I bring campaign data from social platforms, client websites and online stores into one workspace, so I don’t have to rebuild the weekly attribution view across separate tools, and my figures match the account managers’ figures. Since we share a workspace, our web team usually catches broken tags before they distort a report. I value having analytics and machine learning together: I can write SQL, test a Python model and share the result with colleagues in the same notebook. Setup was simple, and our team was sharing notebooks soon after the first login. I also value the cluster settings, which let me size compute for a heavy year of ad impressions and keep the weekly attribution work manageable. Review collected by and hosted on G2.com.

Speed is the weak point for me. When I process a full year of ad impressions across several client accounts, queries slow down noticeably. I filter data early and right-size my clusters, which softens the problem, but I’d also like Databricks to suggest cluster sizes and flag slow queries automatically so I can fix slowdowns without trial and error. Review collected by and hosted on G2.com.