The Dataquest Download
Level up your data and AI skills, one newsletter at a time.
Hello, Dataquesters!
Here’s what we have for you in this edition:
Top Read: Build a complete food ordering app from scratch and learn how core Python concepts come together in a real interactive project. Learn more
From the Community: A standout food ordering app project, practical advice on using AI to build real-world projects, and a smart discussion on row-wise vs. vector-based parsing. Join the discussion
What We’re Reading: How to choose between batch and streaming systems, why smarter questions make better AI agents, why agentic AI is becoming a hiring signal, and why data engineering fundamentals still matter. Learn more
Top Read
Learning Python syntax is one thing. Using functions, loops, dictionaries, and user input together to build a working application is where real understanding starts to develop.
In this hands-on project, you’ll build a complete food ordering app from scratch using core Python fundamentals. Along the way, you’ll learn how to structure a program, create reusable functions, manage application flow, and turn individual coding concepts into a real, interactive application. If you’re ready to move beyond exercises and start building projects, this is a great next step.
From the Community
Building a Food Ordering App: Ashutosh’s project stands out for its extensive use of well-documented functions, making the code easy to understand, maintain, and debug. A simple yet impressive project that serves as a role model for aspiring developers and data learners who are just starting out with app development.
Using AI for Building Real-World Projects: Drawing from his trial-and-error experience, Alberto emphasizes the importance of putting AI skills into practice by building real-world projects and creating meaningful solutions, rather than focusing solely on theory.
Vector-Based vs. Row-Wise Parsing: Mamta highlights the advantages of row-wise web scraping over a vector-based approach, making data extraction more reliable and easier to maintain, especially when fields of interest are missing and vectors become misaligned.
What We're Reading
Batch or Stream? The Eternal Data Processing Dilemma: Real-time processing sounds great, but it isn’t always the right answer. This article breaks down the tradeoffs between batch and streaming systems and offers a practical framework for choosing between them.
Teaching AI Agents to Ask Better Questions (MIT): MIT researchers found that a smaller model asking smarter questions can outperform much larger ones. A fascinating look at why information-seeking may be the next frontier for AI agents.
Agentic AI Is Becoming a Hiring Signal: Mentions of Agentic AI in U.S. job postings grew more than 280% in a year, according to new analysis from Lightcast and Stanford’s AI Index. As AI agents become more common, understanding how to work with them is quickly becoming a valuable skill.
Where Data Engineering Is Heading in 2026: AI may speed up development, but it won’t replace the fundamentals. This analysis highlights why data modeling, orchestration, architecture, observability, and data quality remain essential as data systems grow more complex.
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