We performed a comparison between Databricks and Google Cloud Datalab based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"It's very simple to use Databricks Apache Spark."
"The time travel feature is the solution's most valuable aspect."
"The integration with Python and the notebooks really helps."
"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."
"The initial setup is pretty easy."
"The simplicity of development is the most valuable feature."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"All of the features of this product are quite good."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"It should have more compatible and more advanced visualization and machine learning libraries."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"Databricks' technical support takes a while to respond and could be improved."
"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"The tool should improve its integration with other products."
"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."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"The interface should be more user-friendly."
"The product must be made more user-friendly."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Google Cloud Datalab is ranked 16th in Data Science Platforms with 5 reviews. Databricks is rated 8.2, while Google Cloud Datalab is rated 7.6. 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 Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas Google Cloud Datalab is most compared with IBM SPSS Statistics, Cloudera Data Science Workbench, KNIME, Qlik Sense and Microsoft Azure Machine Learning Studio. See our Databricks vs. Google Cloud Datalab report.
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