PinnedKevin Hu, PhDinTowards Data ScienceThe Origins, Purpose, and Practice of Data ObservabilityData observability is an emerging technology that solves an age-old problem: increasing awareness and trust in data systems.Aug 4, 20221Aug 4, 20221
Kevin Hu, PhDinMetaplaneHow to manage tags for objects in SnowflakeYour data is the foundation for all the insights and strategies that drive your business forward. But the more data you collect, the higher…Mar 8Mar 8
Kevin Hu, PhDinMetaplaneThe essential data observability handbook: Proven techniques for modern data teamsThis detailed guide to data observability covers:Mar 3Mar 3
Kevin Hu, PhDinMetaplaneWhat is Data Completeness? Definition, Examples, and Best PracticesIf you care about whether your business succeeds or fails, you should care about data completeness. Data completeness is important because…Mar 2Mar 2
Kevin Hu, PhDinMetaplaneThree Ways to Retrieve Row Counts in Redshift Tables and ViewsAs your data grows in your Amazon Redshift cluster, it’s important to have an accurate count of the number of rows in your tables or views…Mar 2Mar 2
Kevin Hu, PhDinMetaplaneWhat Is Data Accuracy? Definition, Examples, and Best PracticesIf you care about whether your business succeeds or fails, you should care about data accuracy. Data accuracy is important because it has…Mar 2Mar 2
Kevin Hu, PhDinMetaplaneWhat is Data Validity? Definition, Examples, and Best PracticesHow confident are you that the data you’re working with is actually valid?Mar 2Mar 2
Kevin Hu, PhDinMetaplaneWhat is Data Freshness? Definition, Examples, and Best PracticesIf you care about whether your business succeeds or fails, you should care about data freshness. Fresh data is important because it has a…Mar 2Mar 2
Kevin Hu, PhDinMetaplaneStay Fresh: Two Ways to Track Update Times for Snowflake Tables and ViewsEver experienced a delayed dashboard? Been frustrated by late data for that critical report? That’s the sting of stale data, or rather…Mar 2Mar 2