Databricks data engineering execution beats competitors, but product cohesion matters as the platform scales. Candid feedback from a practitioner at DAIS 2024.
Source: Databricks announcement
In Data Engineering Practitioner’s words
“I think the data engineering side is is great I think that the team has the right Vision overall from the data engineering stand point from the AI I think you guys are executing better really than anyone else out there”
“as you guys are adding more features and putting it all together that there's a sense of cohesion in the product especially when you guys have been growing so much lately it's harder to maintain that Unity across the platform”
Chapters
- 0:00 — Practitioner perspective at DAIS 2024
- 0:30 — Feedback on platform announcements and direction
- 1:01 — Data engineering excellence and AI execution
- 1:32 — Product cohesion as the platform scales
- 2:02 — Feedback responsiveness and platform maturity
Frequently asked questions
How does Databricks' data engineering execution compare to competitors?
According to practitioners, Databricks is executing better on data engineering than competitors. The team has the right vision for data engineering and is building on those strengths effectively, which positions it well for the future.
What's the biggest area for improvement as Databricks scales?
Maintaining product cohesion is the key challenge. As Databricks adds more features and the platform grows, it becomes harder to maintain unity across all components. Everything should work together seamlessly.
Does Databricks listen to customer feedback?
Yes. Customers note that the Databricks team takes feedback seriously, even when critical, and actually improves the platform in response. This responsiveness and willingness to iterate is what makes it a healthy and mature platform.
Why do practitioners feel positive about Databricks' future?
A combination of strong data engineering execution, effective AI implementation, a team that's receptive to feedback, and the overall direction of the platform. These factors together create confidence about what's coming next.
Full transcript
Auto-generated from the video and lightly edited for readability. Timestamps link to the moment in the video.
Show full transcript (8 segments)
0:00I don't know man nice to meet you yeah nice to meet you how's it going how do you like it so far so far it's it's been a lot of fun uh been listening to a few people talk if good talks very fluff fre fluff free which I've enjoyed um lots of very nice people over overall from the Databricks team got into meet with some of the product uh team members and honestly a very nice crowd so how so
0:23many what are you looking for like tomorrow any do you expect anything any announcements um truth is I I know some of the stuff that's being announced but uh but overall there's been some some pleasant surprises some some smart some smart decisions I think that are being made and uh I'm just uh honestly uh I'm I'm very excited about what the next year looks like for Databricks so okay
0:47awesome so give give us the like the main thing you like and maybe the main thing you think we can improve at the breaks um uh I think what I enjoy the most is overall I think the data engineering side is great I think that the team has the right Vision overall from the data engineering stand point from the AI I think you guys are executing better
1:10really than anyone else out there others are playing catch up to you so I think honestly as long as your team keeps on building on those strengths and adding more features as well as making sure that everything fits together under the concept of the data intelligence platform I think that's that's a great future ahead uh obviously I think from the data break
1:32standpoint what I on the flip side to that is I just want to make sure that as you guys are adding more features and putting it all together that there's a sense of cohesion in the product especially when you guys have been growing so much lately it's harder to maintain that Unity across the platform and that's a tough challenge to execute not because it's it's just a top challenge
1:54for any organization yeah espe yeah at some scale like I feel like it's really hard to maintain that but it's also like I feel like the real priority when you see int Le everybody's trying to make sure everything works together so yeah hope we make it work yeah and what I've appreciated overall is as some of you may know I I can be a little bit critical in some areas uh I'm sure I'm giving great hair to some of the product
2:16team members but people take the feedback and actually make the platform better which no defensiveness I've made some harsh criticisms or not not harsh but just strong criticisms and the team listens and they want to make things better and they come back they make things better and uh that that's what makes it a very healthy platform and that's I think what that's what really
2:40how prepared data breaks for a healthy future so amazing yeah okay cool well thank you oh no thank you for having me thank you
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Databricks Answers
Looking for a quick, source-backed answer? Browse Databricks Answers on data engineering, Lakeflow & streaming — direct, documentation-validated answers to common Databricks questions, curated by Youssef Mrini, Senior Developer Advocate at Databricks.