Projects/End-to-End Data Pipeline
advanced6 milestones · ~24 hours

End-to-End Data
Pipeline

A startup is unifying ecommerce orders and SaaS subscription data into one analytics platform. You ship the whole thing: a real warehouse target with a modeled star schema, dbt models with green `dbt test`, a scheduled Airflow DAG with incremental/CDC loads, a bigger dataset than the course sandboxes, CI on every push, and a portfolio README — then defend it live. This is the build data-engineering interviews are actually about.

Full project workspace, live PostgreSQL DB, AI Project Mentor & verified credential

What you'll learn

  • —Land a bigger multi-source dataset in a real warehouse target
  • —Model marts as a star schema with SCD Type II and partitioning
  • —Transform with dbt: sources, models, tests, docs, contracts — green build
  • —Orchestrate with a real Airflow DAG: schedule, retries, backfills, idempotent tasks
  • —Load incrementally with CDC semantics (watermarks + log-based change capture)
  • —Gate every push with CI and ship a README a senior DE respects
  • —Defend architecture, trade-offs, and failure modes in a live review

The 6 milestones

Each milestone is reviewed before you advance.

  1. 01

    Repo setup + warehouse target + dataset landing

    Milestone 1

    Repo hygiene, warehouse sandbox, and the bigger dataset staged raw.

  2. 02

    Model the marts — star schema, SCD II, partitioning

    Milestone 2

    Grain decisions, dim/fact DDL, SCD Type II, partition + cluster keys.

  3. 03

    Ingestion — incremental loads + CDC

    Milestone 3

    API/files to Parquet to warehouse with watermarks and change capture.

  4. 04

    Transform with dbt — models, tests, docs, contracts

    Milestone 4

    Sources, staging, marts, and a fully green `dbt test` suite.

  5. 05

    Orchestrate with Airflow — DAG, retries, backfills

    Milestone 5

    A real DAG: schedule, idempotent tasks, retries, documented backfill.

  6. 06

    CI + README + observability + live defense

    Milestone 6

    GitHub Actions CI, portfolio README, monitoring, and the live defense.

Reading & references

Pre-flight setup: Python data-engineering environment
Setup Guide · Premium
Designing data pipelines: a starting framework
Article · Premium
Star vs snowflake schemas: when to use which
Article
Idempotency in data pipelines
Article · Premium
Logging best practices in Python
Article
Transactional loads in Postgres
Article
Cron crash course
Article
Data quality checks: what to monitor
Article · Premium
Anatomy of a great portfolio README
Article
Capstone pre-flight: warehouse sandbox + repo layout
Setup Guide · Premium
CDC in one page: watermarks vs log-based capture
Article · Premium
Live defense: what the reviewer will ask
Article · Premium

Build your portfolio & get certified

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Free End-to-End Data Pipeline portfolio project — with AI review

Build a real end-to-end data pipeline project for your data analyst portfolio in your own public GitHub repo — free brief, starter template, and milestone guides. When your code is ready, the ₹99 pass gets every milestone reviewed against a fixed rubric (end to end data pipeline walkthrough included) and issues a verifiable certificate on completion.

Is the End-to-End Data Pipeline project free?

The full project brief, milestone guides, and starter template are free to audit. The AI-powered review pass and verified certificate unlock at ₹99.

Can I add the End-to-End Data Pipeline project to my resume and GitHub?

Yes — that is the point. You build in your own public GitHub repo, every milestone is reviewed against a fixed rubric, and the certificate links to the repo employers can inspect. Only list what is visible in your repo.

How long does the End-to-End Data Pipeline project take?

Most students finish the 6 milestones in 2–4 weekends. Milestones unlock in order, and you can re-submit any milestone that needs work.

Rubric-reviewed · Plagiarism-checked · Verifiable certificate