Building Trust in Your Data: Why Data Contracts and Semantic Layers Matter
In today’s data-driven organizations, the challenge isn’t just collecting data—it’s ensuring that data is reliable, consistent, and meaningful across teams. As data architectures grow more distributed and complex, two foundational patterns are emerging as critical: **data contracts** and **semantic layers**. Together, they form the backbone of trustworthy, scalable data infrastructure.
The Silent Killer of Data Trust
We’ve all experienced it: a dashboard breaks because someone upstream changed a schema. A critical ML model fails because data quality degraded without warning. Analytics teams report different numbers for the same metric because they’re querying different tables.
These aren’t edge cases—they’re symptoms of a fundamental problem in how we manage data across distributed teams. As organizations shift from monolithic data warehouses to composable, multi-cloud architectures, the traditional approach of informal agreements and tribal knowledge doesn’t scale.
Enter Data Contracts: Code-Enforced Agreements
A data contract is exactly what it sounds like: a formal, code-enforced agreement between data producers and consumers. Unlike documentation that quickly becomes stale, data contracts are living specifications that define:
Schema structure and data types
Data constraints and validation rules
Service level expectations (freshness, completeness)
Ownership and accountability
Versioning and evolution policies
Why Data Contracts Matter
Think of data contracts as APIs for your data. Just as you wouldn’t build microservices without API contracts, modern data architectures shouldn’t operate without data contracts.
The benefits are tangible:
Prevent Silent Failures: When a producer attempts to change a schema or violate quality constraints, the contract fails loudly—before bad data reaches downstream consumers.
Clear Ownership: Every dataset has an owner responsible for maintaining the contract. No more guessing who to contact when data breaks.
Accelerated Development: Consumers can build with confidence, knowing the data structure and quality guarantees won’t change unexpectedly.
Better Collaboration: Contracts formalize communication between teams, reducing friction and misunderstandings.
Implementing Data Contracts Pragmatically
Here’s the key insight: **not every dataset needs a contract**. Start with high-impact data:
Critical dashboards that executives rely on for decision-making
ML models in production where data drift causes real business impact
Customer-facing analytics where accuracy is non-negotiable
Shared datasets consumed by multiple teams
The implementation approach should be incremental:
Identify critical data pipelines where failure has significant impact
Define contracts using standard formats (YAML, JSON Schema)
Automate enforcement within your data pipelines using validation frameworks
Monitor in production and iterate based on real-world usage
Involve all stakeholders early to ensure contracts reflect actual needs
Modern tools are making this easier. Frameworks like Great Expectations and Soda enable contract validation, while data observability platforms like Monte Carlo can monitor contract compliance and alert on violations.
The Semantic Layer: Your Single Source of Truth
While data contracts ensure reliability at the pipeline level, **semantic layers** solve a different but equally critical problem: **consistency of meaning**.
The Problem: Metric Chaos
In most organizations, the same business metric gets calculated differently across teams:
Finance calculates “Monthly Active Users” one way
Product calculates it slightly differently
Marketing has yet another definition
Each team’s BI tool has its own logic
The result? Endless “reconciliation meetings” where teams argue about whose numbers are correct, eroding trust in data entirely.
What Is a Semantic Layer?
A semantic layer is an abstraction that sits between your raw data and your analytical tools, providing:
Unified metric definitions in code (not just documentation)
Consistent business logic applied regardless of query tool
Access control and governance at the metric level
Performance optimization through intelligent query planning
Instead of each team writing SQL to calculate metrics, they query pre-defined, governed metrics that execute the same business logic every time.
Starburst and Trino: Powering the Modern Semantic Layer
While semantic layers can be implemented through various tools (dbt, Cube, Looker), **Starburst** (the commercial distribution of **Trino**) offers unique advantages for modern data architectures.
Why Trino/Starburst?
Federated Query Capabilities: Unlike warehouse-specific semantic layers, Trino can query across multiple data sources—Snowflake, Databricks, PostgreSQL, S3, even real-time streams—without data movement. Your semantic layer works across your entire data estate, not just one warehouse.
Open Source Foundation: Trino is open source with massive community adoption. Organizations like Apple, Netflix, and Uber rely on it at massive scale. Starburst adds enterprise features while maintaining compatibility with the open ecosystem.
Performance at Scale: Trino’s distributed query engine is built for massive analytical workloads, with intelligent query optimization and caching.
Lakehouse Native: Starburst integrates deeply with Apache Iceberg, Delta Lake, and Hudi—the modern lakehouse table formats that are becoming standard for data infrastructure.
Recent Innovations (2025)
Starburst has made significant moves in 2025 that strengthen its semantic layer story:
Open Semantic Interchange (OSI): In November 2025, Starburst joined forces with Snowflake and other industry leaders to create OSI—an open, vendor-neutral specification for standardizing fragmented data definitions. This means your semantic definitions can work across tools, preventing vendor lock-in.
AI-Ready Data Platform: Starburst’s new platform extends its lakehouse foundation to include multi-agent AI workloads with vector-native capabilities, all accessible through the same semantic layer. As AI becomes core to analytics, having a unified semantic layer for both traditional BI and AI workloads is increasingly critical.
dbt Integration: As of 2025, Trino is officially supported in the dbt Semantic Layer, allowing organizations to define metrics in dbt (using MetricFlow) and query them through Trino’s federated engine. This combination is powerful: dbt for transformation and metric definition, Trino for cross-platform query execution.
Real-World Pattern: The Semantic Layer Architecture
Here’s how forward-thinking organizations are architecting their data platforms:
Data Sources (Multi-Cloud, On-Prem)
↓
Lakehouse Layer (Iceberg/Delta/Hudi)
↓
Data Contracts (Schema + Quality Enforcement)
↓
Transformation Layer (dbt)
↓
Semantic Layer (Trino/Starburst)
↓
Consumption Tools (BI, ML, APIs)
The flow:
Data lands in lakehouse tables with open formats
Data contracts enforce quality and schema at ingestion
dbt transforms raw data into clean, modeled datasets
Semantic layer defines metrics and business logic once
All tools query through the semantic layer for consistency
This architecture enables:
Decoupled storage and compute for cost efficiency
Governance at every layer from ingestion to consumption
Flexibility to add new tools without redefining logic
Performance through intelligent caching and query optimization
Bringing It Together: A Governance Foundation
Data contracts and semantic layers aren’t competing concepts—they’re complementary layers of governance:
Data contracts ensure the reliability and structure of data flowing through pipelines
Semantic layers ensure the consistency and meaning of metrics consumed by humans and applications
Together, they create a foundation of trust that scales with your organization.
For Engineering Leaders
If you’re leading data or engineering teams, here’s what this means practically:
Start with contracts on critical pipelines: Identify your top 5-10 most important datasets and implement contracts there first. You’ll immediately reduce incidents and improve team velocity.
Invest in a semantic layer early: The longer you wait, the more inconsistent metric definitions proliferate. Starburst/Trino offers a modern, open approach that won’t lock you into a single vendor.
Make contracts and metrics code: Just like application code, these should live in version control, go through code review, and be tested in CI/CD. This isn’t “documentation”—it’s infrastructure as code.
Evangelize the “why”: The technical implementation is the easy part. The harder part is getting teams to embrace these patterns. Focus on the benefits: fewer incidents, faster development, consistent metrics, clearer ownership.
Choose open standards: The OSI initiative and open table formats (Iceberg, Delta, Hudi) signal that the industry is moving toward interoperability. Bet on open standards to avoid future lock-in.
The Path Forward
Data infrastructure is evolving rapidly. The monolithic data warehouse is giving way to composable, federated architectures. Batch processing is being supplemented with streaming. AI workloads are becoming first-class citizens alongside traditional analytics.
In this complex landscape, data contracts and semantic layers are not optional—they’re foundational. They represent the maturation of data engineering from ad-hoc pipeline building to principled, governed architecture.
Organizations that adopt these patterns early will move faster, break things less, and trust their data more. Those that don’t will continue fighting fires, reconciling metrics, and wondering why their data initiatives aren’t delivering value.
The tools are ready. The patterns are proven. The question is: when will you start building on this foundation?
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Reference Articles
Data Contracts 101 - Monte Carlo Data
Data Integration: The 2025 Guide - dbt Labs
Starburst Open Semantic Interchange - Learn about OSI and federated query
dbt Semantic Layer Documentation - Official dbt docs
Trino Users - See who’s using Trino at scale