownership; every owner is a leaf or a branch, subdomain depth defaults to one, and declaration roles remain bounded flat-or-grouped containers:

[kata.layout]
levels = ["staging", "intermediate/clean", "intermediate/enriched", "mart"]
domain_roots = ["market/betfair", "model/horsenet/ratings"] # optional disambiguation

[kata.thresholds]
max_subdomain_depth = 1
min_shared_owner_prefix_directories = 2

Run sqb kata, inspect metadata with sqb kata rule SQBKS101, and generate agent guidance from the same active ruleset with sqb kata skills. Use sqb kata skills --check in CI to detect stale guidance. --json, --select, and --exclude are available for automation and model scoping.

Repository rules use the public API:

from sqlbuild.kata import RuleContext, kata


@kata(
    code="XSQBKP001",
    family="prices",
    slug="typed-currency",
    message="price models must declare a currency column",
    remediation="Declare currency in the MODEL columns contract at this model path.",
)
def typed_currency(*, model, ctx: RuleContext):
    return [] if any(column.name == "currency" for column in ctx.declared_columns) else [
        ctx.path_fault()
    ]

Load repository-owned files through rule_paths = ["kata/rules"] or dotted packages through rule_modules. Test each custom rule with RuleCase and evaluate_rule. Selecting custom rules disables caching unless [kata.cache] require_cacheable = true; cacheable rules may import only the supported pure modules and must access project files through RuleContext.

Python is used only for the SQLBuild compiler adapter and selected custom rules. Built-in-only runs cross into the native engine once as a compiled model batch and do not materialize or walk Python AST objects. A selected custom rule can still use the public RuleContext and raw Polyglot AST escape hatch; its findings rejoin native suppression, ordering, and cache policy.

Exact rule_exceptions require a rule, file, and reason and fail when stale. Broader rule_ignores and lone-star allowances also require reasons but are intentionally not stale-checked.

Supported adapters

Adapter Status
DuckDB Supported
MotherDuck Supported
Snowflake Supported
BigQuery Supported
Databricks Supported
PostgreSQL Supported
SQL Server Supported

ClickHouse, Redshift, Trino, Spark, and Athena are on the way.

Snowflake cost estimates

Native Snowflake builds automatically show a compact per-run busy-compute estimate. SQLBuild attributes visible overlapping query intervals fairly across active queries, converts attributed seconds using the warehouse-size credit rate, and estimates USD from the configured rate:

[cost]
usd_per_credit = 3.00

The default is 3.00 USD per credit and is visibly marked as a default. Configure the value with your Snowflake contract rate. Use sqb cost, sqb cost latest, sqb cost <run_id>, or sqb cost history --since 7d to inspect persisted records. --json and --json-output PATH provide a versioned, decimal-safe output contract. Pending detail records are refreshed from Snowflake when inspected again.

These values are attributed compute credits and estimated cost, not Snowflake-billed credits or invoice reconciliation. The estimate uses only query history visible to the executing role and does not reconstruct invisible concurrent work, warehouse resume or idle tail, the 60-second minimum, cloud-services credits, contract adjustments, or multi-cluster billing. Run metadata and query IDs are stored under target/executions/<run_id>/; that statement ledger stores only an SQL digest, not SQL text. Executed SQL artifacts are stored separately under the sensitive target/run/ tree.

Documentation

Full documentation is available at docs.sqlbuild.com.

Runtime operator and extension contracts:

Contributing

We welcome contributions. Please see CONTRIBUTING.md for guidelines.

License

SQLBuild is licensed under the Apache License 2.0.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.87.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for kvlonge from gravatar.com kvlonge

Requires

Python >=3.12

Provides Extra

bigquery dagster databricks dbt dlt ingestr postgres rivers snowflake sqlserver

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sqlbuild-0.83.3.tar.gz (1.5 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

sqlbuild-0.83.3-cp312-abi3-win_amd64.whl (5.6 MB view details)

Uploaded CPython 3.12+Windows x86-64

sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.12+manylinux: glibc 2.17+ x86-64

sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (5.8 MB view details)

Uploaded CPython 3.12+manylinux: glibc 2.17+ ARM64

sqlbuild-0.83.3-cp312-abi3-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.12+macOS 11.0+ ARM64

sqlbuild-0.83.3-cp312-abi3-macosx_10_12_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.12+macOS 10.12+ x86-64

File details

Details for the file sqlbuild-0.83.3.tar.gz.

File metadata

  • Download URL: sqlbuild-0.83.3.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sqlbuild-0.83.3.tar.gz
Algorithm Hash digest
SHA256 83ff9db9bea5c5ec2fdb149f372f7069be07cab77522e9a0ae2fef008757cf13
MD5 e055bcee29094e47832f6f32867bb858
BLAKE2b-256 8eb2ef9893fb50285994e1dfd55fea8eb4f71862fa7976e11700acbd0be3eddf

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3.tar.gz:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlbuild-0.83.3-cp312-abi3-win_amd64.whl.

File metadata

  • Download URL: sqlbuild-0.83.3-cp312-abi3-win_amd64.whl
  • Upload date:
  • Size: 5.6 MB
  • Tags: CPython 3.12+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sqlbuild-0.83.3-cp312-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 cdf7a574357dc5f8145be165d9a51a020d77583831c46741d87634c316884ff2
MD5 2488a6db87a63d52ceb4fe1cef30cc7c
BLAKE2b-256 a2df6479601351f747d2755ab7a2f40403af3f0765c543c18873a6a9e50ecb33

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3-cp312-abi3-win_amd64.whl:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d60c9fb69362619feeb592b4d730b1d8ee0aab3253891446e201e5de0658f566
MD5 0f20a8b736e10ca8fe20bb4f0ee3b878
BLAKE2b-256 4e281539365c0144b3499d90530e0bc621aaa7d3943e000cb3e02e2be22c82ae

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 75aa19c571cb821d859503083ae7bbeb36c14a8450a5068c8936ce92a28324fa
MD5 4de244656593cb4d2bd9776645a829a6
BLAKE2b-256 134c7591ba29c4938fc97a5cdbd77c125ec4b19bb0be7cfeec1fd7131ccfe5c8

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlbuild-0.83.3-cp312-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sqlbuild-0.83.3-cp312-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7637981dec9fb4bc22e92ab351b2fe5ffe034286fb616befcdcbb60c67529173
MD5 4418b8e515899c6018975c4f910f5510
BLAKE2b-256 cde2d9aff244ab76057af9b3395b3da4d59fc5024fe0a0332a3c00b79028cd19

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3-cp312-abi3-macosx_11_0_arm64.whl:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlbuild-0.83.3-cp312-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for sqlbuild-0.83.3-cp312-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 e54e3c80cae43410649409066f31c3149b0d8e2eedc781306b228572ce4314e5
MD5 f06ab8979d2bed091225c23b03e7be98
BLAKE2b-256 66f6e0c9abff43f61c905144dd44e6adeadd840689f80281c831310978046133

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlbuild-0.83.3-cp312-abi3-macosx_10_12_x86_64.whl:

Publisher: publish.yml on chio-labs/sqlbuild

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.87.3

6 files

0.87.2

6 files

0.87.1

6 files

0.87.0

6 files

0.86.0

6 files

0.85.0

6 files

0.84.0

6 files

0.83.4

6 files

This release

0.83.3 This release

6 files

0.83.2

6 files

0.83.1

6 files

0.83.0

6 files

0.82.6

6 files

0.82.5

6 files

0.82.4

6 files

0.82.3

6 files

0.82.2

6 files

0.82.1

6 files

0.82.0

6 files

0.81.2

6 files

0.81.1

6 files

0.81.0

6 files

0.80.2

6 files

0.80.1

6 files

0.80.0

6 files

0.79.0

6 files

0.78.0

6 files

0.77.2

6 files

0.77.1

6 files

0.77.0

6 files

0.76.9

6 files

0.76.8

6 files

0.76.7

6 files

0.76.6

6 files

0.76.5

6 files

0.76.4

6 files

0.76.3

6 files

0.76.2

6 files

0.76.1

6 files

0.76.0

6 files

0.75.1

6 files

0.75.0

6 files

0.74.4

6 files

0.74.3

6 files

0.74.2

6 files

0.74.1

6 files

0.74.0

6 files

0.73.0

6 files

0.72.2

6 files

0.72.1

6 files

0.72.0

6 files

0.71.6

6 files

0.71.5

6 files

0.71.4

6 files

0.71.3

6 files

0.71.2

6 files

0.71.1

6 files

0.71.0

6 files

0.70.0

6 files

0.69.0

6 files

0.68.0

6 files

0.67.2

6 files

0.67.1

6 files

0.67.0

6 files

0.66.6

6 files

0.66.5

6 files

0.66.4

6 files

0.66.3

6 files

0.66.2

6 files

0.66.1

6 files

0.66.0

6 files

0.65.6

6 files

0.65.5

6 files

0.65.4

6 files

0.65.3

6 files

0.65.2

6 files

0.65.1

6 files

0.65.0

6 files

0.64.0

6 files

0.63.8

6 files

0.63.7

6 files

0.63.6

6 files

0.63.5

6 files

0.63.4

6 files

0.63.3

6 files

0.63.2

6 files

0.63.1

6 files

0.63.0

6 files

0.62.2

6 files

0.62.1

6 files

0.62.0

6 files

0.61.0

6 files

0.60.0

6 files

0.59.0

6 files

0.58.0

6 files

0.57.0

6 files

0.56.2

6 files

0.56.1

6 files

0.56.0

6 files

0.55.9

6 files

0.55.8

6 files

0.55.7

6 files

0.55.6

6 files

0.55.5

6 files

0.55.4

6 files

0.55.3

6 files

0.55.2

6 files

0.55.1

6 files

0.55.0

6 files

0.54.2

6 files

0.54.1

6 files

0.54.0

5 files

0.53.0

2 files

0.52.0

2 files

0.51.0

2 files

0.50.0

2 files

0.49.0

2 files

0.48.7

2 files

0.48.6

2 files

0.48.5

2 files

0.48.4

2 files

0.48.3

2 files

0.48.2

2 files

0.48.1

2 files

0.48.0

2 files

0.47.0

2 files

0.46.0

2 files

0.45.5

2 files

0.45.4

2 files

0.45.3

2 files

0.45.2

2 files

0.45.1

2 files

0.45.0

2 files

0.44.4

2 files

0.41.1

2 files

0.41.0

2 files

Yanked

0.40.1

2 files

Yanked reason: Just a shitty release

Yanked

0.40.0

2 files

Yanked reason: shitty release

0.39.3

2 files

0.39.2

2 files

0.39.1

2 files

0.39.0

2 files

0.38.6

2 files

0.38.5

2 files

0.38.4

2 files

0.38.3

2 files

0.38.2

2 files

0.38.1

2 files

0.38.0

2 files

0.37.7

2 files

0.37.6

2 files

0.37.5

2 files

0.37.4

2 files

0.37.3

2 files

0.37.2

2 files

0.37.1

2 files

0.36.0

2 files

0.35.0

2 files

0.34.0

2 files

0.33.0

2 files

0.32.0

2 files

0.31.0

2 files

0.30.1

2 files

0.30.0

2 files

0.29.0

2 files

0.28.1

2 files

0.28.0

2 files

0.27.0

2 files

0.26.2

2 files

0.26.1

2 files

0.26.0

2 files

0.25.1

2 files

0.25.0

2 files

0.24.0

2 files

0.23.0

2 files

0.22.1

2 files

0.22.0

2 files

0.21.0

2 files

0.20.1

2 files

0.20.0

2 files

0.19.0

2 files

0.18.0

2 files

0.16.2

2 files

0.16.1

2 files

0.15.0

2 files

0.14.0

2 files

0.13.0

2 files

0.12.0

2 files

0.10.0

2 files

0.9.0

2 files

0.8.0

2 files

0.7.0

2 files

0.4.0

2 files

0.3.0

2 files

0.2.1

2 files

0.2.0

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page