Jul 3, 2021
5 reasons why you should use TM1py
As an IBM Planning Analytics (TM1) developer, you probably already got asked one of these questions:
How do I connect my could system (Salesforce, Netsuite…) with TM1?
How do I integrate AI into my planning solutions?
How can I automate some tedious tasks?
The answer is TM1py! In this article, we cover the top 5 reasons why you should use it.
1. Benefit from the unique Python ecosystem
No other platform combines ease of use with such vivid open-source ecosystems.
The Python package index currently lists 300’000+ packages at your disposal.
Among those, you will find top-notch packages for Machine Learning, Data, GUIs, plotting, Excel, SQL, web development, IoT, you name it!
Python is almost like a buffet of awesome features. You choose the ones that help you solve your specific business problem and enhance your TM1 application.
2. Express business logic in new efficient ways with Python
TM1py enables TM1 developers to swift TM1 developments with the Python language.
Larger or more complex TM1 models benefit from using TM1py as an extension of the native Turbo Integrator.
Python allows expressing business logic highly efficiently, using its built-in features and extensions.
Measures like NPV or IRR are calculated with one line:
Data structures like lists, sets, dictionaries and modern development techniques like OOP or test-driven development, are optional features in this “Python Buffet”.
Exploiting these innovations contributes to quality TM1 developments.
3. Multi Instance Developments
A TM1py script does not run within the scope of a TM1 instance! It is therefore not more complex to interact with many TM1 instances than it is to interact with 1 instance from a script.
Python can act as the glue between the instances when we are dealing with multiple TM1 instances in an organization. Thus, solving for instance the common problem, that while there are multiple TM1 instances, there must be only one Cost Centre structure and CoA dimension.
In fact, it is surprisingly simple to synchronize a subset or a view or even a hierarchy between two TM1 instances.
4. New Data Integration capabilities for the API-to-API world
With TM1py, we are no longer limited in terms of the data sources we can connect to. Every data source that can be accessed with Python, can be turned into a data source for TM1.
TM1py allows building data pipelines of arbitrary complexity around TM1. TM1py and python provide the solid bedrock for these integration developments.
Scheduled exports (or imports!) to Google Sheets, Microsoft SharePoint, or your Azure Data Lake are just simple illustrations of this exciting new API-to-API landscape that TM1 is becoming a part of.
Thanks to the available packages for python we often end up with very concise and maintainable code.
Python is also incredibly good at arranging and cleansing data thanks to packages like pandas.
Thanks to TM1py, TM1 implementations can now benefit from all these traits.
5. Leverage the collective intelligence of the TM1(py) Community
TM1py is a TM1 community project. Built and maintained by a group of 20+ expert TM1 developers from different regions and companies, with one shared goal: make it as easy as possible to create value with TM1 and python.
TM1py bundles this collective intelligence into a vivid open-source project that everyone in the TM1 community can benefit from. By using TM1py you are saving yourself from writing thousands of lines of code, that are already written, tested, and maintained by the TM1py community for you.
