Data observability and reliability engineering are rapidly emerging as foundational pillars in modern data engineering and MLOps, and ensuring that data pipelines are robust, trustworthy, and capable of supporting critical business operations is imperative.
Data observability is the comprehensive ability to monitor, track, and analyze data as it moves through pipelines, providing real-time insights into data health, quality, and system performance
. It goes beyond traditional monitoring by offering a holistic, proactive approach to identifying and resolving issues before they impact downstream analytics or machine learning models
Data Reliability Engineering focuses on ensuring that data is consistently accurate, available, and dependable over time
. It leverages observability tools and practices to maintain high standards of data quality and system uptime, often borrowing principles from Site Reliability Engineering (SRE) such as Service Level Objectives (SLOs) and error budgets.