Key details at a glance
The five-stage data pipeline flows: Sources (databases, APIs, event streams) → Ingest (Kafka, Airbyte, Fivetran) → Transform (dbt, Spark, Pandas) → Store (BigQuery, Snowflake, PostgreSQL) → Serve (dashboards, APIs, ML features). Orchestration tools (Airflow, Prefect, Dagster) handle scheduling, dependencies, retries, alerts, and observability across the whole pipeline. A pipeline is only as good as its tests — bad data flowing fast through a pipeline is worse than slow, correct data. Raw data is a liability until it's been engineered into a reliable pipeline; properly built, pipelines become a genuine business leverage point.
