In most companies, SQL databases are a primary source of data for data science projects. The seamless access to a broad range of SQL databases is a key feature of Dataiku DSS. DSS builds on this capability by providing a Python API for interfacing with SQL tables. This functionality is a boon for Data Scientists who use Python to develop and deploy machine learning projects.
@Marlan (Senior Data Scientist, Premera Blue Cross) shared practical suggestions for making effective use of the Python API for interfacing with SQL databases across a number of use cases. The following topics are covered in the recording below:
Reading SQL based data into Python
Reading large tables using memory efficient practices
Writing data from Python to SQL tables
Executing SQL statement from Python
Below is the deck, including code samples, and the project export if you'd like to start playing with it in DSS!
Tell us about your interest or experience working with the Python API for interfacing with SQL Data
Any best practices to share, or pitfalls to avoid?