Frequently Asked Questions¶
Which versions of Python does activeviam support?¶
Versions >= 3.7.0
Can I use activeviam on an existing ActivePivot project built in Java?¶
You can use it to execute MDX queries on any running cube and get back a pandas DataFrame but it cannot modify a project built with Java.
Going to production¶
When going to production, do I need to rewrite the project in Java for performance?¶
No since all the computations declared in Python are executed efficiently in Java behind the scene.
Can I run it without the notebook?¶
Yes, notebooks can be converted to executable Python scripts with nbconvert.
Comparison with other tools¶
What is the benefit of activeviam compared to pandas?¶
activeviam stores can handle more data than pandas DataFrame. We have experienced low performance with pandas starting with 4GB datasets while activeviam scales much more efficiently.
Using activeviam, you can build an advanced data model using joins between stores. The data is not duplicated like when you perform a merge in pandas DataFrame.
activeviam has embedded data visualization tools to give you quick insights.
Building scenarios is easy: once your model is defined, it is very easy to compare several versions of your data while in pandas you would have to re-apply all the transformations on each dataset.
Does activeviam replace pandas or spark ?¶
Not really, activeviam is meant to be integrated in an environment with pandas or spark. We think they are very good tools to clean and transform the data while activeviam is made for analysis and visualization. We have actually built connectors to load DataFrames from pandas or spark into activeviam stores.
What is the benefit of activeviam compared to pyplot or matplotlib?¶
No need to write code to define your graph.
The view is not frozen: you can build a dashboard and share it with other users who can still interact with it (for example by adding filters).
What is the benefit of activeviam compared to Tableau or Qlik?¶
Interactivity: you don’t have to export the data and load it into another software. With activeviam, all the measures are defined in Python, adding a new measure or more data is easy.
Performance: when visualizing large datasets, activeviam will still answer very quickly.