# Data Contracts A **data contract** is a declarative specification of what data should look like at a particular point in your system. Rather than writing imperative validation code every time you need to check data, contracts let you define *expectations as data* and then enforce them anywhere. Pointblank's [Contract](../../reference/Contract.md#pointblank.Contract) class combines three things into a single, portable unit: 1. **Schema**: what columns exist and what types they are 2. **Validation steps**: semantic rules the data must satisfy 3. **Metadata**: who owns it, what version it is, and what to do on failure Contracts can be serialized to YAML, version-controlled, and shared across teams. They can serve as the single source of truth for data quality expectations at a given boundary. # Creating Your First Contract The simplest contract just names a set of validation steps: ``` python import pointblank as pb import polars as pl # Define a contract for customer data customer_contract = pb.Contract( name="customer_records", steps=[ pb.Step("col_vals_not_null", columns=["customer_id", "email"]), pb.Step("col_vals_regex", columns="email", pattern=r"^[^@]+@[^@]+\.[^@]+$"), pb.Step("rows_distinct", columns_subset=["customer_id"]), ], ) ``` This contract essentially says: "Any data calling itself `customer_records` must have non-null IDs and emails, valid email formats, and unique customer IDs." Now validate some data against it: ``` python customers = pl.DataFrame( { "customer_id": ["C001", "C002", "C003", "C004"], "email": ["alice@example.com", "bob@corp.io", "charlie@mail.org", "dave@startup.co"], "name": ["Alice", "Bob", "Charlie", "Dave"], "signup_date": ["2024-01-15", "2024-02-20", "2024-03-10", "2024-04-05"], } ) customer_contract.validate(customers) ```
Pointblank Validation
Contract: customer_records Polarscustomer_records
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_not_null()
customer_id -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
email -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_regex()
email ^[^@]+@[^@]+\.[^@]+$ ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
rows_distinct()
customer_id -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
The `.validate()` method compiles the contract into a [Validate](../../reference/Validate.md#pointblank.Validate) object, runs all the checks, and returns the interrogated result. You get the same rich validation report you're used to but the contract itself is a reusable, declarative artifact. # Adding a Schema Contracts can include a [Schema](../../reference/Schema.md#pointblank.Schema) to enforce structural expectations (column names and data types): ``` python order_contract = pb.Contract( name="order_data", direction="source", schema=pb.Schema( order_id="String", customer_id="String", amount="Float64", quantity="Int64", status="String", ), steps=[ pb.Step("col_vals_not_null", columns=["order_id", "customer_id", "amount"]), pb.Step("col_vals_gt", columns="amount", value=0), pb.Step("col_vals_ge", columns="quantity", value=1), pb.Step("col_vals_in_set", columns="status", set=["pending", "shipped", "delivered"]), pb.Step("rows_distinct", columns_subset=["order_id"]), ], version="1.0.0", owner="data-platform-team", ) ``` When a schema is defined, the contract automatically adds a [col_schema_match()](../../reference/Validate.col_schema_match.md#pointblank.Validate.col_schema_match) step before all other validation steps. This means the schema check runs first, and if the table doesn't have the right columns and types, you'll know immediately. ``` python orders = pl.DataFrame( { "order_id": ["ORD-001", "ORD-002", "ORD-003", "ORD-004", "ORD-005"], "customer_id": ["C001", "C002", "C001", "C003", "C002"], "amount": [29.99, 149.50, 9.99, 75.00, 220.00], "quantity": [1, 3, 1, 2, 5], "status": ["shipped", "pending", "delivered", "pending", "shipped"], } ) order_contract.validate(orders) ``` g0MjM1OSw4LjcxOTIzMDc0IEMxMy43ODIyNzA0LDguNzM2NjM5NjcgMTMuNjg3NzY1NCw4Ljc3NjQzMTE1IDEzLjYwNTY5NTYsOC44Mzg2MDUxOCBMMTAuMjQzMzE1NiwxMS4zODUyNTk4IEMxMC4wNzY2ODg2LDExLjUwNDYzNDMgOS45NzcyMDk5MywxMS42OTg2MTgxIDkuOTc3MjA5OTMsMTEuOTAyNTQ5MSBDOS45NzcyMDk5MywxMi4xMDY0ODA3IDEwLjA3NjY4ODYsMTIuMzAwNDYzOSAxMC4yNDMzMTU2LDEyLjQxOTgzODMgTDEzLjYwNTY5NTYsMTQuOTY2NDkzIEMxMy44OTE2OTcsMTUuMTgwMzcyNSAxNC4yOTcwNzI5LDE1LjEyMzE3MjEgMTQuNTEwOTUxNywxNC44MzcxNzA3IEMxNC43MjQ4MzEzLDE0LjU1MTE2OTIgMTQuNjY3NjMwOSwxNC4xNDU3OTQgMTQuMzgxNjI5NCwxMy45MzE5MTQ1IEwxMi41MzEzMjU3LDEyLjUzOTIxMjcgTDIxLjg4MTI0OTUsMTIuNTM5MjEyNyBMMjEuODgxMjQ5NSwxMS4yNjU4ODU0IEwxMi41MzEzMjU3LDExLjI2NTg4NTQgTDE0LjM4MTYyOTQsOS44NzMxODM2NCBDMTQuNjM3Nzg3Miw5LjcxNjUwNDUzIDE0Ljc0OTcwMDYsOS40MDA2NjAxNCAxNC42NDc3MzUxLDkuMTE3MTQ1NTMgQzE0LjU0ODI1NjQsOC44MzM2MzE1NiAxNC4yNjIyNTUsOC42NTk1NDM1MiAxMy45NjM4MTg5LDguNjk5MzM1IFoiIGlkPSJhcnJvdyIgZmlsbD0iIzAwMDAwMCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoMTUuOTI5MjMwLCAxMS44OTQ3MzcpIHJvdGF0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" />
Pointblank Validation
Contract: order_data v1.0.0 Polarsorder_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_schema_match()
-- SCHEMA ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
order_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_not_null()
customer_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_not_null()
amount -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_gt()
amount 0 ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_ge()
quantity 1 ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
col_vals_in_set()
status pending, shipped, delivered ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 8
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" /> rows_distinct()
order_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
Owner: data-platform-teamVersion: 1.0.0
Notes

Step 1 (schema_check) ✓ Schema validation passed.

Schema Comparison
TARGET EXPECTED
COLUMN DATA TYPE COLUMN DATA TYPE
1 order_id String 1 order_id ✓ String ✓
2 customer_id String 2 customer_id ✓ String ✓
3 amount Float64 3 amount ✓ Float64 ✓
4 quantity Int64 4 quantity ✓ Int64 ✓
5 status String 5 status ✓ String ✓
Supplied Column Schema: [('order_id', 'String'), ('customer_id', 'String'), ('amount', 'Float64'), ('quantity', 'Int64'), ('status', 'String')]
Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64
# The [Step](../../reference/Step.md#pointblank.Step) Class Each validation rule in a contract is represented by a [Step](../../reference/Step.md#pointblank.Step), which is a declarative description of a single validation method call. Steps store the method name and its arguments as plain data: ``` python # These are equivalent ways to express a validation rule: step1 = pb.Step("col_vals_gt", columns="revenue", value=0) step2 = pb.Step("col_vals_between", columns="age", left=0, right=150) step3 = pb.Step("col_vals_in_set", columns="country", set=["US", "UK", "CA", "AU"]) step4 = pb.Step("col_vals_regex", columns="phone", pattern=r"^\+?[0-9\-\(\) ]+$") # Steps are data so you can inspect them print(step1) print(step1.method) print(step1.kwargs) ``` Step('col_vals_gt', columns='revenue', value=0) col_vals_gt {'columns': 'revenue', 'value': 0} Because steps are data (not function calls), they serialize cleanly to YAML/JSON and can be reconstructed anywhere. The `method` field corresponds directly to a validation method on the [Validate](../../reference/Validate.md#pointblank.Validate) class, and the `kwargs` are passed through verbatim. ## Available Methods Any validation method from the [Validate](../../reference/Validate.md#pointblank.Validate) class can be used in a Step. Here are the most common ones for contract definitions: | Method | Purpose | |----|----| | [col_vals_gt()](../../reference/Validate.col_vals_gt.md#pointblank.Validate.col_vals_gt), [col_vals_lt()](../../reference/Validate.col_vals_lt.md#pointblank.Validate.col_vals_lt), [col_vals_ge()](../../reference/Validate.col_vals_ge.md#pointblank.Validate.col_vals_ge), [col_vals_le()](../../reference/Validate.col_vals_le.md#pointblank.Validate.col_vals_le) | Numeric bounds | | [col_vals_between()](../../reference/Validate.col_vals_between.md#pointblank.Validate.col_vals_between), [col_vals_outside()](../../reference/Validate.col_vals_outside.md#pointblank.Validate.col_vals_outside) | Range checks | | [col_vals_eq()](../../reference/Validate.col_vals_eq.md#pointblank.Validate.col_vals_eq), [col_vals_ne()](../../reference/Validate.col_vals_ne.md#pointblank.Validate.col_vals_ne) | Equality checks | | [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set), [col_vals_not_in_set()](../../reference/Validate.col_vals_not_in_set.md#pointblank.Validate.col_vals_not_in_set) | Categorical membership | | [col_vals_not_null()](../../reference/Validate.col_vals_not_null.md#pointblank.Validate.col_vals_not_null), [col_vals_null()](../../reference/Validate.col_vals_null.md#pointblank.Validate.col_vals_null) | Null checks | | [col_vals_regex()](../../reference/Validate.col_vals_regex.md#pointblank.Validate.col_vals_regex) | Pattern matching | | [col_exists()](../../reference/Validate.col_exists.md#pointblank.Validate.col_exists) | Column presence | | [rows_distinct()](../../reference/Validate.rows_distinct.md#pointblank.Validate.rows_distinct) | Uniqueness (use `columns_subset=`) | | [rows_complete()](../../reference/Validate.rows_complete.md#pointblank.Validate.rows_complete) | No nulls in any column | | [row_count_match()](../../reference/Validate.row_count_match.md#pointblank.Validate.row_count_match) | Expected row count | | [col_count_match()](../../reference/Validate.col_count_match.md#pointblank.Validate.col_count_match) | Expected column count | # Contract Metadata Contracts support rich metadata that makes them self-documenting and suitable for team workflows: ``` python production_contract = pb.Contract( name="clean_sales_report", direction="target", # "source" or "target" boundary version="2.1.0", # Semantic versioning for evolution owner="data-platform-team", # Who maintains this contract consumers=["analytics-team", "ml-team"], # Who depends on it description="Validated, deduplicated sales data ready for downstream consumption.", on_violation="warn", # "warn", "raise", or "log" schema=pb.Schema( sale_id="String", revenue="Float64", region="String", ), steps=[ pb.Step("col_vals_not_null", columns=["sale_id", "revenue", "region"]), pb.Step("col_vals_gt", columns="revenue", value=0), pb.Step("rows_distinct", columns_subset=["sale_id"]), ], thresholds=pb.Thresholds(warning=0.01, error=0.05, critical=0.10), ) print(production_contract) ``` Contract(name='clean_sales_report', direction='target', version='2.1.0', schema=, steps=3) ## Direction The [direction](../../reference/Contract.md#pointblank.Contract.direction) parameter is metadata that signals where in a pipeline this contract applies: - `"source"`: for inbound/raw data arriving from upstream - `"target"`: for outbound data leaving your transform Direction doesn't change validation behavior, but it's used in pipeline reports and helps teams understand the contract's role in the system. ## Violation Handling The [on_violation](../../reference/Contract.md#pointblank.Contract.on_violation) parameter controls what happens when validation fails (used by the [Pipeline](../../reference/Pipeline.md#pointblank.Pipeline) class, covered in the next guide page): - `"warn"` (default): issue a Python `UserWarning` - `"raise"`: raise a `RuntimeError` (halts execution) - `"log"`: log via the `pointblank.contract` logger # Using [to_validate()](../../reference/Contract.md#pointblank.Contract.to_validate) for Custom Workflows If you need more control, [to_validate()](../../reference/Contract.md#pointblank.Contract.to_validate) gives you back an un-interrogated [Validate](../../reference/Validate.md#pointblank.Validate) object that you can extend with additional checks: ``` python # Start from the contract, then add ad-hoc checks validation = ( order_contract .to_validate(orders) .col_vals_lt(columns="amount", value=500) # Additional check not in the contract .interrogate() ) validation ``` 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" />
Pointblank Validation
Contract: order_data v1.0.0 Polarsorder_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_schema_match()
-- SCHEMA ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
order_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_not_null()
customer_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_not_null()
amount -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_gt()
amount 0 ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_ge()
quantity 1 ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
col_vals_in_set()
status pending, shipped, delivered ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 8
rows_distinct()
order_id -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 9
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" /> col_vals_lt()
amount 500 ✓ 5 5
1.00		 0
0.00		 -- -- -- --
Owner: data-platform-teamVersion: 1.0.0
Notes

Step 1 (schema_check) ✓ Schema validation passed.

Schema Comparison
TARGET EXPECTED
COLUMN DATA TYPE COLUMN DATA TYPE
1 order_id String 1 order_id ✓ String ✓
2 customer_id String 2 customer_id ✓ String ✓
3 amount Float64 3 amount ✓ Float64 ✓
4 quantity Int64 4 quantity ✓ Int64 ✓
5 status String 5 status ✓ String ✓
Supplied Column Schema: [('order_id', 'String'), ('customer_id', 'String'), ('amount', 'Float64'), ('quantity', 'Int64'), ('status', 'String')]
Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64
This is useful when a contract captures your *baseline* expectations, but a specific workflow needs extra checks on top. # Contract Equality and Composition Steps support equality comparison, making it easy to verify contracts: ``` python # Steps are equal if they have the same method and kwargs s1 = pb.Step("col_vals_gt", columns="x", value=0) s2 = pb.Step("col_vals_gt", columns="x", value=0) s3 = pb.Step("col_vals_gt", columns="x", value=10) print(f"s1 == s2: {s1 == s2}") print(f"s1 == s3: {s1 == s3}") ``` s1 == s2: True s1 == s3: False You can compose contracts by combining steps from multiple sources: ``` python # Common checks shared across all tables common_steps = [ pb.Step("col_vals_not_null", columns=["id"]), pb.Step("rows_distinct", columns_subset=["id"]), ] # Table-specific checks sales_steps = [ pb.Step("col_vals_gt", columns="revenue", value=0), pb.Step("col_vals_in_set", columns="region", set=["NA", "EU", "APAC"]), ] # Compose them sales_contract = pb.Contract( name="sales_data", steps=common_steps + sales_steps, version="1.0.0", ) print(f"Total steps: {len(sales_contract.steps)}") ``` Total steps: 4 # Example: Validating with Failures Let's see what happens when data doesn't meet the contract: ``` python # Data with quality issues bad_orders = pl.DataFrame( { "order_id": ["ORD-001", "ORD-002", "ORD-001", "ORD-004", None], # duplicates + null "customer_id": ["C001", None, "C001", "C003", "C002"], # null "amount": [29.99, -5.00, 9.99, 0.0, 220.00], # negative + zero "quantity": [1, 3, 1, 0, 5], # zero (should be >= 1) "status": ["shipped", "pending", "invalid", "pending", "shipped"],# invalid value } ) order_contract.validate(bad_orders) ``` 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" /> iIzAwMDAwMCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoMTUuOTI5MjMwLCAxMS44OTQ3MzcpIHJvdGF0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" />
Pointblank Validation
Contract: order_data v1.0.0 Polarsorder_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_schema_match()
-- SCHEMA ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C66 2
col_vals_not_null()
order_id -- ✓ 5 4
0.80		 1
0.20		 -- -- -- CSV
#4CA64C66 3
col_vals_not_null()
customer_id -- ✓ 5 4
0.80		 1
0.20		 -- -- -- CSV
#4CA64C 4
col_vals_not_null()
amount -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C66 5
col_vals_gt()
amount 0 ✓ 5 3
0.60		 2
0.40		 -- -- -- CSV
#4CA64C66 6
col_vals_ge()
quantity 1 ✓ 5 4
0.80		 1
0.20		 -- -- -- CSV
#4CA64C66 7
col_vals_in_set()
status pending, shipped, delivered ✓ 5 4
0.80		 1
0.20		 -- -- -- CSV
#4CA64C66 8
rows_distinct()
order_id -- ✓ 5 3
0.60		 2
0.40		 -- -- -- CSV
Owner: data-platform-teamVersion: 1.0.0
Notes

Step 1 (schema_check) ✓ Schema validation passed.

Schema Comparison
TARGET EXPECTED
COLUMN DATA TYPE COLUMN DATA TYPE
1 order_id String 1 order_id ✓ String ✓
2 customer_id String 2 customer_id ✓ String ✓
3 amount Float64 3 amount ✓ Float64 ✓
4 quantity Int64 4 quantity ✓ Int64 ✓
5 status String 5 status ✓ String ✓
Supplied Column Schema: [('order_id', 'String'), ('customer_id', 'String'), ('amount', 'Float64'), ('quantity', 'Int64'), ('status', 'String')]
Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64
The validation report clearly shows which steps failed and how many test units were affected. This makes it easy to diagnose data quality issues and communicate them to data producers. # What's Next Now that you understand contracts, the next guide page covers **Pipelines**. They combine a source contract, a transform, and a target contract into a complete boundary enforcement workflow. This lets you validate data at both the *input* and *output* of your data transformations.