Observability for Data Pipelines: Monitoring, Alerting & Tracing Lineage
Jiaqi Liu at GOTO Chicago 2020
Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain even after they are initially built and validated. To truly expand and develop a code base, developers must be able to test confidently during the development process and monitor the production system. Monitoring and testing data pipelines or real-time streaming processes can be very different from monitoring web services.
Jiaqi draws on her experience building and maintaining both batch and real-time stream data pipelines to discuss how to leverage monitoring tools like Prometheus and Grafana to define and visualize metrics, how and when to alert on common health indicators, and how to gain visibility in monitoring not just the system health but the health of the data. General concepts she touches on include observability of pipeline health, interpretability of data results and building features into data pipelines that makes monitoring and testing just a little ...
Jiaqi Liu - Leader in Bridging the Gap between Data Science and Engineering
Jiaqi draws on her experience building and maintaining both batch and real-time stream data pipelines to discuss how to leverage monitoring tools like Prometheus and Grafana to define and visualize metrics, how and when to alert on common health indicators, and how to gain visibility in monitoring not just the system health but the health of the data. General concepts she touches on include observability of pipeline health, interpretability of data results and building features into data pipelines that makes monitoring and testing just a little ...
Jiaqi Liu - Leader in Bridging the Gap between Data Science and Engineering