What Happens After You Click "Buy Now"? A Data Engineering Journey

You tap "Buy Now" on a pair of running shoes. $149.99. One tap. In the next two seconds, that click is copied, split, stored, cleaned and counted by nine different systems. If just one of them gets it wrong, tomorrow the CEO makes a decision on the wrong number. In this video we follow ONE click, order ORD-10482, all the way from your thumb to the boardroom, with the real tools data engineers use at every step. Plus the exact moment where this click almost gets counted twice. ⏱ Chapters 0:00 The Click: one tap, nine systems 0:41 Birth: capture and ingestion (JSON event, producer, Kafka) 2:13 Two paths, one event: streaming vs batch (Spark, Airflow) 3:58 The data lake (S3, Parquet, partitioning, Apache Iceberg) 5:38 The transformation factory (Databricks, Spark, dbt tests) 7:24 The warehouse (Snowflake, star schema, SQL, Power BI) 8:36 The conductor (Airflow DAGs, retries, backfills) 9:26 The payoff: from click to insight 🧠 What you'll learn • Why apps send events to Kafka instead of writing straight to a database • Topics, partitions, offsets and retention, explained simply • Streaming vs batch, and why you need both (and why they must agree) • How a data lake stores everything cheaply, and why raw data is never clean • What Apache Iceberg adds: safe updates, schema changes, time travel • How Spark cleans data at scale, and how dbt models and tests it • Star schema basics, and why Snowflake separates storage from compute • How Airflow schedules, retries and backfills your pipelines 🛠 Tools covered Kafka · Spark Structured Streaming · Airflow · Amazon S3 · Parquet · Apache Iceberg · Databricks · dbt · Snowflake · Power BI 📺 More from the Data Engineering Fixes series • Your pipeline ran twice (duplicate data) • Two dashboards, two numbers • Your pipeline is green. Your data is wrong. 👉 Subscribe for more practical data engineering and AI. #dataengineering #kafka #apachespark #snowflake #dbt #airflow #datalake #apacheiceberg #databricks #dataengineer #bigdata #etl