Case Study: Good Data at Good Eggs - Data Infrastructure Correctness and Reliability

This case study from Good Eggs highlights the importance of data quality and reliability in a data-driven organization. The company uses Dagster, a data orchestration platform, to manage its data ingest process. By implementing custom data frame types, they are able to run automated type checks on their data, which guarantees invariants such as non-null values and categorical data types. This approach ensures that bad data is caught quickly, reducing the risk of polluting downstream data artifacts. The use of structured metadata also provides benefits for human analysts, enabling them to diagnose data quality problems more efficiently. Overall, Dagster's support for custom data types and structured metadata has improved the reliability and efficiency of Good Eggs' data ingest process, reducing latency from days to under an hour.

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