Data Contract Tools That End the Producer-Consumer Blame Game.

Schema is one piece - necessary but not sufficient. Logiciel formalizes three contract dimensions: schema (Avro/Protobuf/JSON with compatibility modes), SLA (freshness, latency, availability commitments), and quality (row-level guarantees, distribution constraints, business rules). All three are versioned in Git, code-reviewed in PRs, and enforced at runtime. A schema registry alone catches breaking type changes; full data contracts catch the broader producer-consumer reliability problem. For US teams scaling beyond ad-hoc producer-consumer agreements (typically the 30+ data engineer threshold), pure schema registry approaches leak the failure modes Logiciel was designed to prevent. Confluent Schema Registry compatibility means existing schemas migrate without rework.

We support dbt contracts (model contracts, column-level constraints) and extend them with runtime enforcement, multi-language coverage, and cross-team workflows. dbt contracts are excellent within the dbt ecosystem - they catch breaking changes at compile time and enforce constraints at run time. But dbt contracts only cover dbt models; producer-consumer relationships outside dbt (Kafka topics, streaming feature pipelines, reverse-ETL flows, ML training data) need broader enforcement. Logiciel provides contracts across the full data stack, with dbt contracts as one well-supported pattern. Most customers running dbt contracts adopt Logiciel to extend the practice beyond dbt boundaries.

Yes - inferred contracts can be a starting point, then tighten progressively. Logiciel auto-infers schema, observed SLAs (P50/P95/P99 freshness from history), and quality patterns (typical distributions, null rates) from existing pipelines, generating draft contracts that humans then review and ratify. Tightening over time: start with monitoring-only enforcement (alerts on violations), move to soft enforcement (warnings in CI), then strict enforcement (blocks in CI, quarantines in production) as the producer-consumer relationship matures. Most customers adopt contracts on the 5-10 most cross-team-critical datasets first, then expand. Mature contract programs typically cover 50-100 critical datasets across the org.

Configurable per contract - block in CI, quarantine downstream, alert consumers, or version the contract. Block-in-CI is most common for high-stakes datasets (financial reporting, billing, customer-facing): the producer's CI pipeline fails on incompatible schema changes, forcing a deliberate decision rather than an accidental break. Quarantine-downstream is useful for less-critical changes: the new data sits in quarantine until consumers approve. Alert-consumers is a softer pattern for development phases. Versioning supports planned breaking changes - multiple contract versions live in parallel during migration windows. The granular control means contracts support your most sensitive use cases without over-constraining experimentation.

Programmatic API plus dashboard. Consumer teams integrate with the contract API to receive contract metadata (current version, SLA commitments, quality expectations) at build time and runtime. Many tools (BI, ML platforms, application code generators) consume contracts automatically - for example, BI tools generate validated SQL from contract metadata; ML feature stores enforce contract-defined SLAs on training data; application code generators produce type-safe consumers from Avro schemas. The contract API is the single source of truth; consumers don't depend on side-channel documentation. For internal portal use cases, we provide a contract browser UI with search, lineage, and ownership.

Per contract tier - predictable at scale, with unlimited contract consumers and producers. Mid-market customers (50-200 active contracts) typically pay $30-70K ARR for the contract platform as part of broader data infrastructure tier. Enterprise tiers (500+ contracts, advanced workflows, dedicated TAM, US-citizen support) start at $150K ARR. Pricing is transparent with workload-grounded TCO comparisons available at evaluation. Compared to building contracts infrastructure in-house (typically a 3-5 engineer-year investment plus ongoing maintenance), the platform pays back quickly. Contract programs are most valuable at the >30 data engineer threshold; below that, lighter-weight schema registry approaches often suffice.

The 5-10 most cross-team-critical datasets - typically the ones that have caused the most production incidents from producer-consumer drift, or the ones that show up in financial reporting, billing, customer-facing analytics, or regulatory submissions. Start with monitoring-only enforcement (alerts on contract violations) for 30 days to baseline the failure modes. Then move to soft enforcement in CI (warnings) for another 30 days. Then strict enforcement (CI blocks, runtime quarantines) once the producer team has internalized the discipline. Most customers expand from this initial 5-10 to 30-100 contracted datasets over 12-18 months as confidence builds and the value compounds.