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 works as a standalone framework or points at your existing dbt project with no migration and no edits to your dbt files.

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. It keeps a low, dbt-like floor for SQL models and adds 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.
  • Works with your existing dbt project. Point SQLBuild at a dbt project and run ordinary dbt selections alongside SQLBuild models. It reads the manifest and drives the dbt CLI as a subprocess; it never edits your dbt files. dbt-native --state and --defer remain available for production-shaped, state-aware selections. See dbt compatibility.
  • 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.

Works with your existing dbt project

Point SQLBuild at a dbt project and run a sqb dbt command. The first time, it bootstraps a minimal twin project from your dbt_project.yml and profile (reusing your dbt connection), then runs your selection through dbt:

sqb dbt build --select path:models/marts

SQLBuild preserves dbt-native state and defer arguments when you need dbt's own state-aware selection:

sqb dbt build --state path/to/state --defer --select state:modified+

Ordinary sqb dbt plan, run, and build commands do not fingerprint dbt models or inspect production state automatically. Use dbt-native --state/--defer for production-shaped comparisons. See dbt compatibility.

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.

Concurrent microbatches

Native delete_insert microbatch models can opt into parallel batch execution. Serial execution is the default and remains recommended unless parallel batches provide a meaningful runtime benefit. Enable the project capability and set a per-model ceiling:

[settings]
microbatch_concurrency = true
microbatch_unaccounted_partition_policy = "synthesize"

MODEL (
  materialized incremental,
  incremental_mode microbatch,
  incremental_strategy delete_insert,
  cursor event_time,
  cursor_type timestamp,
  cursor_grain hour,
  batch_size 1h,
  batch_concurrency 4,
);

batch_concurrency is a model ceiling, not a separate worker pool. Batches share the build's global concurrency limit and connection pool with every other DAG node. First-run and full-refresh target bootstrap remain serialized. Concurrent batches are rejected for adapters that have not explicitly declared same-target concurrent delete/insert support.

Every native microbatch, including serial models and projects where the capability is disabled, records successful half-open partitions and their model fingerprints. Standard builds use the warehouse _sqlbuild_microbatches table. Virtual builds store equivalent events in the configured DuckDB or Postgres state backend and scope them to the immutable physical model version. Virtual builds renew a physical-version lease while mutating shared data; standard-mode orchestrators must prevent overlapping invocations for the same model destination.

Janitor may read microbatch history to protect active physical versions, but it never drops or prunes _sqlbuild_microbatches or virtual microbatch_events. Removing virtual event history is an explicit sqb state reset operator action, not ordinary retention cleanup.

The append-only history distinguishes physical continuity from fingerprint continuity. Known gaps are recovered before new normal work. Automatic replay_on_change ranges are durable and version-specific, while explicit backfills remain one-shot requests. If destination progress is not explained by retained history, microbatch_unaccounted_partition_policy controls reconciliation:

  • synthesize accepts inferred physical coverage and records an unknown fingerprint.
  • recover_empty counts candidates in bounded chunks, reruns empty intervals, and synthesizes non-empty intervals.
  • recover_all reruns every unaccounted interval without a preliminary count.

Synthetic coverage preserves forward progress but weakens version guarantees and remains visible in warnings and JSON output. Target DML and event insertion do not require a cross-system transaction; idempotent delete_insert recovery handles failures between those operations.

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].

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/runs/<run_id>/; raw SQL is not persisted.

Documentation

Full documentation is available at docs.sqlbuild.com.

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 (0.83.2)

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

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