Decoupling Systems to Get Closer to the Data
The Real Python Podcast
Exploring Backend Database Selection for Data Analysis
Exploring the implications of selecting DuckDB as a backend database tool, discussing its scalability, functionalities, and ease of use compared to other options for data analysis.
What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.
Phillip contrasts Ibis’s workflow with other Python dataframe libraries. We discuss how “getting close to the data” speeds things up and conserves memory.
He describes the different approaches Ibis provides for querying data and how to select a specific backend. We discuss ways to get started with the library and how to access example data sets to experiment with the platform.
Phillip discovered Ibis while looking for a tool that allowed him to reuse SQL queries written for a specific data platform on a different one. He recounts how he got involved with the Ibis project, sharing his background in open source and learning how to contribute to a first project.
This episode is sponsored by Mailtrap.
Course Spotlight: Creating Web Maps From Your Data With Python Folium
You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country.
Topics:
- 00:00:00 – Introduction
- 00:02:18 – How did you get started with Ibis?
- 00:08:10 – First contribution to open source
- 00:13:46 – Comparing Ibis to other dataframe libraries
- 00:20:09 – Sponsor: Mailtrap
- 00:20:43 – What goes into the selection of backend?
- 00:27:07 – Database connections vs SQL compilers
- 00:30:03 – Raw SQL approach
- 00:34:06 – Dataframe approach
- 00:38:31 – What does “getting close to the data” mean?
- 00:41:52 – Video Course Spotlight
- 00:43:24 – Phillip in the cloud - YouTube channel
- 00:44:56 – Access to sample data sets
- 00:50:11 – Additional resources
- 00:52:50 – What are some of the backends Ibis supports?
- 00:54:13 – Entry points to the platform
- 00:55:00 – How are you supported?
- 00:57:10 – Exporting a SQL query
- 00:59:23 – What are you excited about in the world of Python?
- 01:04:28 – What do you want to learn next?
- 01:07:12 – How can people follow your work online?
- 01:08:00 – Thanks and goodbye
Show Links:
- Ibis - the portable Python dataframe library
- The Leading Designer and Builder of Enterprise Data Systems - Voltron Data
- PEP 249 – Python Database API Specification v2.0
- sqlglot: Python SQL Parser and Transpiler - GitHub
- Ibis – getting_started
- ibis-examples: A repository of runnable examples using ibis
- Ibis – Reference Documentation
- PyScript - Run Python in your HTML
- pixi - Prefix.dev
- uv: An extremely fast Python package installer and resolver, written in Rust
- PyCon US 2024
- LearnCraft Spanish – Fluency for Serious Learners
- ibis: the portable Python dataframe library - GitHub
- Ibis – Blog Posts
- Phillip in the Cloud - YouTube
- Phillip Cloud (@cpcloudy) / X
- cpcloud (Phillip Cloud) · GitHub
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