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."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 valuable feature is the set of libraries that are used to support the functionality that we require."
"The most advantageous feature is the logic building."
"I can use Anaconda for non-heavy tasks."
"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."
"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 solution is stable."
"It helped us find find the optimal area for where our warehouse should be located."
"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"The solution is very easy to use."
"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."
"Its lightweight and fast processing are valuable."
"The initial setup is pretty easy."
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"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."
"Anaconda can't handle heavy workloads."
"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."
"The solution would benefit from offering more automation."
"Having a small guide or video on the tool would help learn how to use it and what the features are."
"Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring."
"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."
"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."
"I would like it if Databricks made it easier to set up a project."
"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."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"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."
"There are no direct connectors — they are very limited."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
Anaconda is ranked 13th in Data Science Platforms with 15 reviews while Databricks is ranked 1st in Data Science Platforms with 78 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 "A nice interface with good features for turning off clusters to save on computing". Anaconda is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI, IBM SPSS Statistics and IBM Watson Studio, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio.
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