Fabian Werkmeister

I help you to become fluent in Data and enable you to translate between business & tech ★ Data Scientist @FUNKE ★ 🐍Python 🤖Data Science 🛠️Data Engineering

We are swimming in data.
Yet decisions are still based on gut feeling.
I build systems that close this gap ⬇️

I didn't start in tech, but switched over from economics.
That made me become a data engineer, who understands both business and data very well.
I don't just build pipelines, I build actionable data systems on the Google Cloud Platform that help you decide what to do next.

Here's what you can expect working with me:
1. Business First
Coming from a business background, I speak your language. I use simple and GCP-native setups, keeping your costs low and maintenance easy. My success metric isn't lines of code written, it's how fast I can help you make good decisions based on your data.

2. No Black Boxes
I don't build for you, I build with you. My process is "White-Box," meaning nothing is hidden and you will be involved from day one. All done within in a transparent and communicative partnership. 

3. Engineering with Heart
I believe technical projects fail due to human friction, not because of technological issues. I bring reliability, empathy and a huge passion for my craft to actually solve your business problems.

If you are looking for a Senior GCP Data Engineer who combines technical depth with business clarity and a genuine, transparent partnership  - let’s talk!

👉 Book a free call with me (you can find the link below) to see if we are a match.

Data Scientist

Growth only happens outside of your comfort zone.
And this year I'll be stepping 𝘄𝗮𝘆 outside of mine!

What began as a random idea during lunch last year turned into a company with my wife Daniela Werkmeister
Introducing: 𝗪𝗲𝗿𝗸𝗺𝗲𝗶𝘀𝘁𝗲𝗿 𝗟𝗮𝗯𝘀 🚀

As of today, I am available as a 𝗙𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁.

𝗪𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮𝗱𝘃𝗶𝗰𝗲, 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗼𝗿 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗱𝗼 𝗳𝗼𝗿 𝘆𝗼𝘂:
✅ GCP Data Engineering & Architecture
✅ Data Integration & Modeling
✅ Data Warehouse & BI Reporting
✅ Data Strategy & Architecture Consulting

You get technical excellence delivered with a huge passion, a lot of ❤️ and the right pinch of humour :)

Because in the end, data projects are about people working together to solve problems.


I am ready to build something amazing with you!

𝗪𝗵𝗲𝗻𝗲𝘃𝗲𝗿 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗿𝗲𝗮𝗱𝘆, 𝗯𝗼𝗼𝗸 𝗮 𝗳𝗿𝗲𝗲 𝗰𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝗺𝗲 𝗵𝗲𝗿𝗲:
https://lnkd.in/eNy-gb8j
(you can also find the booking link on my profile)

I build actionable data systems on GCP that drive decisions | Technical excellence delivered with passion & ♥️ | Freelance Data Engineer & Independent Consultant

Your most powerful skill in data is not Python or SQL.
𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘁𝗼 𝗮𝘀𝗸 𝗯𝗮𝘀𝗶𝗰 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀.

I used to sit in meetings with business and sales
Nodding along when "revenue" was thrown around
Definitely didn't want to look like I don't know the basics

One day, I actually had to recompute this column
I noticed that I had no idea how to approach that
Felt embarassed to ask, since project start was long ago

But I couldn't continue, so I just asked in the next meeting:
"Can we briefly define 'revenue' in this specific context?"
Long story short - got the best explanation ever 😅

Expected to get an epic roasting
Indeed got several 1 on 1 deep dives
Just by getting involved and asking questions!

𝟯 𝗘𝗮𝘀𝘆 𝗦𝘁𝗲𝗽𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘁𝗵𝗶𝘀:
1️⃣ Play the beginner and ask fundamental questions
2️⃣ Listen carefully and 𝘄𝗿𝗶𝘁𝗲 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗱𝗼𝘄𝗻!
3️⃣ Try to keep your tech jargon on a basic level, too


It feels uncomfortable to ask basic things.
But there is something much more uncomfortable:
➡️ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗮 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗻𝗼 𝗼𝗻𝗲 𝘁𝗿𝘂𝘀𝘁𝘀


𝗣𝗦: next level is to ask 3 different departments and align their vastly different definitions of "revenue! 😜

---

Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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𝗔𝗺𝗮𝘁𝗲𝘂𝗿: "I build complex data platforms"
𝗣𝗿𝗼: "I talk to stakeholders"

I used to think my job as a data engineer was to stay quiet and build the most sophisticated solution possible.

I thought business context was just random noise.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁?
❌ I built complex solutions for problems that didn't exist.
❌ I delivered "insights" that no one could use.
❌ I was frustrated because nobody "got it."
➡️ 𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗿𝗮𝗻𝗱𝗼𝗺 𝗻𝗼𝗶𝘀𝗲

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝘁𝗵𝗶𝗻𝗴𝘀 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘁𝗼 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 𝘁𝗵𝗶𝘀:

𝟭. 𝗧𝗮𝗹𝗸 𝘁𝗼 𝗽𝗲𝗼𝗽𝗹𝗲
Don't guess what they need - have a conversation!

𝟮. (𝗧𝗿𝘆 𝘁𝗼) 𝘀𝗽𝗲𝗮𝗸 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲
Stop hiding behind your tech jargon and try to understand their goals and challenges, even if this takes 2 or 3 calls

𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗶𝗺𝗽𝗹𝗲
Take 1 problem and solve it with the minimum setup
➡️ Then iterate and build upon this


𝗬𝗼𝘂 𝗮𝗹𝘀𝗼 𝘄𝗼𝘂𝗹𝗱𝗻'𝘁 𝗰𝗼𝗼𝗸 𝘂𝗽 𝗮 𝟱-𝘀𝘁𝗮𝗿 𝘀𝘁𝗲𝗮𝗸 𝗱𝗶𝗻𝗻𝗲𝗿 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗴𝘂𝗲𝘀𝘁𝘀 𝗮𝗿𝗲 𝘃𝗲𝗴𝗲𝘁𝗮𝗿𝗶𝗮𝗻𝘀 😜

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Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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Don't rely on goals for the new year.
𝗕𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 ⤵️

We all know the drill.
1st of January.
Motivation is high.

You set giant, ambitious goals.
❌ "I'll read 50 books."
❌ "I'll finally get fit and live more healthy."
❌ "I'll become an expert in Python, AWS and Snowflake."

By February, life happens.
Motivation fades.
You quit.

I used to fall into this trap every single year.
Until I shifted my mindset from 𝗚𝗼𝗮𝗹𝘀 to 𝗦𝘆𝘀𝘁𝗲𝗺𝘀:

✅ "Read 50 books" ➡️ 𝗥𝗲𝗮𝗱 𝟭𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝗲𝗱. 
✅ "Get fit " ➡️ 𝗧𝗿𝘆 𝘁𝗼 𝗴𝗲𝘁 𝟭𝟬𝗸 𝘀𝘁𝗲𝗽𝘀 𝗶𝗻 𝗲𝗮𝗰𝗵 𝗱𝗮𝘆. 
✅ "Master Python" ➡️ 𝗟𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝟭𝟱 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗮 𝗱𝗮𝘆.

This removes the pressure and focuses on consistency.
And remember my favorite equation: 𝟭 > 𝟬
Doing a tiny bit is always better than doing nothing at all!


Here's how I approach 2026:
• 10min of self-compassion and gratitude before bed
• 20min of working and coding on a new pet project 
• Cold shower immediately after waking up
  (let's see if this one will work out 😅)


𝗣𝗦: In 2026 I will also start the biggest thing in my professional career, but more on that tomorrow 😉 

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Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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I make learning Data Science effortless for you | Data Science @ Bosch

Hi 👋🏻
I'm Banias, AI Engineer at Netze BW.

Applying new technologies and methods in Data Science & AI is my passion.

Through my experience and continual learning, I've gained valuable insights and tips that I love sharing with Data Scientists, Machine Learning Engineers and builders like you.

If you're looking for practical advice, helpful resources, and articles on the latest trends or best practices in the field, you're in the right place.

Let's connect and stay updated on the exciting world of Data Science together. Feel free to reach out, read my articles, and let's grow our knowledge and network. :)


Note: My views are my own and don‘t represent my employer.

Banias Baabe

How do you hit 98.7% accuracy with RAG on complex documents?

You don't use vector search.

Traditional RAG often fails because semantic similarity isn't true relevance. For tough domains, you need a system that reasons.

This is where 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 shines.

It's a new, vectorless RAG framework that mimics how a human expert analyzes a document.

Instead of chunking and embedding, 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 builds a "table-of-contents" tree and uses tree search to find the most relevant information.

Why is this better?
✅ 𝗛𝘂𝗺𝗮𝗻-𝗹𝗶𝗸𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: It simulates how experts navigate complex information.
✅ 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: The reasoning is traceable and interpretable. No more "vibe retrieval."
✅ 𝗡𝗼 𝗔𝗿𝗯𝗶𝘁𝗿𝗮𝗿𝘆 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: It respects the document's natural structure.
✅ 𝗡𝗼 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕: The system relies purely on LLM reasoning and document hierarchy.

This approach achieved state-of-the-art results on the FinanceBench benchmark.

🔗 Check it out: github(.)com/VectifyAI/PageIndex


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I teach AI Engineers the concepts and tools they need to stay ahead | AI Engineer @ Netze BW

Running a 70B LLM on a single 4GB GPU is possible with 𝗔𝗶𝗿𝗟𝗟𝗠.

𝗔𝗶𝗿𝗟𝗟𝗠 optimizes inference memory usage so you can run big models without quantization, distillation, or pruning.

It runs inference layer by layer, loading only the layer that is needed for the next compute step, then freeing that memory before moving on.​

To make that practical, it first preprocesses the original Hugging Face weights and re-shards them by transformer layer (instead of giant multi‑GB shards), so each load is focused and smaller.​

This matters because disk I/O becomes the bottleneck when you stream weights.

The main thing that still sits in GPU memory is the KV cache for attention, which is much smaller than full model weights for typical context lengths.​

𝗔𝗶𝗿𝗟𝗟𝗠 also adds prefetching so it can overlap loading the next chunk of weights with the current compute, which helps claw back some speed.

And if you want more throughput, it supports an optional “model compression” mode using block-wise quantization of weights to shrink what needs to be loaded from disk, with the project claiming up to ~3x speedup and minimal accuracy loss.

🔗 Link to repo: github(.)com/lyogavin/airllm


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Stop destroying your documents with 512-token chunks.

Traditional RAG is breaking your tables, splitting your diagrams, and mixing up your model specifications.

I just read a great article from Alex Vesa about a working data modeling approach for complex technical documents.

The problem? Most RAG systems treat a 500-page service manual like a blog post. They chunk blindly. A table that spans three pages gets shredded into meaningless fragments.

Here's what actually works:

✅ Pages, not chunks
Use full pages as your retrieval unit. That's how humans read technical docs anyway.
✅ Three-page sliding windows
Process each page with its neighbors (N-1, N, N+1). Now your system knows when content continues across boundaries.
✅ Treat tables like they matter
Extract structure. Generate metadata. Flatten for search. Keep all three representations.
✅ Multi-vector indexing
Dense embeddings for semantic search. Sparse vectors for exact matches. ColBERT for precision. Combine them.
✅ Domain-specific metadata
Model applicability, component types, cross-page relationships. Not just title and date.

Link ➙ https://lnkd.in/dJ9aJKAB

Thinking about using 𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿 for your next project?

You might want to think again.

Here's the reality of using it in production:
1️⃣ 𝗜𝗩𝗙𝗙𝗹𝗮𝘁 𝗶𝗻𝗱𝗲𝘅𝗲𝘀 𝗻𝗲𝗲𝗱 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝘀. New vectors throw off your cluster distribution over time.
2️⃣ 𝗛𝗡𝗦𝗪 𝗶𝗻𝗱𝗲𝘅𝗲𝘀 𝗮𝗿𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗵𝗼𝗴𝘀. We're talking 10GB of memory for hours just to build an index for millions of vectors.
3️⃣ 𝗜𝗻𝘀𝗲𝗿𝘁𝗶𝗻𝗴 𝗻𝗲𝘄 𝘃𝗲𝗰𝘁𝗼𝗿𝘀 𝗶𝘀 𝗮 𝗽𝗮𝗶𝗻. With IVFFlat, you have to reindex preiodically. With HNSW, you're looking at graph traversals and memory allocation for every new vector.
4️⃣ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗰𝗹𝗲𝗮𝗿-𝗰𝘂𝘁. Pre-filtering is great for selective filters, but post-filtering can give you incomplete results, and it only gets worse with more filters.
5️⃣ 𝗡𝗼 𝗯𝘂𝗶𝗹𝘁-𝗶𝗻 𝗵𝘆𝗯𝗿𝗶𝗱 𝘀𝗲𝗮𝗿𝗰𝗵. You'll have to figure out how to balance vector similarity with text relevance and normalize scores on your own.

A new extension, pgvectorscale, does fix some of these problems with more memory-efficient indexing and better filter performance. But it isn't supported out-of-the-box on Azure and AWS, so you'd have to manage your own Postgres instance.

𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿 is a great tool, but it's not a silver bullet. Make sure you're ready for the operational overhead before you commit.

🔗 Link to article: https://lnkd.in/d6jSRy2y


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Tired of blocking I/O messing up your async Python apps?

Try 𝗮𝗶𝗼𝗳𝗶𝗹𝗲𝘀 - the game-changer for handling files in asyncio without the headaches.

It's an Apache-licensed library that wraps standard file ops into async magic.

Why you should use it:
• Mirrors Python's built-in file API, so it's super familiar.
• Supports buffered text, binary files, and even tempfiles asynchronously.
• Delegates to a thread pool to keep your event loop flying.


🔗 Link to repo: github(dot)com/Tinche/aiofiles

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Vector databases are bloated.

What if you could get the same power with 97% less space?

Try 𝗟𝗘𝗔𝗡𝗡, the world's smallest vector index.

It transforms your laptop into a private and powerful RAG system.

You can finally RAG on everything.
-> Your entire file system
-> Millions of local emails
-> 💻 Your entire codebase
-> 💬 All your chat histories

Why is 𝗟𝗘𝗔𝗡𝗡 interesting?
✅ 𝗜𝘁'𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝘆 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁: Index 60 million documents in just 6GB instead of 201GB.
✅ 𝗜𝘁'𝘀 𝟭𝟬𝟬% 𝗽𝗿𝗶𝘃𝗮𝘁𝗲: Your data never leaves your laptop. No cloud costs, period.
✅ 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗹𝗼𝘀𝘀: Get the same search quality as heavyweight solutions.

🔗 Link to repo: github(dot)com/yichuan-w/LEANN


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Do you want to identify which lines use the most amount of memory in your Python script?

Try 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿.

𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 is a Python module to make a line-by-line analysis of memory consumption for Python functions.

Below you can see how to use memory_profiler within your Python script:

• Decorate the function you want to profile with @𝗽𝗿𝗼𝗳𝗶𝗹𝗲.
• Run the script by passing the option -𝗺 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 to load the 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 module.

Link to project: pypi(dot)org/project/memory-profiler


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What, if an API call in your program fails?

Because of, let’s say, an instable internet connection?

This is not so uncommon.

You usually should have some retry mechanism in your program.

With 𝐭𝐞𝐧𝐚𝐜𝐢𝐭𝐲 in Python, this isn’t a problem anymore.

𝐭𝐞𝐧𝐚𝐜𝐢𝐭𝐲 offers a retrying behavior with a decorator with powerful features like:

- Define Stop Conditions
- Define Wait Conditions
- Customize retrying on Exception
- Retry on Coroutines

One of my favorite libraries!

Check it out: github(dot)com/jd/tenacity


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OpenAI's Responses API isn't what they're telling you it is.

They are talking about how it's "faster, more flexible, easier" than the old chat completions API.

But here's what's really going on.

OpenAI hides their reasoning traces.

Unlike Claude, DeepSeek, or Qwen, you can't see GPT-5's chain of thought.

This creates a massive problem for developers using the stateless chat completions API.

Without access to reasoning traces, you can't pass them back into the conversation.

Result? GPT-5 performs worse than it should in your applications.

The Responses API is the workaround.

✅ It's stateful (OpenAI stores conversation history on their servers)
✅ They maintain the hidden reasoning traces in the backend
✅ They plug it into conversations automatically
✅ They strip it out before sending responses back to you

You get full model capability without ever seeing the reasoning.

Here's the uncomfortable truth:

The old stateless API is actually simpler.

Prefix caching works in both. Parallel tool calling works in both.

There's nothing inherently better about stateful architecture here.

The Responses API only exists because OpenAI won't show you what their models are thinking.

Meanwhile, Anthropic keeps their reasoning traces completely transparent.

If you're building with AI APIs in 2026, this matters.

The question is whether you're comfortable with your provider hiding critical information from you.

Link to article: https://lnkd.in/drZjD9Xj


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➕ For daily practical AI and Python posts, follow Banias Baabe.

AI makes your team faster.

But it also puts your data at risk.

Files are moving to new tools constantly.

AI models are training on things they shouldn't see.

And old security tools just can't see what is happening.

They are blind to the context that actually matters.

That is why I'm watching Cyberhaven’s launch on February 3.

They are revealing a new platform that combines data security and protection into one tool.

It's built to handle how messy and fast data moves today.

Join the live event to see:
📍 A live demo of this new AI-native platform.
📍 How to spot the few dangerous actions hidden inside millions of normal ones.
📍 Real stories from leaders on why old rules don't work anymore.

Don't wait for a data leak to find the holes in your security.

Grab your spot here: https://lnkd.in/dC-aGjjM

Struggling with slow data processing for AI training?

Try 𝗹𝗶𝘁𝗱𝗮𝘁𝗮, the Python library that transforms and optimizes datasets at scale!

𝗹𝗶𝘁𝗱𝗮𝘁𝗮 offers:
🟢 Streaming large cloud datasets without local downloads
🟢 Accelerating training by 20x with optimized data
🟢 Parallelizing tasks across multiple machines effortlessly
🟢 Securely handling sensitive data with advanced encryption

Perfect for images, text, video, and more.

Check it out: github(dot)com/Lightning-AI/litdata


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The Battle

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Banias Baabe

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taplio

The Battle

Fabian Werkmeister

Banias Baabe

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Fabian Werkmeister's LinkedIn Performance

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