Towards Data Science
Jun 12
I Thought Data Engineering Was Just Writing Scripts. I Was Wrong.
I tried to make my ETL pipeline production-ready. Three things broke.
I Thought Loading Data Was the Finish Line. It Was the Starting Point.
Building my first dbt models and learning what “analysis-ready” data actually means The post I Thought Loading Data Was the Finish Line. It Was the Starting Point.
Related stories
Towards Data Science
Jul 10
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra The post I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer appeared first on Towards Data Science .
Towards Data Science
Jun 11
BI Is Dead, Long Live BI
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
Towards Data Science
Aug 3
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
Towards Data Science
Jun 18
Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit
Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
Towards Data Science
Jun 23
I Spent an Hour on a Data Preprocessing Task Before Asking Gemini
How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions The post I Spent an Hour on a Data Preprocessing Task Before Asking Gemini appeared first on Towards Data Science .
Towards Data Science
Jul 19
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
Towards Data Science
Jul 2
The Untaught Lessons of RAG Question Parsing: Structure Before You Search
Enterprise Document Intelligence [Vol. 1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science .
Towards Data Science
Jun 26
How to Ace Data and ML Behavioural Interviews
How to smash through data / ML behavioural interviews The post How to Ace Data and ML Behavioural Interviews appeared first on Towards Data Science .
Towards Data Science
Aug 7
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Enterprise Document Intelligence [Vol. 1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
Towards Data Science
2d ago
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.
Towards Data Science
Jun 25
One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About
A reflection on the first month of learning data engineering in public, and what actually kept me going. The post One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About appeared first on Towards Data Science .