The fastest way for agentic teams to build and run trusted data pipelines, from any source.

The current machine learning revolution has been enabled by the Cambrian explosion of Python open-source tools that have become so accessible that a wide range of practitioners can use them. As a simple-to-use Python library, dlt is the first tool that this new wave of people can use. By leveraging this library, we can extend the machine learning revolution into enterprise data.

Python and machine learning under security constraints are key to our success. We found that our cloud ETL provider could not meet our needs. dlt is a lightweight yet powerful open source tool we can run together with Snowflake. Our event streaming and batch data loading performs at scale and low cost. Now anyone who knows Python can self-serve to fulfil their data needs.

I am building our internal skills usage leaderboard so we can see how people are using AI and spread what's working. It started as a hacked-together GitHub Workflow calling the Databricks API and dumping output to JSON. dltHub turns this into a cohesive process without messy scripts to dedupe queries or wrangle intermediate tables. And the best part is anyone on the team can use agents to easily contribute.

It's easy to write AI-assisted code and get a prototype. dltHub instead is built for the inspect-validate-debug loop, which is where AI-assisted data engineering actually lives or dies. The first platform I've seen that treats that loop as a design assumption, not a feature.

The modeling layer is where the real cost lives, and it's where consulting companies like ours have been doing the most repetitive, high-cost manual work. We are excited about the dltHub product vision for the modeling layer because the main challenge in all data projects is how the data is being interpreted. That's what helps make better business decisions faster, and that is what we are all about.

In dltHub, agents propose transformations in Python, dlt.hub.transformation compiles them to SQL through Ibis, and execution stays inside DuckDB. The data doesn't move into Python memory at any step — the same composition pattern Ibis and Arrow are designed around.

Every Ops leader, irrespective of the company, is trying to answer the same question: is my team getting better or worse?

I always had good intuition about what data I needed, but never the resources to get it and measure it reliably.

Our data was spread across many systems and pulling them together was non-trivial manual effort. Even when we built some automation, we couldn't bear the cost of reconciling it to existing reporting, let alone build, run, and maintain it.

dltHub changes the equation for me.

It's the first product I've seen built for operators (and their agents), not just the fully-staffed enterprise data teams. I'm excited to see what this unlocks for my AI-enabled peers in the near future.

dltHub and Snowflake deliver a simple, end-to-end pathway for financial institutions to transform raw data into governed analytics and AI-ready datasets without needing a full engineering team. Whether you're an engineer stepping in to answer business questions, an analyst building your own pipelines, or someone fluent in AI with basic Python skills, you can pull data from core banking systems, market feeds, and APIs directly into Snowflake. You deliver outcomes that once required specialised data engineering resources.

dltHub lets my agents develop data pipelines locally, test changes quickly and cheaply in CI, and then runs them in the cloud against my largest workloads. It gives them the tools to take care of the knucklehead stuff so that I can get a good night's sleep.

We weren't looking for a custom rebuild, and we didn't want to staff up a data team to run something we couldn't sustain at our size. The toolkit by dltHub took what we already had, structured the parts that needed cleaning up, and gave us back a stack we can confidently run, with Chat-BI on top that actually understands our business.

We needed analytics on our GitHub Actions CI. With dltHub we wired the end-to-end pipeline up in hours, not days. Now we see where builds drag, fix the slow spots, and ship faster.

The quality of AI output depends on the frameworks and context used. This is where the dltHub AI Workbench shines. Ingestion used to sit with me and one other person. Now our analytics engineers self-serve.

Ingestion moved from being owned by a small group with deep tool knowledge to something any Python developer on the team could author, review, and ship. The question changed from "who knows the tool?" to "what data do we need next?"

We migrated from a SaaS ETL platform to custom-owned dltHub pipelines with the help of AI, gsheets, Zendesk, HubSpot, Asana, Personio, REST APIs, S3 to Redshift. Five of our senior analysts now author and maintain those pipelines themselves. Data engineering is no longer a bottleneck for our analytics work.

Before dltHub and the AI Workbench, I had three options: paid SaaS, hand-code, or agent from scratch. Each had a tradeoff, lock-in, time, or hallucinations. I've migrated clients off all three. Context on 9,700+ APIs out of the box.

I haven't hand-coded in months. The dltHub AI Workbench lets me go from requirement to ingestion, transformation, deployment, and visualisation, all from one chat window. I migrate clients from expensive SaaS pipeline services for 10–100x cost reductions.

dltHub is geared towards exactly that, data exploration, logging, quick prototyping and data quality are first-class concerns, not afterthoughts. That's what you need when any engineer can spin up a pipeline in minutes.

I needed to build dlt pipelines on top of a legacy ERP: 1,231 tables and zero documentation. Manual exploration would have taken weeks. Instead, I used Claude Code and dltHub. The whole thing took a few minutes to write and a few hours to run. The alternative was weeks of manual exploration, or paying a consultant to do the same work, more slowly.