Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain and iterate on 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 Liu 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 bit easier, such as the ability to trace data lineage and designing for immutable data.
Jiaqi Liu is a Lead Software Engineer at the Center for Translational Data Science at the University of Chicago. Previously she was Tech Lead at Button and Data Scientist at Capital One Labs. She is passionate about bridging the gap between the science and engineering part of data-driven work and champions inclusivity in the workplace, and advocates for a culture where everyone’s input matters. She is also active in the Write/Speak/Code and Women Who Code communities.he’s a director at Women Who Code NYC.
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