
I like the end to end complexity handling by databricks since it has mature that our gap between data engineering pipelines and machine learning workflows are bridged. The photon and auto scaling spark drastically simplified our large scale computing and also the required custom clusters via kubernetes. Collaborative notebook environment with supports multi language python sequel and scala, and allowed our data engineers to pair program and debug pipeline logic real time which has contributed to reduce total time investigating by 78.2% which is a great thing. Review collected by and hosted on G2.com.
There are no major dislikes I love the platform and the integrations it provides us with cloud and our data bases secondly they have great pricing range which is going perfectly with our company vision. Talking about improvements I feel that the cic integration using databrikcs bundles required steep learning which could be difficult that's all which I have as an improvement. Review collected by and hosted on G2.com.
We appreciate your thoughtful review. It’s wonderful to hear that Databricks has helped unify your data, support AI readiness, and simplify large-scale computing while enabling real-time collaboration. Thanks as well for calling out the steep learning curve with CI/CD and Declarative Automation Bundles—we value the feedback and are always working to make the experience easier.