SLAs, SLOs, SLIs
Definitions of producers and consumers in data pipelines 👨💻👩💻
Producer: The party responsible for creating, transforming, and delivering a data asset. They serve as the data source and are accountable for its quality and delivery. In a data pipeline context, the producer is the team or system that ingests raw data, cleans it, and makes it available for others.
Consumers: The party who uses or relies on the data asset for their own purposes, such as analysis, reporting, or powering an application. The consumer depends on the producer's data to be of a certain quality to fulfill their business needs.
Note: The same team or system can act as both producer and consumer for different data assets, and roles can shift as data flows through multi-stage pipelines. The importance lies in treating your data as a product which directly impacts business outcomes.
The relationship between them is one of supply and demand, but it operates as a collaborative partnership rather than a simple transactional exchange. The producer supplies data, and the consumer demands it, but both parties share responsibility for the success of the data relationship.
Information bias between producers and consumers
Information asymmetry between producers and consumers creates a situation where each party has critical knowledge the other lacks. Consumers have deep understanding of business requirements and quality thresholds that matter for their use cases, while producers have technical expertise about system constraints and processing capabilities. Even though producers aim to deliver high-quality data, consumers are typically better positioned to define what 'quality' means in their specific context and must communicate these requirements clearly. Conversely, producers need to transparently communicate their technical limitations and capabilities. This mutual knowledge gap necessitates a shared responsibility paradigm where both stakeholders actively collaborate to bridge information asymmetries.
What are SLAs (Service-Level Agreements)?
A SLA functions as a formal agreement between the producer and consumer, defining the expected performance standards and quality benchmarks for a data asset. In practice, an SLA establishes mutual commitments where the producer agrees to meet specific quality thresholds while the consumer agrees to reasonable usage patterns and feedback mechanisms.
Clarity of Expectations: It creates explicit understanding of performance targets, eliminating ambiguity about what constitutes acceptable data delivery and quality levels.
Accountability Framework: The formal agreement establishes clear ownership boundaries and response procedures, ensuring both parties understand their roles when issues arise and how problems will be resolved.
Operational Confidence: It builds consumer confidence in the data pipeline's reliability, reducing the need for extensive downstream validation while still maintaining appropriate quality checks as a safety net.
What are SLOs and SLIs (Service-Level Objectives and Indicators)?
An SLO (Service Level Objective) is a specific, measurable target for a service's performance. An SLI (Service Level Indicator) is the metric used to measure that performance. SLIs are the raw measurements, while SLOs are the goals you set for those measurements.
Data Quality is the business goal that drives everything. To achieve it, teams create SLAs that define the business relationship and consequences. These SLAs are built on specific SLOs that set measurable performance targets. Finally, SLIs provide the actual measurements needed to evaluate whether SLOs are being met.
Think of it as: Business Goal → Business Contract → Performance Targets → Actual Measurements
Vertical SLAs and transversal SLOs
There is also a notion of vertical SLAs and transversal SLOs.
Vertical SLAs ⬇️ = Individual contracts between one producer and one consumer (like separate rental agreements for each tenant in a building)
Transversal SLOs ↔️ = One set of operational standards that cuts across all contracts (like the building's overall maintenance standards that satisfy all tenants)
Example: Let’s say a producer serves data to the Marketing, Finance, and Customer Service teams.
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