We performed a comparison between Anaconda and Google Cloud Datalab based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The documentation is excellent and the solution has a very large and active community that supports it."
"It helped us find find the optimal area for where our warehouse should be located."
"The virtual environment is very good."
"The most valuable feature is the set of libraries that are used to support the functionality that we require."
"I can use Anaconda for non-heavy tasks."
"The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors."
"The most advantageous feature is the logic building."
"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"All of the features of this product are quite good."
"When you install Anaconda for the first time, it's really difficult to update it."
"I think that the framework can be improved to make it easier for people to discover and use things on their own."
"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."
"The solution would benefit from offering more automation."
"It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database."
"One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together."
"Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring."
"One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."
"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."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The interface should be more user-friendly."
"The product must be made more user-friendly."
Anaconda is ranked 13th in Data Science Platforms with 15 reviews while Google Cloud Datalab is ranked 14th in Data Science Platforms with 5 reviews. Anaconda is rated 7.8, while Google Cloud Datalab is rated 7.6. The top reviewer of Anaconda writes "Offers free version and is helpful to handle small-scale workloads". On the other hand, the top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Anaconda is most compared with Databricks, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI and IBM SPSS Statistics, whereas Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and KNIME.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms 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.
