data-contracts

name

data-contracts

description

Define and enforce schema contracts between producer and consumer teams — field types, nullability, allowed values, versioning, breaking vs. non-breaking changes, and change detection patterns. Use this skill whenever two teams or services share a dataset and upstream changes keep breaking the downstream silently, when a team wants to formalize what a dataset "promises" to its consumers, or when setting up schema validation at pipeline boundaries. Also trigger when the user asks about schema evolution, backward/forward compatibility, schema registries, Great Expectations for inter-team contracts, or when a producer is about to make a "small" schema change and you want to assess its downstream impact. If upstream and downstream are owned by different people, this skill should be active.

Data Contracts

Why Contracts Exist

Without contracts, schema changes are discovered by broken dashboards or failed pipeline runs — often hours or days after the change shipped. A data contract is a formal agreement between the team that produces a dataset (producer) and the teams that consume it (consumers). It specifies exactly what the dataset promises, and gives consumers a way to know when that promise is broken.

Contracts aren't bureaucracy — they're the interface boundary for data, the same way an API contract is the interface boundary for a service.

---

Contract Structure

A minimal contract covers:

1. Schema — fields, types, nullability, allowed values
2. Grain — what one row represents (e.g., "one row per customer per day")
3. Freshness — how recent the data is guaranteed to be
4. SLA — when the data is available by (e.g., "available by 6am UTC daily")
5. Owner — who to contact when something breaks
6. Version — the contract version, so consumers can pin to a stable version

Example contract in YAML

# contracts/churn_features_v2.yaml
contract:
  name: churn_features
  version: "2.1.0"
  owner: [e-mail]
  consumers:
    - [e-mail]
    - [e-mail]
  grain: "One row per customer_id per feature_date"
  sla:
    available_by: "06:00 UTC"
    freshness_max_lag_hours: 24

schema:
  - name: customer_id
    type: STRING
    nullable: false
    description: "FK to dim_customers"
  - name: feature_date
    type: DATE
    nullable: false
    description: "The date the features were computed for"
  - name: days_since_last_login
    type: INTEGER
    nullable: true
    description: "Null if customer has never logged in"
  - name: subscription_tier
    type: STRING
    nullable: false
    allowed_values: ["free", "pro", "enterprise"]
  - name: predicted_churn_score
    type: FLOAT
    nullable: true
    range:
      min: 0.0
      max: 1.0

---

Breaking vs. Non-Breaking Changes

Not all schema changes are equal. The producer team needs to know which changes require consumer sign-off before shipping.

Change	Breaking?	Why
Add a new nullable column	No	Consumers can ignore it
Add a new non-null column with no default	Yes	Existing queries may fail if they SELECT * or expect fixed column count
Remove a column	Yes	Any consumer using it breaks
Rename a column	Yes	Same as remove + add
Change type from INT to FLOAT	Usually no	Implicit upcast
Change type from FLOAT to INT	Yes	Data loss for fractional values
Narrow allowed_values (remove a valid value)	Yes	Existing data may violate the new constraint
Expand allowed_values (add a new value)	No	Consumers may need to handle the new case, but won't break immediately
Change grain (e.g., daily → hourly)	Yes	All downstream aggregations are now wrong
Change freshness SLA	Depends	If consumers depend on the old window, their pipelines may run against stale data

The rule of thumb: if removing the old behavior would cause any consumer's code or pipeline to produce wrong results or errors, it's breaking.

---

Versioning

Use semantic versioning: MAJOR.MINOR.PATCH.

• MAJOR — breaking changes. Consumers must update before the old version is retired.
• MINOR — non-breaking additions (new nullable columns, new allowed values).
• PATCH — documentation fixes, description changes, no schema change.

When shipping a breaking change:

1. Release the new version alongside the old one (v1 and v2 both exist)
2. Notify all registered consumers with a migration timeline
3. Give consumers at least N sprints (agree on N with stakeholders) to migrate
4. Deprecate the old version — mark it in the contract as status: deprecated
5. Remove the old version after the sunset date

---

Schema Validation at Pipeline Boundaries

Contracts only work if they're enforced. Add validation at the point where data crosses a team boundary.

Using Great Expectations

import great_expectations as gx

context = gx.get_context()
ds = context.sources.add_pandas("churn_features")
da = ds.add_dataframe_asset("batch")
batch = da.build_batch_request(dataframe=df)

suite = context.add_expectation_suite("churn_features_v2_contract")
suite.add_expectation(gx.expectations.ExpectColumnToExist(column="customer_id"))
suite.add_expectation(gx.expectations.ExpectColumnValuesToNotBeNull(column="customer_id"))
suite.add_expectation(gx.expectations.ExpectColumnValuesToBeInSet(
    column="subscription_tier", value_set=["free", "pro", "enterprise"]
))
suite.add_expectation(gx.expectations.ExpectColumnValuesToBeBetween(
    column="predicted_churn_score", min_value=0.0, max_value=1.0
))

results = context.run_validation_operator("action_list_operator", assets_to_validate=[batch])
if not results["success"]:
    raise ValueError("Contract validation failed — aborting load")

Run this check in the consumer's ingestion pipeline, not just in the producer's — the consumer owns detecting when the data doesn't match what was promised.

Schema Registry (Kafka / event-driven)

For streaming pipelines, use Confluent Schema Registry to enforce Avro or Protobuf schemas at the topic level.

from confluent_kafka.schema_registry import SchemaRegistryClient
from confluent_kafka.schema_registry.avro import AvroDeserializer

schema_registry_conf = {"url": "http://schema-registry:8081"}
sr_client = SchemaRegistryClient(schema_registry_conf)

# Deserializer enforces that the message matches the registered schema
# — rejects messages that don't conform
avro_deserializer = AvroDeserializer(sr_client)

Schema Registry enforces compatibility rules (BACKWARD, FORWARD, FULL) automatically — a producer that tries to register a breaking schema change is rejected.

---

Change Detection Pattern

The producer should run a diff check before deploying any schema change to a shared dataset. This catches accidental breaking changes.

import pandas as pd

def check_schema_diff(old_schema: dict, new_schema: dict) -> list[str]:
    issues = []
    old_fields = {f["name"]: f for f in old_schema["schema"]}
    new_fields = {f["name"]: f for f in new_schema["schema"]}

    # Removed columns
    for name in old_fields:
        if name not in new_fields:
            issues.append(f"BREAKING: column '{name}' removed")

    # Type changes
    for name, field in new_fields.items():
        if name in old_fields and field["type"] != old_fields[name]["type"]:
            issues.append(f"BREAKING: column '{name}' type changed from {old_fields[name]['type']} to {field['type']}")

    # Nullable changed to non-nullable
    for name, field in new_fields.items():
        if name in old_fields:
            if not field.get("nullable", True) and old_fields[name].get(, ):
                issues.append()

     issues

Add this check to CI so schema changes are flagged before they reach production.

---

Consumer Registration

Consumers should be registered in the contract so producers know who to notify when making changes.

consumers:
  - team: ml-team
    contact: [e-mail]
    uses:
      - customer_id
      - feature_date
      - predicted_churn_score
    pipeline: s3://ml-pipelines/churn/ingest.py
    registered: 2024-03-01
    sla_dependency: "needs data by 05:00 UTC for morning model training run"

Knowing which columns each consumer uses means you can safely remove columns that nobody is consuming — and you know exactly who to call when a breaking change is unavoidable.

---

Contracts in dbt

dbt's contracts block (available in dbt Core 1.5+) enforces schema contracts at model build time:

# models/marts/schema.yml
models:
  - name: fct_churn_features
    config:
      contract:
        enforced: true  # dbt will fail if the model doesn't match the declared schema
    columns:
      - name: customer_id
        data_type: varchar
        constraints:
          - type: not_null
      - name: feature_date
        data_type: date
        constraints:
          - type: not_null

When enforced: true, dbt compares the model's actual output schema against the declared one and fails the build on mismatch — catching drift before it reaches the warehouse.

---

Contract Lifecycle Checklist

When shipping a change to a shared dataset:

• Identified all registered consumers from the contract file
• Classified the change as breaking or non-breaking
• For breaking changes: new version deployed alongside old, not replacing it
• Consumers notified with migration deadline
• Contract YAML updated with new version number and changelog entry
• Schema validation checks updated to reflect the new contract
• Old version marked status: deprecated with sunset date
• CI enforces schema diff check on the contract file