In modern analytics environments, where data pipelines span multiple sources, transformations, and destinations, ensuring continuous data quality has become a critical operational priority. This systematic review investigates recent advances in end-to-end pipeline observability as a key strategy for maintaining data quality assurance across complex analytics systems. Following PRISMA methodology, we analyzed peer-reviewed articles, industry whitepapers, and technical case studies published between 2015 and 2024 to synthesize emerging practices, technologies, and challenges. Our findings reveal that traditional monitoring techniques are inadequate for today’s distributed, multi-cloud, and real-time analytics pipelines. Modern pipeline observability frameworks extend beyond basic uptime metrics, incorporating multi-layered visibility across ingestion, transformation, storage, and consumption stages. Innovations include metadata-driven anomaly detection, real-time data drift monitoring, lineage tracking, schema evolution alerts, and SLA-based data quality checks. Tools such as Monte Carlo, Databand, Soda, and OpenLineage are leading a new generation of observability platforms that offer comprehensive insights into data freshness, completeness, accuracy, and trustworthiness. Integrating observability natively into orchestration systems (e.g., Airflow, Dagster) and embedding data reliability SLAs into business operations have become best practices for proactive quality management. Despite these advancements, challenges persist, including scalability in high-velocity environments, managing observability across heterogeneous ecosystems, and balancing automation with human oversight. Furthermore, standardization of observability metrics and the integration of ethical frameworks for responsible data monitoring remain underdeveloped. This review concludes by proposing future research directions, including the development of autonomous, self-healing observability systems, cross-cloud observability standards, and frameworks for measuring the business impact of data quality issues. As analytics systems grow increasingly complex, mastering end-to-end pipeline observability will be vital for building resilient, trustworthy, and high-performing data infrastructures.
Bukhari et al. (Fri,) studied this question.