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How data actually moves

7 parts

This path is for analysts who inherited a pipeline and were never in the room when it was designed. Parts walk the path from sources to a table someone queries, batch versus streaming, where the data lives, orchestration, what dbt is for, dev versus prod, and how you notice a break. Start with the first part on this page.

Start with part 1 →

  1. 1 How data moves from source systems to a dashboard: the four stages of a data pipeline Inherited a broken dashboard number? Map data from sources through land, transform, and serve so you can debug the path, not the logos. Part 1 of How data actually moves for analysts who inherit pipelines.
  2. 2 Batch vs streaming in plain English Real time is a latency budget, not a vibe. Learn batch, streaming, and micro-batch in plain English so you know when yesterday is fine, when minutes matter, and when delay truly costs money.
  3. 3 Data warehouse, data lake, or plain database: which one does your data need? App database, warehouse, or lake? Walk a plain decision tree for where data should live so product traffic, cheap history, and certified analytics stop fighting over one system.
  4. 4 What data orchestration does, explained with one simple metaphor Orchestration is kitchen management for data: schedules, dependencies, and retries. Learn the Airflow and dbt mental models so broken morning numbers point to a clear task, not a vague outage.
  5. 5 What is dbt? How it turns raw warehouse data into clean tables dbt turns SQL transforms into versioned models with tests and docs. Learn staging, intermediate, and marts so you can read a change even if you do not own the repo.
  6. 6 Why data teams use separate dev, test, and production environments Laptop truth is not production truth. Learn what dev, stage, and prod mean for data work, a simple promotion path from notebook to trusted table, secrets hygiene, and who should be allowed to write where.
  7. 7 How to find out a data pipeline broke before your dashboard users do Pipeline observability means early signals: freshness, volume, schema, and a business check. Red, yellow, green without theater, plus a human who gets paged.