Here are the best platforms (and resources) for hands‑on data‑engineering project tutorials, with what they’re good for and who they suit:

1. Coursera

• Good for: structured, course‑to‑project learning (specializations from universities and cloud providers).
• Strengths: curated paths, graded projects, certificates; strong cloud/data engineering courses from Google Cloud, IBM, and universities.

2. Udacity (Data Engineering Nanodegree)

• Good for: project‑based curriculum with mentor support.
• Strengths: end‑to‑end capstone projects, career‑focused, emphasis on pipelines, databases, Spark, cloud deployments.

3. Udemy

• Good for: inexpensive single courses and targeted project tutorials (ETL, Airflow, Spark, Kafka).
• Strengths: lots of practical, project‑based courses; frequent sales.

4. DataCamp

• Good for: interactive, code‑first exercises and short projects (Python, SQL, Spark).
• Strengths: browser-based exercises, clear progression for data engineering toolset basics.

5. Pluralsight

• Good for: intermediate/advanced tech topics and tool deep dives (Kafka, Flink, Spark, data architecture).
• Strengths: skill assessments, path learning, professional developer focus.

6. Kaggle

• Good for: practical datasets, kernels/notebooks, community notebooks for ingest/transform/feature pipelines.
• Strengths: free datasets and runnable notebooks; great for practicing ETL and feature engineering on real data.

7. GitHub (project repos & example pipelines)

• Good for: real projects and reference implementations (Airflow dags, dbt projects, streaming apps).
• Strengths: source code you can clone, run, and modify; many reproducible tutorials and templates.

8. YouTube (channels & playlists)

• Good for: free walkthroughs and end‑to‑end build videos (Airflow, Spark, Kafka, cloud services).
• Strengths: visual demos, many creators show live projects and deployments.

9. Cloud provider learning platforms

• Google Cloud Skill Boosts (formerly Qwiklabs), AWS Skill Builder, Microsoft Learn / Azure Labs
• Good for: cloud‑native data engineering projects (BigQuery, Dataflow, Glue, Redshift, Dataproc, Synapse).
• Strengths: hands‑on labs, temporary cloud accounts, real infra practice with step‑by‑step labs.

10. dbt Learn and dbt Hub

• Good for: analytics engineering and modular transformation projects.
• Strengths: opinionated, project templates, practical examples and community packages.

11. Airflow / Prefect docs + tutorial repos

• Good for: orchestrating pipelines; many official and community tutorials include runnable projects.
• Strengths: production‑oriented DAG examples, local-to-cloud deployments.

12. Blogs & Medium/Towards Data Science

• Good for: step‑by‑step project walkthroughs and architectural case studies.
• Strengths: many practical tutorials with code snippets and end‑to‑end explanations.

How to pick the right platform (quick guide)

• Absolute beginners: DataCamp, Coursera introductory specializations, YouTube beginner playlists.
• Learn-by-building (recommended): Udacity, Coursera guided projects, Cloud provider labs + GitHub templates.
• Tool deep dives (Airflow, Kafka, Spark): Pluralsight, Udemy, official docs + GitHub examples.
• Cloud‑native production skills: Cloud Skill Boosts / AWS Skill Builder + real labs and cost‑controlled cloud accounts.
• Analytics/transformations (dbt): dbt Learn + dbt Hub + GitHub sample projects.

Sample project ideas and where to learn them

• Batch ETL pipeline (local → data warehouse): Coursera/Udacity project + GitHub repo templates.
• Streaming ingest with Kafka + Spark Streaming: Pluralsight/Udemy + GitHub examples.
• Orchestration with Airflow: Airflow docs + YouTube walkthroughs + GitHub DAGs.
• ELT with dbt + BigQuery: dbt Learn + Google Cloud Skill Boosts.
• End‑to‑end cloud data lake + analytics: Cloud provider labs (Skill Boosts/AWS Skill Builder) + Coursera specialization.

If you want, I can:

• Recommend 3 specific beginner-to-advanced tutorials (with direct course titles).
• Suggest a 6–8 week project roadmap using free and low‑cost resources.