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.
The article describes how the author built an incremental data pipeline using dbt snapshots to monitor the job market they are attempting to enter. It also discusses the challenges faced with an API that provided misleading information during the process. The post was published on Towards Data Science.
I tried to make my ETL pipeline production-ready. Three things broke.
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 .
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
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).
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 .
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 .
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
The article titled "What We Miss About Missing Values" explores the often overlooked assumptions embedded in the data we observe, particularly focusing on how missing values can influence analysis and interpretation. It delves into the hidden biases and methodological implications that arise when data is incomplete, urging readers to consider these factors when working with real-world datasets.
The article explores how different data representations can alter our interpretation of the same dataset, highlighting that the way data is visualized or structured can lead to varying narratives. It examines the implications of these storytelling choices for data analysis and communication. The piece emphasizes the importance of thoughtful data presentation in shaping conclusions.
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 .