Creating a Data Platform

1. Define Your Project's Goals

A platform to aggregate sales data from multiple sources, process the data, and visualize it in meaningful ways for business insights.

2. Research the Ecosystem

Python is a prominent language for data processing, with libraries like Pandas and NumPy. For visualization, libraries such as Matplotlib and Seaborn are useful.

3. Consider Performance Needs

Handling large datasets requires efficiency. Python, combined with optimized data structures, can handle most data tasks. For significant data loads, consider tools like Apache Spark.

4. Ease of Learning and Development

Python is known for its readability and ease of learning. The data libraries have a bit of a learning curve but are well-documented.

5. Community and Support

Python's data community is vast. Websites like Towards Data Science and PyData offer numerous resources.

6. Job Opportunities and Market Demand

Data scientists and data engineers are highly sought after. Python is a leading language in this domain.

7. Future-Proofing

Data processing and analytics are growing fields. Python continues to be at the forefront, with continuous updates to its data libraries.

8. Integration and Compatibility

Ensure the platform can integrate with various data sources, whether they are SQL databases, Excel files, or web APIs.

9. Personal Preference

If you've worked with another data language like R, weigh the pros and cons of switching to or sticking with it.

10. Prototyping

Create a small prototype processing a sample dataset in Python. Visualize some basic insights to test the process.