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Hello Data Engineers,
Let’s talk about data contracts – a buzzword you might have heard around the data engineering watercooler. In plain language, a data contract is like a formal agreement between those who produce data (upstream software engineers, services, APIs) and those who consume it (downstream analytics, pipelines). If you’ve ever had a pipeline break because someone upstream changed a JSON field name or CSV format without telling anyone, you’ve felt the pain a data contract aims to solve! A data contract explicitly defines the schema, shape, and expectations of data being shared, and often includes guarantees about delivery (like SLAs) and meaning (semantics). In essence, data contracts ensure everyone is on the same page about the data – no unwelcome surprises.
What Are Data Contracts and Why Should You Care?
By definition, “data contracts are formal agreements that define and enforce data schemas between producers and consumers”. Just as an API contract (like an OpenAPI spec) defines what a web service will send/receive, a data contract spells out the structure and allowed values of data that one system provides to others. This could be a file, an event stream, or a database table – any interface where data flows between teams. The contract might specify, for example, “Column X will always be an integer between 0 and 100, and we will populate it every day by 8 AM.” If the producer violates this (say column X is missing or has a string “N/A”), the contract is broken – akin to breaching a test or an SLA.
Why does this matter? Because modern data architectures are increasingly distributed. No longer is one central team managing everything in a monolithic warehouse. Now, domain-aligned teams own their data products. Without contracts, a change in Team A’s service can inadvertently wreak havoc on Team B’s dashboard. Data contracts bring accountability and communication to this relationship. The upstream team commits to not break the schema or data assumptions without notice, and the downstream team gains confidence (and can even automate enforcement of these rules). According to the folks at dbt Labs, the ability to define and validate data contracts is essential for cross-team collaboration in analytics engineering. It flips the script from the central data team trying to police everything to each producer taking responsibility for the quality of the data they emit (a “shift-left” of data quality).
Concretely, a data contract typically covers several aspects: Schema, Semantics, Service Level Agreements (SLAs), and Metadata/Governance. Schema is the structural definition (fields and types), semantics capture business rules or allowed values, SLAs might say how quickly data will be delivered or corrected if issues arise, and metadata could include owner contacts, version info, etc. It’s not just theory – many companies document these contracts in YAML/JSON specs or even use tools to enforce them in real-time. For example, here’s a snippet of a simple data contract in JSON Schema form:
{
"$id": "https://example.com/person.schema.json",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "person",
"type": "object",
"properties": {
"firstName": {
"type": "string",
"minLength": 2,
"maxLength": 250,
"description": "The person's first name."
},
"lastName": {
"type": "string",
"minLength": 2,
"maxLength": 250,
"description": "The person's last name."
},
"age": {
"description": "Age in years",
"type": "integer",
"minimum": 0
}
}
}
How to Implement Data Contracts (Step-by-Step)
Implementing data contracts involves both process and technology. Here’s a mini-guide to introduce them into your data pipeline workflow:
Define the Contract Upfront: Start at the design phase. When an upstream team is providing a new data feed (be it an API endpoint, Kafka topic, or database table), collaborate to write down the expected schema and rules. This could be as simple as a Google Doc or as formal as a JSON Schema or Avro schema file. The key is to capture all expectations: data types, mandatory/optional fields, allowed ranges or enums, update frequency, etc. For instance, if a service emits a dataset of
user_events, specify thatevent_idis a UUID,timestampis ISO8601,event_typeis one of["click", "view", …], etc., and that the feed will be daily at midnight. This document is your contract. Version it in source control if possible.Validate Incoming Data Against the Contract: To enforce the contract, set up validation checks whenever data is ingested. For event streams, you might use Confluent’s Schema Registry to reject invalid messages. For batch files or APIs, use validation libraries like Great Expectations or Pandera. For example, if the contract says “age is non-negative,” your test should fail on negative values. Many orgs integrate these checks directly into ETL/ELT pipelines so issues surface early.
Automate Alerts and Communication: A contract is only useful if breaches are visible. Set up alerting when validation fails. If last night’s batch of
user_eventsincludes a newevent_typevalue not in the contract, that should page the data team. This creates a fast feedback loop. Some teams even dashboard these checks with metrics like Data Contract Compliance: 99.9%.Evolve Contracts with Versioning: Requirements change. Your contract should evolve. Use semantic versioning (v1, v2…) or date-based tags. Let old and new versions coexist temporarily for smoother transitions. Some tools allow simulating what would break if a new version were applied.
🛠️ Companies Leading in Data Contract Infrastructure
As the space matures, several companies and OSS projects are building purpose-built tooling for data contracts:
Gable.ai: A shift-left data platform that lets data producers and consumers collaborate on contract design and enforcement. Founded by the former Head of Data at Convoy.
Data Contract Studio & CLI (by Astrafy): A combined GUI + CLI to define, test, and enforce YAML-based data contracts in Git workflows.
PayPal Open Contract Spec: A YAML-based specification developed by PayPal, now open-sourced for others to customize and adopt in GitOps environments.
Data contracts are as much about culture and collaboration as they are about schemas. To get them right:
Start with critical data pipelines.
Focus on columns and rules that would cause damage if broken.
Educate upstream teams on the “why.”
Don’t over-engineer. Avoid turning this into bureaucracy.
Leverage existing models where possible (e.g. protobufs, dbt sources).
As the Atlan guide puts it, “Data contracts formalize the assumptions that were always there.”
And they don’t exist in a vacuum. They complement your observability stack and are foundational to treating data as a product – where each table has an interface, SLA, and an owner.
Until next time —
Stay reliable, stay accountable.
— nextgendata ✍️