dltHub

“Data democracy for our product, business, and operation teams means that they can independently satisfy a majority of their data needs through no-code self-service. If the teams want to build anything else to push the company forward, they don't need to wait for permission or data access to do it. All kinds of new reporting is being done that wasn't possible before.”

“It was easy to adopt dlt by our data and application teams. It follows good engineering practices, has open source code and the documentation allows our engineers to self serve and solve most of the problems. With the help of dlt, we were able to reduce the costs of some of pipelines by up to 50%.”

“dlt has enabled me to completely rewrite all of our core SaaS service pipelines in 2 weeks and have data pipelines in production with full confidence. We also achieved data democracy for our data platform. Our product, business, and operation teams can independently satisfy a majority of their data needs through no-code self-service. The teams built multi-touch attribution for how Harness acquires customers, and models for how Harness customers utilize licenses. If the teams want to build anything else to push the company forward, they don't need to wait for permission or data access to do it.”

“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?”

“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.”

“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.”

“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.”

“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.”

“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. You can pull data from core banking systems, market feeds, and APIs directly into Snowflake.”

“Every Ops leader 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. dltHub changes the equation: it's the first product I've seen built for operators and their agents, not just fully-staffed enterprise data teams.”

“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.”

“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.”

“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.”

“I am building our internal skills usage leaderboard so we can see how people are using AI and spread what's working. 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.”

“The main reasons for why we ended up changing was reliability, we wanted to know what our pipelines were doing, extreme ease of use for Python users... and the open source community.”

“With dlt we could replace something that was extremely daunting by a state of simplicity.”

“One of the really good advantages of dlt compared to other tools is that if our team wants an LLM to help out to do something in dlt, the steps that the LLM has to do are pretty simple [as it goes through the OSS libraries]... If you want to ask the same thing through Stitch or for Airbyte, it has to go through the documentation online to figure out the things, and that becomes a problem.”

“I always try to think that dlt for us is like LEGO. We have the pieces, dlt provides me and us with the manual of how to put those pieces together, and then off we go from there.”

“We tried Airbyte for all this data but the Kafka source was barely maintained. And because of the UI-only implementation of Airbyte, you are bound to what the UI gives you. We also looked at using Stitch, but there you do have that pay-per-go, pay-per-usage model. We didn't like that idea for the amount of data we’d use”

“The data 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 agentic transformation 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.”

“What I didn't expect is how much it unblocks the team. A mid-level engineer can spin up a prototype, browse the raw data in dltHub's local DuckDB workspace, validate the SQL schema - all without pulling in a senior. That loop of prototype, inspect, fix, re-run - that's the real unlock. Static connectors and cloud platforms just don't give you that kind of development workspace.”

“AI-generated pipelines ship fast, but they also fail in ways nobody predicted. That's why dlt's logging matters so much to us. We get detailed, readable traces in CI/CD - not the cryptic nonsense you get from most connectors. If you can't add custom logging to diagnose production failures, you can't run AI-generated code in production. Full stop.”

“The Dataset Browser changed how we scope. We can inspect nested source data right inside the pipeline - spot a column name mismatch, fix it, and move on. With Fivetran or Singer you're flying blind until data lands in the warehouse. With dlt, my engineers debug in the pipeline, not after it.”

“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.”

“At Tasman, we're using Claude Skills as one component of how we accelerate delivery - encoding our standards so every engineer generates pipelines that follow our conventions, not generic defaults. The Toolkits in dltHub's AI Workbench turbocharge that approach.”