We performed a comparison between Anaconda and Databricks based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."I can use Anaconda for non-heavy tasks."
"The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results."
"The documentation is excellent and the solution has a very large and active community that supports it."
"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."
"The notebook feature is an improvement over RStudio."
"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."
"The virtual environment is very good."
"The solution is stable."
"We have the ability to scale, collaborate and do machine learning."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"The technical support is good."
"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."
"The most valuable feature is the ability to use SQL directly with Databricks."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"The simplicity of development is the most valuable feature."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"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."
"The solution would benefit from offering more automation."
"I think that the framework can be improved to make it easier for people to discover and use things on their own."
"Anaconda should be optimized for RAM consumption."
"Anaconda can't handle heavy workloads."
"When you install Anaconda for the first time, it's really difficult to update it."
"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."
"Pricing is one of the things that could be improved."
"The Databricks cluster can be improved."
"Databricks can improve by making the documentation better."
"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."
"It would be great if Databricks could integrate all the cloud platforms."
"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."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."
Anaconda is ranked 18th in Data Science Platforms with 1 review while Databricks is ranked 1st in Data Science Platforms with 47 reviews. Anaconda is rated 7.8, while Databricks is rated 8.2. 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 Databricks writes "Ahead of the competition in building data ecosystems, but needs to improve ease-of-use". Anaconda is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI, IBM SPSS Statistics and Domino Data Science Platform, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Azure Stream Analytics.
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.