We performed a comparison between Anaconda and Tableau based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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 most valuable feature is the set of libraries that are used to support the functionality that we require."
"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."
"It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science."
"The notebook feature is an improvement over RStudio."
"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 solution is stable."
"From the data science point of view, we use it for model building purposes. For example, if we are using it for a bank and we want to understand how much loan the bank can provide, we can use visualization to show the educational qualification, salary, gender, and city of a customer, and by using this information, we can arrive at the loan amount that this person is eligible for. I can also use it to view all prospective customers, so essentially, this is going to help me in model building as well as in understanding and segmenting customers and doing forecasting and predictive analytics. We use model widgets, and we can create thousands of visualizations, such as motion charts and bubble charts. We can also create animated versions of the graphs and view the data from multiple dimensions. These are the features that we typically use and like."
"Tableau's most valuable features are user-friendliness and have a connection between multiple source systems. You can publish a report by using Tableau Public and there you can make your data online, not only batches of data, you can use it as an online analytical tool."
"Tableau is highly scalable. Now that they've introduced Hyper, you can create an extract of more than 5 million rows in minutes and then do your analysis."
"The solution has a lot of customization when comparing to Microsoft BI."
"I like Tableau's heat maps and the storyboard. You can create data stories and tons of visuals with it, and it goes together really well. Tableau lets you manipulate the data in various ways."
"The maps and colors and interface are all fantastic."
"It's very easy to use and users don't need any IT support to access it as the information is right there."
"The use of a storyboard helps the flow of the data visualisation."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"Anaconda can't handle heavy workloads."
"It also takes up a lot of space."
"I think that the framework can be improved to make it easier for people to discover and use things on their own."
"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."
"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 should be optimized for RAM consumption."
"I would like them to include the Italian language, as I can see there are other foreign language in the product."
"The interface can be improved, in part because there is no indication that something is running or that it's processing."
"Requires a lot of user training."
"When we put more information on a single screen, it gets compressed and superimposed in many places while scrolling."
"Implementation requires a technical background."
"Tableau has so many functions, so sometimes it's hard to find the right solution quickly. I have to search multiple menu bars to find the right command."
"Tableau could be improved by introducing a data manipulation layer within the tool itself. Currently, data manipulations require using additional tools like Alteryx. If Tableau included these capabilities, it would reduce the need for external dependencies. The tool gets slower when we feed huge amounts of data."
"In the next release, there should be more information describing each chart because users have a difficult time telling them apart. They should also include the animations/videos, similar to Power BI."
Anaconda is ranked 13th in Data Science Platforms with 17 reviews while Tableau is ranked 2nd in BI (Business Intelligence) Tools with 293 reviews. Anaconda is rated 8.0, while Tableau is rated 8.4. 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 Tableau writes "Provides fast data access with in-memory extracts, makes it easy to create visualizations, and saves time". Anaconda is most compared with Databricks, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI and Dataiku, whereas Tableau is most compared with Microsoft Power BI, Domo, Amazon QuickSight, SAS Visual Analytics and Databricks.
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.