sqlbuild 0.16.2

Typed, test-first SQL pipelines with local E2E testing

Typed, test-first SQL pipelines with local E2E testing.

SQLBuild is a SQL pipeline framework that validates SQL at compile time, blocks bad data before promotion, and runs full E2E tests with no warehouse required.

Key features

• SQL unit tests that chain across models - Mock your sources, assert on the model you care about, and SQLBuild resolves every intermediate model automatically. One test file can be a full integration test across your pipeline.
• End-to-end scenarios with local replay - Define coherent fixture worlds, run the real project graph in an isolated warehouse slice, capture JSONL snapshots, and replay them locally through DuckDB for fast CI feedback.
• Audits that block bad data - Audits run before data reaches the target table. For full table builds, SQLBuild materializes into a staging table and only promotes if audits pass. For incremental models, delta-phase audits validate each batch before DML.
• Python macros, not Jinja - Macros are real Python functions. Testable, debuggable, and composable with standard tooling.
• Change-aware incremental rebuilds - Fingerprint-based query change detection, schema diff tracking, and configurable backfill policies with automatic cascade through the DAG.
• Cursor-based incremental processing - Automatic gap detection and resume. If a model fails for several runs, the next build replays from where it left off. Microbatch mode splits large ranges into configurable batches.
• User-defined functions - SQL and Python UDFs managed as project resources, with table functions for predicate-pushdown-friendly alternatives to final-layer views.
• Environment diffs - Compare schemas and row-level data between environments with sqb diff prod:dev.
• Zero-copy cloning - Branch environments instantly with sqb clone without duplicating data. No manifest.json required.
• Custom materializations - Write materialization logic in Python with full framework integration, including audit hooks, schema change signals, and query change detection.
• Path-between selectors - --select fact_orders~daily_activity_rollup selects every model on the shortest path between two nodes.

Quick start

pip install sqlbuild
# or
uv add sqlbuild

Create and run the playground project:

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

How it works

1. Define your models as SQL files with MODEL() headers that declare configuration, schema, and audits inline
2. Compile to resolve references, validate SQL, infer column types, check contracts, and compute column lineage - all offline
3. Plan what needs to change based on fingerprints, schema diffs, and backfill policies
4. Build by executing the plan: materializing models, validating data before promotion, and ensuring bad data never reaches production
5. Test with chained unit tests, E2E scenario tests, and local replay through DuckDB - no warehouse required

Example

A simple staging model:

MODEL (
  materialized view,
  tags [staging],
);

SELECT
  id AS order_id,
  customer_id,
  ordered_at,
  status
FROM __source("raw__orders")

An incremental model with microbatch processing:

MODEL (
  materialized incremental,
  incremental_strategy delete_insert,
  cursor activity_hour,
  cursor_type timestamp,
  cursor_grain hour,
  cursor_inputs (
    fact_orders ordered_at,
  ),
  incremental_mode microbatch,
  batch_size 1d,
  tags [marts],
  post_hook ["grant select on @@CTX:target.qualified to role analytics"],
);

SELECT
  DATE_TRUNC('hour', o.ordered_at) AS activity_hour,
  COUNT(*) AS orders_placed,
  SUM(o.quantity) AS waffles_ordered
FROM __ref("fact_orders") o
GROUP BY DATE_TRUNC('hour', o.ordered_at)

A chained unit test:

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,
         'credit_card' AS payment_method
),
__assert__no_negative_totals AS (
  SELECT * FROM __ref("fact_orders") WHERE total_cents < 0
)
SELECT 1

An end-to-end scenario:

SCENARIO (
  description "Customer refund updates daily revenue correctly",
  tags [revenue, refund],
);

WITH
__source__raw__orders AS (
  SELECT 1 AS order_id, DATE '2026-01-01' AS order_date, 100.00 AS amount
),
__source__raw__refunds AS (
  SELECT 1 AS refund_id, 1 AS order_id, DATE '2026-01-01' AS refund_date, 25.00 AS amount
),
__expected__daily_revenue AS (
  SELECT DATE '2026-01-01' AS order_date, 75.00 AS revenue
),
__assert__all_refunds_linked AS (
  SELECT * FROM __ref("fact_refunds") WHERE order_id IS NULL
)
SELECT 1

Scenario files live under tests/scenarios/**/*.sql. Run them in the target warehouse with:

sqb scenario test
sqb scenario test revenue__customer_refund --retain

Capture local replay snapshots as JSONL under tests/_scenario_snapshots/<scenario_name>/:

sqb scenario capture revenue__customer_refund
sqb scenario test revenue__customer_refund --local
sqb scenario test --local --sync-snapshots
sqb scenario test --local --refresh

Snapshots are committable test data. Review them for sensitive values before committing.

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.