🇫🇷 Paris data folks 💛 We’re hosting a dlt Community Meetup in Paris on Feb 4th (6–9 PM) together with Polycea. A community meetup focused on practical takeaways, shared learnings, and conversations with people using dlt hands-on. Join us here:

Data quality is the vegetables of data engineering: everyone agrees it's important, but nobody wants to implement it. To increase your 𝚟̶𝚎̶𝚐̶𝚎̶𝚝̶𝚊̶𝚋̶𝚕̶𝚎̶ ̶𝚒̶𝚗̶𝚝̶𝚊̶𝚔̶𝚎̶ test coverage, check out these 11 delicious recipes. https://dlthub.com/blog/practical-data-quality-recipes-with-dlt

Semantic layers are the most important "boring" part of data.  Building them manually is a bottleneck for Chat-BI. dlt is changing the game by "autofilling" the metadata gap, turning months of modeling into minutes of automation. 

Most data quality failures happen because checks come too late. dlt + dltHub treat quality as a lifecycle: in-flight checks, safe staging, and production monitoring. Catch issues earlier, fix less and trust your data more. docs: https://dlthub.com/docs/general-usage/data-quality-lifecycle

@ssp.sh drops another great deep-dive, a declarative data stack dlt + @clickhouse.com + @rilldata.com that simplifies tracking cloud spend across multiple platforms. A complete cloud-native FinOps setup in minutes. 🔗 Explore the full walkthrough

We're thrilled to take the stage at the Snowflake for Startups Pitch Night! Join us at the Silicon Valley AI Hub to see how dlt's code & LLM-first infra-native data ingestion library is the fastest way to get compliant data into Snowflake.

AI workflows break on LLM updates? Our Anti-Entropy pattern fixes this with declarative scaffolds, error loops, and dashboards for antifragile convergence. Saves time, acts like an invisible senior engineer. Ditch crutches, skip the ‘Deer in Headlights’ panic!

To overcome complex cloud cost analysis, @ssp.sh showed how dlt can ingest and normalize AWS, GCP, and Stripe data into a unified cost dashboard. The result is a single view for ROI analysis powered by a simple ELT. Check out the full solution! 👇

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.

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

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 👉

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

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.

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. 🧵👇

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.

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...

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

"If this song could stop aging like fine wine, that would be swell" www.youtube.com/watch?v=HLUX...

Trump and Putin together are supporting a fascist candidate in the Romanian presidential elections. 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.

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...

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

vibe coding in a nutshell: www.youtube.com/watch?v=OhYW... #databs

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...

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.

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

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

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

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

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

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

I put on my robe and wizard hat #dlthub hoodie #dagster hat #databs

Data teams using dlt often build declarative pipeline interfaces to enable low-code collaboration. So we created dlt+ Project, expanding this concept into a full collaboration interface. 🚀 🔗 Read more: dlthub.com/blog/project 👀 Join early access: info.dlthub.com/waiting-list #dataengineering

data doesn't lie, politicians do

Data teams don’t move fast. They wait. They wait for dbt runs. They wait for queries. They wait to see if something breaks. Stop testing in production. Start testing like an engineer. 🔗 dlthub.com/blog/staging #databs #dataengineering

Stop waiting on your warehouse. dlt+ Cache lets you run dbt models, test data, and debug transformations instantly—with DuckDB, Iceberg, Snowflake, BigQuery, ClickHouse & more. Fast. Composable. No waste. 🔗 dlthub.com/blog/cache

Is your job going to be replaced by AI? Probably. But just as we’ve moved on from writing assembly code, our roles are evolving rather than vanishing. Embrace change, hone your expertise, and be proactive about learning. As the saying goes, "when the student is ready, a teacher will appear."

New in early access: dlt+ Project & Cache! ⚡ Test transformations locally before loading 💰 Reduce cloud costs, iterate faster 💡 Define & manage pipelines in YAML Try it out! 🔗 dlthub.com/blog/dltplus... #DataEngineering #databs

check it out!

🚀 Live Event w MotherDuck & dltHub: Fast & Scalable Analytics Pipelines 📅 Feb 26 | 17:00 CET | Zoom 🔹 Easy data ingestion 🔹 Custom ETL pipelines in Python 🔹 Smooth transition from DuckDB to MotherDuck 🔥 Live demo + Q&A! 🔗 Register: lu.ma/79a7lysr?utm... #databs

Testing in production isn’t a rite of passage, it’s a failure of process. 💰 Every test query = vendor profit 🚨 Every mistake = production issue 😩 Every debug run = rerunning the entire pipeline Staging for data is here dlthub.com/blog/staging #dataengineering #databs

Debezium + CDC + Python + dlt → Real-time PostgreSQL replication, no Kafka. 🔥 New blog by OSS contributor Ismail Simsek → A Python-native CDC pipeline for PostgreSQL & DuckDB. ✅ No Kafka ✅ Python-first ✅ Step-by-step guide Read it now → dlthub.com/blog/debeziu... #dataengineering

Breaking into data engineering? Join our Data Talks Club Data Engineering Zoom camp workshop and learn how to load data like a pro lu.ma/quyfn4q8

Writing Iceberg or Delta on filesystem/buckets? dlt supports both: dlthub.com/docs/dlt-eco... And if you wanna Bring Your Own Compute Engine, dlt supports that too (via ibis) dlthub.com/blog/datasets #databs #dataengineering #datasky

🔥 The biggest lie in data engineering?
"This tool will make your life easier." Commoditized tools are built for selling, not engineers.
They hide complexity & costs, locking you in. The alternative? Democratization, flexibility, and control. read more: dlthub.com/blog/goodbye... #databs

𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐫𝐮𝐧 𝐝𝐥𝐭 𝐨𝐧 𝐀𝐖𝐒 𝐥𝐚𝐦𝐛𝐝𝐚? (tldr - 100x cheaper than Saas) Here are code templates: Github: github.com/codingcyclis... Blog: dlthub.com/blog/dlt-aws... We use GCP ourselves: dlthub.com/blog/dlt-seg... #databs

2x your data ingestion pipeline development speed with Cursor AI. Mooncoon 🦝, a consulting partner at dltHub, shares a step by step tutorial how to do it. See the guide + git repo here: dlthub.com/blog/mooncoon

✨ OSS Enterprise success story time! Stellantis (14 car brands including Jeep & Maserati) is using @dlt to: - Cut 60+ data tools down to 4 - Speed up pipelines by 66% - Onboard devs in 2hrs Open source eating enterprise, one pipeline at a time 🚀 Check it out here! www.youtube.com/watch?v=Kj3E...

Tired of juggling tools & formats to access data? With dlt’s universal interface, you can: 🔄 Switch between local & cloud with ease ⚡ Test data w/o touching prod 🚀 Build portable, scalable pipelines dlthub.com/blog/datasets #databs