[QA.DT.5] Utilize incremental metrics computation
Category: OPTIONAL
Incremental metrics computation allows teams to efficiently monitor and maintain data quality without needing to recompute metrics on the entire dataset every time data is updated. Use this method to significantly reduce computational resources and time spent on data quality testing, allowing for more agile and responsive data management practices.
Start by identifying the specific data quality metrics that
are essential for your system. This could include metrics
related to accuracy, completeness, timeliness, and
consistency. Depending on your dataset's size and complexity,
select a tool or framework that supports incremental
computation. Some modern data processing tools, such
as Apache
Spark
Segment your data into logical partitions, often based on time, such as daily or hourly partitions. As new data is added, it becomes a new partition. Automate the computation process by setting up triggers that initiate the metric computation whenever new data is added or an existing partition is updated.
Continuously monitor the updated metrics to help ensure they reflect the true state of your data. Periodically validate the results of the incremental metrics computation against a full computation to ensure accuracy. As you get more familiar with the process, look for ways to optimize the computation to save even more on computational resources. This could involve refining your partitions or improving the computation logic.
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