We performed a comparison between Cloudera DataFlow and Databricks based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The initial setup was not so difficult"
"The most effective features are data management and analytics."
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"The solution is very simple and stable."
"We can scale the product."
"Databricks helps crunch petabytes of data in a very short period of time."
"It's very simple to use Databricks Apache Spark."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"It's not easy to use, and they need a better UI."
"Databricks has a lack of debuggers, and it would be good to see more components."
"Costs can quickly add up if you don't plan for it."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
Cloudera DataFlow is ranked 13th in Streaming Analytics with 4 reviews while Databricks is ranked 2nd in Streaming Analytics with 78 reviews. Cloudera DataFlow is rated 7.2, while Databricks is rated 8.2. The top reviewer of Cloudera DataFlow writes "Has good data management and analytics features". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Cloudera DataFlow is most compared with Confluent, Amazon MSK, Hortonworks Data Platform, Informatica Data Engineering Streaming and Spring Cloud Data Flow, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio. See our Cloudera DataFlow vs. Databricks report.
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