Adrian Brudaru
@datateam.bsky.social
Data engineer & Cofounder @dlthub. Building out the tooling i wish i had.
European data teams can enjoy lightning-fast analytics & production-ready pipelines with @motherduck EU region fully available.
Choose loading via MotherDuck or dlt's native DuckLake destination → support for Postgres, DuckDB, SQLite, MySQL.
Choose loading via MotherDuck or dlt's native DuckLake destination → support for Postgres, DuckDB, SQLite, MySQL.
Motherduck Europe & dlt DuckLake support
MotherDuck lands in Europe with serverless DuckDB warehousing. dlt adds DuckLake support, giving EU teams a fast, modern data stack.
dlthub.com
November 13, 2025 at 3:23 PM
European data teams can enjoy lightning-fast analytics & production-ready pipelines with @motherduck EU region fully available.
Choose loading via MotherDuck or dlt's native DuckLake destination → support for Postgres, DuckDB, SQLite, MySQL.
Choose loading via MotherDuck or dlt's native DuckLake destination → support for Postgres, DuckDB, SQLite, MySQL.
Ever launched a data pipeline & wondered what’s happening under the hood? The dlt Workspace Dashboard gives real-time visibility into pipeline state, schemas, live dataset queries, run traces → all in one web app. Built with
@marimo.io.
Try it now: 👉 https://dlthub.com/docs/general-usage/dashboard
@marimo.io.
Try it now: 👉 https://dlthub.com/docs/general-usage/dashboard
November 12, 2025 at 3:39 PM
Ever launched a data pipeline & wondered what’s happening under the hood? The dlt Workspace Dashboard gives real-time visibility into pipeline state, schemas, live dataset queries, run traces → all in one web app. Built with
@marimo.io.
Try it now: 👉 https://dlthub.com/docs/general-usage/dashboard
@marimo.io.
Try it now: 👉 https://dlthub.com/docs/general-usage/dashboard
After pushing LLMs to their limits, we found a better way.
A hybrid model that grounds AI in verified facts → fewer hallucinations, faster onboarding, and data pipelines that just work.
Read more here 👉
A hybrid model that grounds AI in verified facts → fewer hallucinations, faster onboarding, and data pipelines that just work.
Read more here 👉
The feature we were afraid to talk about
This is the story of how we made our LLM generation workflow superior to starting from raw docs.
dlthub.com
October 23, 2025 at 9:42 AM
After pushing LLMs to their limits, we found a better way.
A hybrid model that grounds AI in verified facts → fewer hallucinations, faster onboarding, and data pipelines that just work.
Read more here 👉
A hybrid model that grounds AI in verified facts → fewer hallucinations, faster onboarding, and data pipelines that just work.
Read more here 👉
The real AI win isn't superhuman agents, it's scaled mediocrity.
Doing less with less at massive scale unlocks tasks that were once uneconomical.
The magic is in aggregate value, not perfect outputs. Empower teams with practical AI tools.
🔗 https://dlthub.com/blog/the-real-ai-win-scaled-mediocrity
Doing less with less at massive scale unlocks tasks that were once uneconomical.
The magic is in aggregate value, not perfect outputs. Empower teams with practical AI tools.
🔗 https://dlthub.com/blog/the-real-ai-win-scaled-mediocrity
October 17, 2025 at 12:39 PM
The real AI win isn't superhuman agents, it's scaled mediocrity.
Doing less with less at massive scale unlocks tasks that were once uneconomical.
The magic is in aggregate value, not perfect outputs. Empower teams with practical AI tools.
🔗 https://dlthub.com/blog/the-real-ai-win-scaled-mediocrity
Doing less with less at massive scale unlocks tasks that were once uneconomical.
The magic is in aggregate value, not perfect outputs. Empower teams with practical AI tools.
🔗 https://dlthub.com/blog/the-real-ai-win-scaled-mediocrity
For years, the most celebrated person on the data team was the one who could write the heroic, last-minute query.
We celebrated firefighters. Craftsmen.
We built a culture around reacting, not architecting. A leverage trap of our own making.
We celebrated firefighters. Craftsmen.
We built a culture around reacting, not architecting. A leverage trap of our own making.
October 14, 2025 at 12:50 PM
For years, the most celebrated person on the data team was the one who could write the heroic, last-minute query.
We celebrated firefighters. Craftsmen.
We built a culture around reacting, not architecting. A leverage trap of our own making.
We celebrated firefighters. Craftsmen.
We built a culture around reacting, not architecting. A leverage trap of our own making.
1/5 Perfect Friday reading: Erfan Hesami's guide to dlt makes data pipelines actually simple.
Our co-founder spent a decade rebuilding the same pipelines. One question changed everything: "What if there was a way to reuse code?"
That became dlt.
🧵👇
Our co-founder spent a decade rebuilding the same pipelines. One question changed everything: "What if there was a way to reuse code?"
That became dlt.
🧵👇
October 10, 2025 at 11:50 AM
1/5 Perfect Friday reading: Erfan Hesami's guide to dlt makes data pipelines actually simple.
Our co-founder spent a decade rebuilding the same pipelines. One question changed everything: "What if there was a way to reuse code?"
That became dlt.
🧵👇
Our co-founder spent a decade rebuilding the same pipelines. One question changed everything: "What if there was a way to reuse code?"
That became dlt.
🧵👇
The Zero Theorem (2013)
A lonely data worker tries to prove that all his computation adds up to zero. Ten years later, we call it AI productivity.
A lonely data worker tries to prove that all his computation adds up to zero. Ten years later, we call it AI productivity.
October 7, 2025 at 12:21 PM
The Zero Theorem (2013)
A lonely data worker tries to prove that all his computation adds up to zero. Ten years later, we call it AI productivity.
A lonely data worker tries to prove that all his computation adds up to zero. Ten years later, we call it AI productivity.
Pipelines working...but platform missing?
This hands-on workshop covers lightweight infrastructure, CI/CD, and flow automation for real-world data workflows.
Location: Online
Date: September 24th, 2025
Time: 16:00 (CET | Berlin)
community.dlthub.com/productizing...
This hands-on workshop covers lightweight infrastructure, CI/CD, and flow automation for real-world data workflows.
Location: Online
Date: September 24th, 2025
Time: 16:00 (CET | Berlin)
community.dlthub.com/productizing...
Workshop: Productizing Data Platforms - Infrastructure for Orchestrating dlt Pipelines by The Scalable Way
Learn to productize your data platform and orchestrate dlt pipelines. This hands-on workshop covers lightweight infrastructure, CI/CD, and flow automation, giving you practical steps to build a scalab...
community.dlthub.com
September 9, 2025 at 12:16 PM
Pipelines working...but platform missing?
This hands-on workshop covers lightweight infrastructure, CI/CD, and flow automation for real-world data workflows.
Location: Online
Date: September 24th, 2025
Time: 16:00 (CET | Berlin)
community.dlthub.com/productizing...
This hands-on workshop covers lightweight infrastructure, CI/CD, and flow automation for real-world data workflows.
Location: Online
Date: September 24th, 2025
Time: 16:00 (CET | Berlin)
community.dlthub.com/productizing...
Scalable Lakehouse Architectures with Iceberg and Polaris!
Simon from Tactile shared insights on tackling bottlenecks in data loading using Apache Iceberg and our open-source dlt library.
youtu.be/gb5fwIO4pX0?...
#databs #iceberg
Simon from Tactile shared insights on tackling bottlenecks in data loading using Apache Iceberg and our open-source dlt library.
youtu.be/gb5fwIO4pX0?...
#databs #iceberg
Scalable Lakehouse Architecture with Iceberg & Polaris: A Battle-tested Playbook
YouTube video by Apache Iceberg
youtu.be
June 12, 2025 at 7:15 AM
Scalable Lakehouse Architectures with Iceberg and Polaris!
Simon from Tactile shared insights on tackling bottlenecks in data loading using Apache Iceberg and our open-source dlt library.
youtu.be/gb5fwIO4pX0?...
#databs #iceberg
Simon from Tactile shared insights on tackling bottlenecks in data loading using Apache Iceberg and our open-source dlt library.
youtu.be/gb5fwIO4pX0?...
#databs #iceberg
Flobots - Handlebars (Official Video)
YouTube video by FlobotsVEVO
www.youtube.com
May 15, 2025 at 7:48 AM
Trump and Putin together are supporting a fascist candidate in the Romanian presidential elections.
Enough said.
Enough said.
May 13, 2025 at 9:56 AM
Trump and Putin together are supporting a fascist candidate in the Romanian presidential elections.
Enough said.
Enough said.
I’ve been quiet on this too long. I’m not speaking as a founder here. Just as a person who believes that racism, hate, have no place in a just society.
Trumpism isn’t “just politics.” It’s an ideology that dehumanizes.
I won’t make space for it in my life, my conversations, or my circle.
Trumpism isn’t “just politics.” It’s an ideology that dehumanizes.
I won’t make space for it in my life, my conversations, or my circle.
May 4, 2025 at 7:58 AM
I’ve been quiet on this too long. I’m not speaking as a founder here. Just as a person who believes that racism, hate, have no place in a just society.
Trumpism isn’t “just politics.” It’s an ideology that dehumanizes.
I won’t make space for it in my life, my conversations, or my circle.
Trumpism isn’t “just politics.” It’s an ideology that dehumanizes.
I won’t make space for it in my life, my conversations, or my circle.
Vibe code dltHub Jaffle Shop API: using Curl to get responses for pagination.
With this workflow you allow your agent to grab information that isn't available in docs such as response structure
#dataengineering #databs
www.youtube.com/watch?v=fpNZ...
With this workflow you allow your agent to grab information that isn't available in docs such as response structure
#dataengineering #databs
www.youtube.com/watch?v=fpNZ...
Vibe code dltHub Jaffle Shop API: using Curl to get responses for pagination
YouTube video by dltHub
www.youtube.com
April 19, 2025 at 3:30 AM
Vibe code dltHub Jaffle Shop API: using Curl to get responses for pagination.
With this workflow you allow your agent to grab information that isn't available in docs such as response structure
#dataengineering #databs
www.youtube.com/watch?v=fpNZ...
With this workflow you allow your agent to grab information that isn't available in docs such as response structure
#dataengineering #databs
www.youtube.com/watch?v=fpNZ...
We just dropped a 4h knowledge bomb in collaboration with
@freecodecamp.bsky.social and #DataTalksClub
It's designed for people already in the data field who want to upskill to senior DE knowledge in data loading best practices.
www.youtube.com/watch?v=T23B...
#dataengineering #databs
@freecodecamp.bsky.social and #DataTalksClub
It's designed for people already in the data field who want to upskill to senior DE knowledge in data loading best practices.
www.youtube.com/watch?v=T23B...
#dataengineering #databs
Data Engineering with Python and AI/LLMs – Data Loading Tutorial
YouTube video by freeCodeCamp.org
www.youtube.com
April 19, 2025 at 3:27 AM
We just dropped a 4h knowledge bomb in collaboration with
@freecodecamp.bsky.social and #DataTalksClub
It's designed for people already in the data field who want to upskill to senior DE knowledge in data loading best practices.
www.youtube.com/watch?v=T23B...
#dataengineering #databs
@freecodecamp.bsky.social and #DataTalksClub
It's designed for people already in the data field who want to upskill to senior DE knowledge in data loading best practices.
www.youtube.com/watch?v=T23B...
#dataengineering #databs
Firebringer | I don't really want to do the work today
YouTube video by Sebastian Andras
www.youtube.com
April 18, 2025 at 3:51 PM
Loved the energy around Apache Iceberg at the Amsterdam meetup.
At dltHub, we’re focused on making Iceberg actually usable—modernizing legacy stacks with auto-ingestion, schema handling, and pipeline orchestration.
Modernization doesn’t have to hurt.
www.linkedin.com/posts/data-t...
At dltHub, we’re focused on making Iceberg actually usable—modernizing legacy stacks with auto-ingestion, schema handling, and pipeline orchestration.
Modernization doesn’t have to hurt.
www.linkedin.com/posts/data-t...
#apacheiceberg #moderndatastack #datapipelines #dlthub | Adrian Brudaru
Violetta’s talk with Lakekeeper showed exactly what we’re focused on at dltHub: making Iceberg actually usable, by modernizing your data stack and automating ingestion, schema evolution, and pipeline ...
www.linkedin.com
April 3, 2025 at 9:53 AM
Loved the energy around Apache Iceberg at the Amsterdam meetup.
At dltHub, we’re focused on making Iceberg actually usable—modernizing legacy stacks with auto-ingestion, schema handling, and pipeline orchestration.
Modernization doesn’t have to hurt.
www.linkedin.com/posts/data-t...
At dltHub, we’re focused on making Iceberg actually usable—modernizing legacy stacks with auto-ingestion, schema handling, and pipeline orchestration.
Modernization doesn’t have to hurt.
www.linkedin.com/posts/data-t...
1/2 It’s not that LLMs can’t code pipelines. It’s that they read your API docs and said: ‘nah.’
Just dropped a benchmark test on AI-generated EL pipelines using Pipedrive.
TL;DR – bad docs reliably break even the best models.
Got 3 solid examples of where things went wrong.
Just dropped a benchmark test on AI-generated EL pipelines using Pipedrive.
TL;DR – bad docs reliably break even the best models.
Got 3 solid examples of where things went wrong.
April 1, 2025 at 9:53 AM
1/2 It’s not that LLMs can’t code pipelines. It’s that they read your API docs and said: ‘nah.’
Just dropped a benchmark test on AI-generated EL pipelines using Pipedrive.
TL;DR – bad docs reliably break even the best models.
Got 3 solid examples of where things went wrong.
Just dropped a benchmark test on AI-generated EL pipelines using Pipedrive.
TL;DR – bad docs reliably break even the best models.
Got 3 solid examples of where things went wrong.
One more experiment that went great - it looks feasible to convert any pipeline to better code or another framework.
dlthub.com/blog/moderni...
#databs
#dataengineering
dlthub.com/blog/moderni...
#databs
#dataengineering
Erase data loading tech debt with dlt + Cursor + LLMs
Modernisation at its finest, from trash to cutting edge in seconds. It works amazing, just give it a try, stop paying for tech debt
dlthub.com
March 27, 2025 at 3:25 PM
One more experiment that went great - it looks feasible to convert any pipeline to better code or another framework.
dlthub.com/blog/moderni...
#databs
#dataengineering
dlthub.com/blog/moderni...
#databs
#dataengineering
Vibe coding EL pipelines - an exploration and first success. Inside:
- problem definition
- a first experiment ended surprisingly successful, some learnings enclosed
- I will keep pushing and see how far it's possible to go with more complex cases
dlthub.com/blog/convert...
#databs
- problem definition
- a first experiment ended surprisingly successful, some learnings enclosed
- I will keep pushing and see how far it's possible to go with more complex cases
dlthub.com/blog/convert...
#databs
From Airbyte YAML to Python with dlt, Cursor and LLMs
In this microblog + video we explore generating python pipelines (dlt REST API) from Airbyte low code yaml spec. Tl;dr: it works well.
dlthub.com
March 25, 2025 at 4:48 PM
Vibe coding EL pipelines - an exploration and first success. Inside:
- problem definition
- a first experiment ended surprisingly successful, some learnings enclosed
- I will keep pushing and see how far it's possible to go with more complex cases
dlthub.com/blog/convert...
#databs
- problem definition
- a first experiment ended surprisingly successful, some learnings enclosed
- I will keep pushing and see how far it's possible to go with more complex cases
dlthub.com/blog/convert...
#databs
How do you run dlt on #airflow?
Check out this comprehensive guide written by our consulting partners Untitled Data Company.
selectstarfrom.substack.com/p/how-to-run...
#databs #dataengineering
Check out this comprehensive guide written by our consulting partners Untitled Data Company.
selectstarfrom.substack.com/p/how-to-run...
#databs #dataengineering
How to run dlt with Airflow
Learn how to run dlt (or any Python code) with Airflow using PythonOperator, PythonVirtualenvOperator, KubernetesPodOperator, and external compute services
selectstarfrom.substack.com
March 19, 2025 at 9:27 AM
How do you run dlt on #airflow?
Check out this comprehensive guide written by our consulting partners Untitled Data Company.
selectstarfrom.substack.com/p/how-to-run...
#databs #dataengineering
Check out this comprehensive guide written by our consulting partners Untitled Data Company.
selectstarfrom.substack.com/p/how-to-run...
#databs #dataengineering
Managed cloud databases markup compute by 35-70x. It was fine when humans ran queries. AI doesn’t need coddling.
Iceberg lets you:
✅ Run queries anywhere
✅ Cut insane compute costs
✅ Keep control over your data
It’s your budget. Stop setting it on fire. dlthub.com/blog/iceberg...
#databs
Iceberg lets you:
✅ Run queries anywhere
✅ Cut insane compute costs
✅ Keep control over your data
It’s your budget. Stop setting it on fire. dlthub.com/blog/iceberg...
#databs
Why Iceberg + Python is the Future of Open Data Lakes
Data lakes are broken. Python + Iceberg fixes them. No lock-in. No silos. Just open, AI-ready data. Read on why and how to switch ->
dlthub.com
March 19, 2025 at 9:15 AM
Managed cloud databases markup compute by 35-70x. It was fine when humans ran queries. AI doesn’t need coddling.
Iceberg lets you:
✅ Run queries anywhere
✅ Cut insane compute costs
✅ Keep control over your data
It’s your budget. Stop setting it on fire. dlthub.com/blog/iceberg...
#databs
Iceberg lets you:
✅ Run queries anywhere
✅ Cut insane compute costs
✅ Keep control over your data
It’s your budget. Stop setting it on fire. dlthub.com/blog/iceberg...
#databs
What can we learn from how humans think, to apply that to LLMs?
Humans think in steps, ranging from "right now" to "overnight"
Here are a couple of examples
Humans think in steps, ranging from "right now" to "overnight"
Here are a couple of examples
March 14, 2025 at 5:31 PM
What can we learn from how humans think, to apply that to LLMs?
Humans think in steps, ranging from "right now" to "overnight"
Here are a couple of examples
Humans think in steps, ranging from "right now" to "overnight"
Here are a couple of examples
I tried DeepResearch. It gave me tons of sources that were LLM hallucinated trash. I called it out and asked how to mitigate. The answer?
"Use trusted sources only"
So much for that.
#databs
"Use trusted sources only"
So much for that.
#databs
March 12, 2025 at 5:08 PM
I tried DeepResearch. It gave me tons of sources that were LLM hallucinated trash. I called it out and asked how to mitigate. The answer?
"Use trusted sources only"
So much for that.
#databs
"Use trusted sources only"
So much for that.
#databs
March 11, 2025 at 4:39 PM