@anomalyarmor
data observability
Nothing here yet.
Nothing here yet.
3d ago · 19 min read · Data downtime is the total period during which data is missing, erroneous, or otherwise unfit for use. It is the data equivalent of application downtime: the window between when something breaks and when it is fully resolved. During data downtime, da...
Join discussionApr 12 · 3 min read · It's 9am. You planned to build a new pipeline today. Instead you're debugging why the revenue dashboard shows zeros, tracing a stale table through three upstream dependencies, and explaining to a VP that yesterday's numbers were wrong. By noon you've...
Join discussionApr 12 · 8 min read · You sign up for a data quality tool. You land on an empty dashboard. There's a button that says "Add Connection." You click it, paste your credentials, wait for discovery to finish, and then... nothing obvious to do next. You poke around. Maybe you f...
Join discussionApr 12 · 4 min read · I've done data engineering over the years at CJ, Savings.com, MySpace, Chegg, LinkedIn, Microsoft, One Medical, and AbnormalAI. The thing that's always stuck with me is how the job gets harder in a way that sneaks up on you. When you build a pipeline...
Join discussionApr 12 · 11 min read · The six dimensions of data quality are accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension measures a different aspect of whether data is fit for its intended use. Together they define whether a dataset can be tr...
Join discussion