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Using the Dataiku DSS Python API for Interfacing with SQL Databases - Watch on Demand

CoreyS
Community Manager
Community Manager
1 min read 5 0 675

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:

  1. Tell us about your interest or experience working with the Python API for interfacing with SQL Data
  2.  Any best practices to share, or pitfalls to avoid?

Comment below!