Hello Databricks Community! 👋
As data engineers, we often encounter a unique set of challenges when it comes to ensuring data quality, especially when dealing with silent failures that do not raise immediate alarms but can lead to incorrect reports. These issues can arise from fan-out joins, duplicate keys, stale tables, and more.
Let's engage in an open discussion about your experiences handling these silent failures on Databricks. How do your teams identify and resolve these issues before they impact your data products? What are some common challenges you face, and how have you worked to overcome them?
I’d love to hear your stories, strategies, and any tools or workflows you've found particularly effective. Your insights could be invaluable for those of us looking to enhance our approaches to data quality. Feel free to share your thoughts, challenges, and solutions!
If anyone is open to a brief chat, I'm also conducting informal research on this topic and would appreciate the opportunity to learn from your experiences. I’ll be happy to share back a summary of what I gather from our discussions.
Looking forward to reading your responses and learning from this incredible community!