Role dbt Does Well Python Does Better Structured data cleaning (staging) β Possible, but inconvenient Designing mart table structures β Also possible User-specific calculations β Inconvenient β Super flexible Scoring, conditional matching, if-else logic β Very cumbersome β Ideal Filtering based on user input β Not possible β Core feature Explaining recommendations, tuning logic β β Fully customizable For Example -- This kind of logic is painful in dbt... SELECT CASE WHEN user.age BETWEEN policy.min_age AND policy.max_age THEN 30 ELSE 0 END + CASE WHEN user.income < policy.income_ceiling THE_ ELSE 0 END + ... In dbt, the concept of a βuserβ doesnβt even exist dbt is built for models that apply the same logic to everyone Python, on the other hand, can generate different recommendations per user based on input π dbt is great for static modeling, but dynamic, user-input-driven recommender systems are better suited for Python. ...
π Building a Batch Data Pipeline with AWS, Airflow, and Spark
β¨ Project Summary Assuming I am working for a fintech company, I built a batch pipeline that automatically aggregates β transforms β analyzes credit card data. Since I couldnβt use real data, I used synthetic transaction data generated using Faker, but I believe it was sufficient for the purpose of designing the overall data flow and structure. π― Goal βBuild an Airflow pipeline that processes realistic financial data with Spark, analyzes and stores them.β ...
Hugo First Post Testing
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