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WanjohiChristopher
WanjohiChristopher

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Data Quality Checks

Dataquality checks you need to keep in mind while validating your data, ensuring it is reliable, and can be trusted before reporting or performing analysis:

  1. Data Completeness - check whether the data has all required data fields and check missing values.
  2. Data Consistency - make sure data is uniform across different sources
  3. Data Accuracy - compare the correctness of the data with already known or expected values.
  4. Data Timelines - check whether the data is up-to-date within expected timelines.
  5. Data Relevance - is the data relevant to the business or its requirements and does it meet its purpose.
  6. Data Integrity - is the data logically consistent and adhering to the defined business rules in place. #data #dataengineering #dataanalytics #dataintegrity #datascience #dataengineers #datascientists #dataanalysts

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API Trace View

Struggling with slow API calls?

Dan Mindru walks through how he used Sentry's new Trace View feature to shave off 22.3 seconds from an API call.

Get a practical walkthrough of how to identify bottlenecks, split tasks into multiple parallel tasks, identify slow AI model calls, and more.

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