For the past decade, SQL has reigned king of the data transformation world, and tools like dbt have formed a cornerstone of the modern data stack. Until recently, Python-first alternatives couldn't compete with the scale and performance of modern SQL. Now Ibis can provide the same benefits of SQL execution with a flexible Python dataframe API.

In this talk, you will learn how Ibis supercharges open-source libraries like Kedro, Pandera, and the Boring Semantic Layer and how you can combine these technologies (and a few more) to build and orchestrate scalable data engineering pipelines without sacrificing the comfort (and other advantages) of Python.

We all mix pictures, emojis and text freely in our communications. So, why not in our code? This session takes a whimsical look at what mixing emoji with Python and SQL might look like (spoiler alert: a lot like those "rebus" stories in Highlights Magazine for Kids!). We'll discuss the benefits of doing so, challenges that emoji present, and demo a rudimentary Python preprocessor that intercepts Python and SQL code containing emojis submitted from Jupyter notebooks and translates it back into text-only code using an emoji-to-text dictionary before passing it on to Python for execution. This session is intended for all levels of programmers.

In this hands-on tutorial, you'll go from a blank notebook to a fully orchestrated data pipeline built entirely in Python, all in under 90 minutes. You'll learn how to design and deploy end-to-end data pipelines using familiar notebook environments, using Python for your data loading, data transformations, and insights delivery.

We'll dive into the Ingestion-Tranformation-Delivery (ITD) framework for building data pipelines: ingest raw data from cloud object storage, transform the data using Python DataFrames, and deliver insights via a Streamlit application.

Basic familiarity with Python (and/or SQL) is helpful, but not required. By the end of the session, you'll understand practical data engineering patterns and leave with reusable code templates to help you build, orchestrate, and deploy data pipelines from notebook environments.