7.1 Data Pipeline Architecture¶

Role at Stage 7: Model Infrastructure¶

Move raw data through repeatable ingestion, cleaning, validation, and lineage steps. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

%%{init: {"flowchart": {"htmlLabels": true, "nodeSpacing": 80, "rankSpacing": 110}, "themeVariables": {"fontSize": "18px"}} }%%
flowchart LR
  P["<b>7.1</b><br/>Data Pipeline<br/>Architecture"]
  P --> S1["<b>7.1.1</b><br/>ETL and Scheduled Jobs"]
  P --> S2["<b>7.1.2</b><br/>Crawlers and<br/>Connectors"]
  P --> S3["<b>7.1.3</b><br/>Cleaning and<br/>Validation"]
  P --> S4["<b>7.1.4</b><br/>Lineage and Versioning"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
7.1.1 ETL and Scheduled Jobs	ETL and Scheduled Jobs is the working skill inside Data Pipeline Architecture that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.1.2 Crawlers and Connectors	Crawlers and Connectors is the working skill inside Data Pipeline Architecture that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.1.3 Cleaning and Validation	Cleaning and Validation is the working skill inside Data Pipeline Architecture that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.1.4 Lineage and Versioning	Lineage and Versioning is the working skill inside Data Pipeline Architecture that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Data Pipeline Architecture supports a deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards..
Build and inspect this artifact: Create an ETL pipeline.
Measure progress with: Track records, rejected rows, schema versions, and lineage.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Create an ETL pipeline.

Measure: Track records, rejected rows, schema versions, and lineage.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 7: Model Infrastructure.