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!
We're wondering:
- 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?
Comment below!