Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines
DOI:
https://doi.org/10.15662/IJRAI.2022.0506029Keywords:
Real-time data validation, Data quality monitoring, Data pipeline observability, Data quality gating, Streaming data validation, Anomaly detection in data streams, Schema enforcement, Data integrity checks, vent-driven data pipelines, Cloud-native data pipelines, Data freshness monitoring, Data drift detection, Automated data quality rules, ETL/ELT pipeline validation, Data reliability engineeringAbstract
Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines describes real-time data quality monitoring within cloud-based data pipelines using a triage and gating approach, and formulates the main objectives and research questions. Real-time data quality monitoring within cloud-based data pipelines is considered a necessary capability to mitigate, detect, and manage data quality issues. Data pipelines ingest, process, and publish streams of data potentially originating from many geographically dispersed sources and targeting multiple upstream and downstream consumers. An effect of these characteristics is that the best data cleaning options are seldom explored in advance and validated for effectiveness and efficiency. Data noise may, therefore, not be adequately controlled or reduced. Quality gate design principles are introduced, and the concept of streaming gatelets is proposed to support the deployment of micro gates able to monitor data streams and control their onward journey in the data pipeline. The method also defines thresholds for measurements, utilizes severity levels to trigger remedial actions, and supports the fast-track and stop-check gating strategies.
Real-time data quality monitoring within cloud-based data pipelines is considered a necessary capability to mitigate, detect, and manage data quality issues. Data pipelines ingest, process, and publish streams of data potentially originating from many geographically dispersed sources and targeting multiple upstream and downstream consumers. An effect of these characteristics is that the best data cleaning options are seldom explored in advance and validated for effectiveness and efficiency. Data noise may, therefore, not be adequately controlled or reduced. Quality gate design principles are introduced, and the concept of streaming gatelets is proposed to support the deployment of micro gates able to monitor data streams and control their onward journey in the data pipeline. The method also defines thresholds for measurements, utilizes severity levels to trigger remedial actions, and supports the fast-track and stop-check gating strategies.
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