Part 1 — Data SLAs — Defining the “What” (Metrics & Measurement)

2026-02-16 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

This article introduces the concept of Data Service Level Agreements (SLAs) specifically for batch data pipelines, aiming to guide first-time implementers. It emphasizes defining the "what" through metrics and measurement, acknowledging that many existing resources explain general SLA definitions. The author intends to provide practical context for implementing data SLAs, focusing on daily data behaviors rather than theoretical explanations or code examples. A key aspect highlighted is the use of a "criticality level" for data pipelines, which can be integrated into the overall SLA policy to inform response and resolution strategies.

Key takeaway

For Data Engineers establishing data governance, defining clear Data SLAs for batch pipelines is crucial. You should start by identifying key metrics and integrating a criticality level for each pipeline. This approach ensures that your SLA policies are practical and directly address the operational realities and business impact of data availability and quality.

Key insights

Implementing data SLAs for batch pipelines requires defining metrics and considering data criticality.

Principles

• SLAs should align with data pipeline criticality.
• Focus on batch pipelines for initial SLA implementation.

In practice

• Define metrics for batch data pipelines.
• Incorporate criticality levels into SLA policies.

Topics

• Data SLAs
• Batch Pipelines
• Data Engineering
• Criticality Level
• Data Metrics

Best for: Data Engineer, MLOps Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.