ETL Pipeline Architecture Diagram Examples

These pipeline examples show how the same source-transform-warehouse shape adapts to classic batch ETL, modern ELT, streaming pipelines, and change-data-capture.

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ETL Pipeline Architecture Diagram Examples

Real examples

Classic batch ETL (the baseline)

Who uses it: Team building a first analytics pipeline

Scheduled hourly or nightly job

Extract from sources into a staging area

Transform in the pipeline (Spark, Pandas, dbt models pre-load)

Load only clean, modeled data into the warehouse

Warehouse stays small and clean

Why this works: Classic batch ETL transforms before load — the diagram shows transformation as a discrete stage, which keeps the warehouse minimal but pushes complexity into the pipeline tooling.

ELT (modern cloud-warehouse pattern)

Who uses it: Team using Snowflake / BigQuery / Redshift

Extract and Load raw data into the warehouse first

Transform inside the warehouse via SQL (dbt)

Raw layer + staging layer + marts layer in the warehouse

Easier to debug — raw data is queryable

Uses warehouse compute, not external Spark cluster

Why this works: ELT inverts the order — the diagram puts load before transform and the transform stage lives inside the warehouse. This works because modern cloud warehouses make transformation cheap at scale.

Streaming pipeline

Who uses it: Team needing near-real-time data

Sources stream events through Kafka / Kinesis

Transform via Flink / Spark Streaming / ksqlDB

Continuous load into the warehouse or a real-time store

Latency in seconds, not hours

No scheduler — pipeline runs continuously

Why this works: Streaming replaces the batch scheduler with a continuously running pipeline — the diagram drops the scheduler box and adds a stream-processing engine, because freshness requirements push the architecture from periodic batches to always-on flow.

Change Data Capture (CDC)

Who uses it: Team syncing operational DBs into the warehouse

Read the source DB's change log (binlog, WAL)

Stream changes via Debezium → Kafka

Apply changes to warehouse tables incrementally

Low impact on source database (no heavy queries)

Near-real-time without full extracts

Why this works: CDC eliminates the periodic full-extract — the diagram replaces the extract stage with a change-log reader, so the warehouse stays in sync without ever scanning the source tables.

Tips for better results

Always show the scheduler as a separate component above the pipeline — orchestration is a deployable system, not a property of the transform stage.
Make monitoring a distinct node; pipeline failures are silent without alerts on stage success / freshness.
Label transformation location explicitly (in-pipeline vs in-warehouse) — that single choice is the ETL-vs-ELT distinction.
For streaming pipelines, drop the scheduler and add a stream-processing engine; mixing both confuses readers.

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