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."It's very simple to use Databricks Apache Spark."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"We have the ability to scale, collaborate and do machine learning."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"Easy to use and requires minimal coding and customizations."
"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."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"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."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"The pricing of Databricks could be cheaper."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"Would be helpful to have additional licensing options."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"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."
"The product must be made more user-friendly."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Google Cloud Datalab is ranked 15th 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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