In the wake of the tsunami of big data, it would seem we’re entering the era of the data contract.
Data contracts are formal agreements similar in spirit to service-level agreements (SLAs). They are drafted and enforced to ensure that the data products an organization relies on are of high quality, meeting the needs of both data producers and data consumers. This makes data contracts an increasingly essential aspect of modern data management.
This is why it’s important for data professionals to understand more than just the theory behind data contracts.
The right data contract examples provide context on the difference they make in the real world. This enables more data leaders to advocate for their use.
Schema, semantics, and metadata: 3 key responsibilities of the data contract
“The power of data contracts is that they're designed to unite teams and disciplines across an entire company, while also integrating seamlessly into the individual tools and workflows at all stages of the data lifecycle.” — Data Contracts: Building Production Grade Pipelines at Scale (O’Reilly, 2024)
The heart of every data contract lies in three core responsibilities: schema, semantics, and metadata.
Together, these components ensure that data assets are properly structured, business logic is consistently applied, and metadata maintains data quality and traceability throughout the entire data lifecycle.
1. Schema
Data contracts enforce schema rules that define data types, ensuring consistency and preventing invalid data from entering the system. By using data representation standards like JSON Schema or YAML, data teams can codify the required structure, types, and constraints of a given dataset to ensure data consistency and quality.
For example, imagine a data contract drafted to support a subscription-based fitness service called CryptoCrunch. Let’s say this platform connects fitness tracking devices to user accounts, capturing data like workout performance, energy expenditure, and biometric data.
(For fun, let’s also imagine CryptoCrunch’s user base creates biometric energy by doing intense core-related exercises to offset the fossil fuel energy required for Bitcoin mining. Because why not?)
In this (objectively awesome) example, a JSON Schema could define a contract for a CryptoCrunch database table containing user information, ensuring that every record has a valid email address and a non-empty customer ID. Data contracts can enforce these rules in near real-time during data ingestion. Alternatively, they can enforce data quality rules as part of CI/CD workflows to catch schema violations early in the data pipeline.
Note: YAML is often used alongside JSON Schema because it provides a more human-readable format, making it easier for teams to define and review schema specifications.
2. Semantics
In addition to enforcing schema, data contracts define clear rules for data producers, ensuring that the data they generate meets the expected standards before moving downstream. This includes the business logic that defines how data consumers and other stakeholders should interpret and use data in the specific context of the organization.
For example, in the CryptoCrunch platform, a data contract might define a "workout session" as a set of core-related exercises that generates a certain amount of biometric energy. The business logic could specify that a valid workout session must last at least 30 minutes and generate a minimum amount of energy to qualify for Bitcoin mining offsets.
Semantic rules like these are critical for maintaining consistency, especially when data is transformed or shared across teams, such as customer service, analytics, and data engineering. If CryptoCrunch expands its service to include partnerships with other energy-efficient platforms, these contracts would ensure that everyone downstream is using data that reflects these specific business rules.
By embedding this business logic directly into the data contract, the platform ensures that teams responsible for workout performance analytics or energy expenditure tracking always work with data that aligns with organizational expectations. This reduces the risk of errors or inconsistencies and ensures that the data correctly reflects user activities and energy credits.
3. Metadata
The third key responsibility of data contracts involves managing metadata, which is essential for maintaining consistency and traceability across datasets. Metadata typically includes details such as when the data was created, how it has been processed or transformed since its creation, and who owns or is responsible for it.
This metadata management is a crucial part of data governance, with data contracts ensuring that data quality, consistency, and ownership are enforced across the organization. By codifying these governance rules into contracts, organizations can ensure that data adheres to defined quality standards before those organizations progress through a data pipeline or the data lifecycle.
For example, CryptoCrunch metadata might track important details such as when a user's workout data was generated, how much energy was expended during the session, and whether that energy was successfully converted into Bitcoin mining offsets. Additionally, the metadata would indicate who “owns” the data—whether it’s linked to an individual user’s account or aggregated for system-wide energy consumption reports.
By managing this metadata, the CryptoCrunch data contract would ensure that each user’s workout data adheres to the platform’s quality standards before being processed to generate energy credits or used in performance analytics. This process ensures that any issues are addressed before the data moves further down the CryptoCrunch pipeline, maintaining both data integrity and governance.
Data contract examples: Enforcing quality and consistency
“It’s not a matter of if a data issue will arise, but rather a matter of when, and increased scope leads to an increased probability of issues being highlighted.” — Data Contracts: Building Production Grade Pipelines at Scale (O’Reilly, 2024)
It’s an understatement to say that data contracts are universally beneficial to all data-dependent organizations.
But certain use cases and applications highlight the value of data contracts more definitively than others, like analytics databases, transactional databases, and event streaming.
Example 1: Analytics databases
Analytics databases, such as data warehouses or data lakes, often aggregate immense amounts of data from multiple sources. As these databases grow, maintaining data quality becomes more difficult, especially when new datasets are introduced or existing data sources evolve.
Left unchecked, schema changes that break downstream processes can have chaotic repercussions:
- Corrupted reporting and analytics: When even a seemingly simple schema change is not enforced or caught early, it can lead to errors in reporting, analytics dashboards, and even machine learning models. Imagine a basic yet key field in a database like "total sales" being altered or removed. Dashboards that data consumers rely upon downstream might then display misleading data or fail entirely.
- Increased business decision risks: Analytics databases are commonly used to derive insights that inform business strategy. Therefore, incorrect or incomplete data can hamstring data-driven decision-making. In these instances, stakeholders and executives might base their actions on misleading information—like underestimating product demand or making incorrect financial projections—which could then result in lost revenue, flawed campaign strategies, or reputational damage to the organization itself.
- Longer debugging and fixing cycles: Without data contracts to enforce schema validation, engineers and data scientists can get left on the hook, spending considerable time manually troubleshooting why certain processes are failing. This can bog down (or outright stall) critical business operations and divert resources from more valuable tasks.
On these modern data platforms, however, data contracts enforce quality across multiple data sources, ensuring that downstream analytics remain accurate. Tools like dbt (data build tool) integrate with data contracts to ensure that transformations performed on analytics databases follow schema and business logic rules.
By enforcing schema consistency, changes to a dataset’s structure (e.g., adding new columns or renaming an existing column) cannot break mission-critical downstream analytics processes like dashboards or machine learning models.
This does more than keep organizational data pipelines running smoothly. Data contracts ensure that all insights and reports derived from upstream databases are, and will remain, accurate.
Example 2: Transactional databases
Transactional databases—think those used in ecommerce or customer management systems (CMS)—deliver value by capturing and processing real-time operational data. When taken as a whole, this immense amount of data, order information, payment information, and customer profiles are often leveraged in the aggregate to become a single source of truth for core business functions.
As such, this data must remain exceptionally consistent over time for business models to improve. Data inconsistencies, then, create issues that naturally eat into this “truth” that business-to-business (B2B) and business-to-consumer (B2C) companies must rely on:
- Billing errors and financial loss: At the most basic level, severe financial issues can occur if the data used for transactions is inconsistent or inaccurate. Take a business shifting from a B2C to B2B model. If its data schema is not updated to handle corporate accounts, invoices could then be sent to incorrect entities or with the wrong amounts, resulting in significant revenue loss or legal disputes.
- Operational disruptions: Since transactional databases often serve as the backbone for retail businesses, business logic needs to be enforced properly. When it isn’t, this can disrupt operations as a whole—from inventory and order fulfillment to customer management. When this happens, business operations may halt completely, leading to costly delays.
- Customer relationship damage: There’s often more than money on the line, as inaccurate customer data can result in poor service or communication. Imagine, due to operational disruptions, orders are delayed or delivered to incorrect addresses. Then customer service representatives who are given outdated or incorrect information are expected to resolve these issues. It becomes easy to see how negative reviews and customer churn would quickly impact an organization’s brand, not just its bottom line.
That said, changes of this sort are exceptionally common in B2B and B2C organizations, as businesses naturally evolve and expand. The data contract is what keeps business growth on track, enforcing business logic and ensuring data remains consistent with a given business's needs.
By defining clear rules, data contracts help manage dependencies between various data assets, ensuring that changes in one area do not disrupt downstream processes. Additionally, in this example, contracts also ensure ACID (Atomicity, Consistency, Isolation, Durability) compliance, maintaining data consistency and accuracy as business models evolve, while minimizing the risk of data corruption in critical systems.
Example 3: Event streaming
Event-driven systems handle real-time data and can involve complex interactions, such as user activity tracking, IoT device data, or financial transactions.
As these systems mature and gain further adoption, and the subsequent volume and velocity of data increase proportionally, ensuring data consistency becomes a significant challenge, especially in distributed systems.
This is what makes event-driven systems particularly vulnerable to real-time data inconsistencies—race conditions, event duplication, etc. And, unfortunately, it doesn’t take a big inconsistency to cause major problems in the moment:
- Data integrity breakdown: As mentioned, inconsistencies can lead to corrupted data in real-time systems, especially those relying on event-driven architectures (that is, systems where the outcome of an event depends on the sequence or timing of events that occur simultaneously or close together). This could be as simple as the processing of a cancellation order that hasn’t yet been placed. It’s a hiccup, but one that could leave a system in an incorrect state, or one that leads to incorrect information downstream, further undermining data integrity.
- Operational latency and failures: Event streaming often requires low-latency processing to handle high volumes of data in real time. Without data contracts enforcing data quality and structure, invalid or malformed events can slow down processing pipelines, cause failures in real-time applications, and lead to delayed responses. In industries like finance or telecommunications, these delays could lead to missed opportunities or regulatory violations.
- Critical system outages: If event streaming systems break down due to inconsistent or corrupt data, it can lead to larger system outages. For instance, a streaming service that relies on real-time telemetry data for monitoring system health could be blind to performance issues, resulting in prolonged downtime or service degradation that affects customers and revenue.
In event streaming, data contracts enforce structure and data quality rules when APIs transfer real-time data across systems. By doing so, the contract ensures that event information (such as user updates or financial transactions) remains valid and consistent, regardless of how many times it is processed.
While the streaming infrastructure handles event sequencing, data contracts prevent issues like corrupt data or out-of-schema events from being processed, preserving data quality and reliability in distributed environments.
From examples to education: Working together to draft a better way forward
“What makes data contracts powerful is what also makes them difficult to implement. The power of data contracts is that they're designed to unite teams and disciplines across an entire company, while also integrating seamlessly into the individual tools and workflows at all stages of the data lifecycle.” — Data Contracts: Building Production Grade Pipelines at Scale (O’Reilly, 2024)
On its own, this quote encapsulates the inherent challenge of data contracts. It does, however, also tip a hat toward the solution: education and advocacy.
Data contracts are indeed powerful. In addition to the practical examples in this article, they also have the power to unify various teams and disciplines within an organization around common data standards and expectations.
But this exceptional integrativeness can be hampered by challenges with data contract implementation and support—let alone buy-in from organizational leadership and stakeholders, each with competing priorities and knowledge bases.
So, alignment is key for unleashing the benefits that data contracts can (and should) provide for data teams worldwide. Which brings us back to the quote, and those featured throughout the article. Truth be told, the book they’re all from—Data Contracts: Building Production Grade Pipelines at Scale—is, actually, the very first book from Gable.ai.
Written by our own Chad Sanderson and Mark Freeman (and published by our awesome friends at O’Reilly), the book is our sincere attempt to help spark more education and advocacy amongst our data engineering peers. We’d love for you to grab your own free copy, as soon as it’s available. Especially if you believe in the power data contracts can and do have in modern organizations, but maybe sometimes struggle to articulate exactly how.
That said, we’re still a few weeks away from our publishing date. But don’t panic. You can already download the first few chapters to get an edge on your co-workers downstream.
Enjoy! We can’t wait to hear what you think.
