We performed a comparison between Databricks and Informatica Data Engineering Streaming based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Databricks, Microsoft and others in Streaming Analytics."Databricks has helped us have a good presence in data."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"The solution is very simple and stable."
"I like cloud scalability and data access for any type of user."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Databricks integrates well with other solutions."
"It improves the performance."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"Implementation of Databricks is still very code heavy."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"Pricing is one of the things that could be improved."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"Skill requirement is required. There is a learning curve."
Earn 20 points
Databricks is ranked 2nd in Streaming Analytics with 78 reviews while Informatica Data Engineering Streaming is ranked 15th in Streaming Analytics with 1 review. Databricks is rated 8.2, while Informatica Data Engineering Streaming is rated 8.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Informatica Data Engineering Streaming writes "Helps with real-time data processing and improves decision-making overall". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas Informatica Data Engineering Streaming is most compared with Google Cloud Dataflow, Apache Flink, Starburst Enterprise, Mule Anypoint Platform and IBM InfoSphere DataStage.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.