Getting Started with dlt: A Simple, Scalable Way to Handle Data Ingestion
When I first ventured into the world of data engineering, ingestion felt like the soggy basement of the pipeline: necessary, a little messy, and not something I would brag about. Dashboards had all the sparkle, and data modeling felt like a fun puzzle to solve. But ingestion? That was just the plumbing.
Still, as someone helping clients with wildly different systems, tight budgets, and a variety of data sources, I quickly learned: if your plumbing isn’t solid, nothing flows. So finding tools that make ingestion simple, scalable, and (dare I say) kind of enjoyable? That’s become part of the mission at Database Tycoon.
Why dltHub Caught My Eye
What drew me to dlt was its promise of simplicity without sacrificing power. It’s open-source, Python-native, and has strong opinions on best practices. It supports both beginners and advanced teams, with features like automatic schema evolution, declarative pipelines, and built-in support for destinations like BigQuery, DuckDB, and PostgreSQL.
Coming from a background in data strategy and UX rather than hardcore engineering, I appreciated that dlt felt accessible. It didn’t assume I had years of pipeline infrastructure experience under my belt.
So, a few teammates and I signed up for their certification course, hoping it would help us not only understand the tool but become fluent enough to confidently recommend it to clients if it was a good fit.
The Certification Experience
The dlt certification was one of the more thoughtfully designed technical courses I’ve taken.
Each module walked through real-world ingestion scenarios, starting with local filesystem ingestion, then progressing through REST APIs with pagination and authentication, and finally guiding us through dbt integration and cloud destinations. It used Google Colab, which removed the setup overhead and let me focus on learning rather than configuring.
The pacing struck a great balance: technical but not overwhelming. If you’ve never dealt with pagination or token refresh logic before, you’ll walk away understanding exactly how it works.
What I Loved
Within minutes, I was running pipelines and seeing results. It felt hands-on and immediately rewarding.
Everything was broken into digestible pieces, with context around why each step mattered.
By the end, I could load data from an external API, transform it, and land it in a warehouse without needing a big team or years of engineering experience.
As a 30-something who grew up in the ’90s, I appreciated working with a Pokémon dataset. 🐉
What Could Be Improved
If I had to nitpick, I’d say I could’ve benefited from a bit more handholding when it came to some of the error messages during experimentation. But that’s not specific to dlt, just part of the growing pains of learning a new tool and stitching multiple tools together. Still, a few more annotated examples or FAQs could help first-timers debug faster.
How I Plan to Use dlt in the Real World
As a co-founder of Database Tycoon, I’m often helping small and mid-sized businesses that don’t have dedicated data engineers. dlt gives us a way to set up ingestion pipelines that are:
Easy to version control and audit
Secure (with native support for secrets and credentials)
Adaptable to changing schemas or data sources
I’m especially excited to pair it with the Stripe API for clients we’re building Stripe financial dashboards for. Whether it’s for internal reporting, client-facing analytics, or just better operational visibility, dlt seems like a strong option for ingestion.
Final Thoughts
The dlt certification gave me more than just new technical skills, it also gave me confidence around the ingestion part of the data pipeline. It also helped me feel prepared to recommend the tool to our engineers working on client projects. In my opinion, this tool has a real future (and present) in the modern data stack.
If you’re a data analyst, strategist, or builder who's ready to step into ingestion for the first time, or looking for a better way to handle it, I highly recommend giving dlt a try. Start with the course. You’ll walk away knowing more than you expected, and excited to build what’s next!