In a world increasingly embracing Python, plug-and-play solutions and AI-generated code, our generation growing up with these advancements may not fully grasp the challenges faced by our predecessors. Meanwhile, data engineering, traditionally known for its complexity, can now transition into the plug-and-play realm too, thanks to Python libraries such as dlt.

Aimed to be both fun and insightful, this talk will educate the listener on the concepts of data engineering our generation finds most important and enable them to use high level abstractions to automate most of what used to be highly manual work. The juniors will gain an appreciation for the difficulties in data pipeline engineering, the seniors - a straightforward solution to expedite the creation of robust pipelines.

From the perspective of junior data engineers such as us, the talk will walk through the challenges associated with constructing a data pipeline and demonstrate how these can be effectively addressed using Python libraries such as dlt that simplify the intricacies of data extraction, transformation, and loading.


Affiliation: dltHub

Writer by choice and a data enthusiast at heart. Crafting compelling narratives with Open Source Software at dltHub. With a background in International Relations, I am currently pursuing Computer Science, focusing on Machine Learning, at TU Berlin.

visit the speaker at: GithubHomepage

Hiba Jamal

Affiliation: dltHub

The data field has been my home for 3 years. I'm now a Data Science Working Student at dltHub in Berlin. Previously, I contributed as a researcher, data scientist and business analyst in startups and government-funded projects in Pakistan. Currently pursuing a master's degree in data analytics and AI for business management, I hold a prior degree in Computer Science with a touch of liberal arts.

visit the speaker at: Github