This is a VERY good question. I will break it into 2 parts:
Reading and pre-processing:
At that scale, libraries like Pandas are usually not a good bet. A simple pd.read_csv could result in an out-of-memory error. Options are:
Datatables: https://github.com/h2oai/datatable - Can read and process large datasets quickly and efficiently
A million observations of 20 features should be very manageable on a laptop, if a little slow. Cloud computing for very large datasets is staggeringly expensive and offers little or no benefit unless and until you have good parallelization in place. I would recommend keeping that option as your last resort.
For the initial data exploration and ...
There are 2 things you can do here:
1.) Use libraries like Dask to speed up your data preprocessing. Here is the link
2.) Use cloud computing services like Azure, AWS or GCP. I am not aware of other two but I have worked on Azure and it provides a lot of options for implementing a data science solution. You get options like Auto-ML, Azure Designer, Python ML ...
Updated Instructions to install Conda on Google Colab Oct 2021
The process is much simpler with condacolab python library
Import condacolab python library
!pip install -q condacolab
Post kernel restart, check condacolab installation
You can ...