Full End-to-End ETL Pipeline + Gold Data Lakehouse Architecture Tutorial 2026

Описание: In this video, we build a production-ready ETL pipeline and implement a Gold-tier Data Lakehouse Architecture from scratch — fully containerized and ready for real-world data engineering workloads!

We walk through every layer of the pipeline using a modern open-source stack, showing you how raw data gets ingested, transformed, and served as clean, query-ready gold tables — all running locally with Docker.
Tech Stack:

🐍 Python — Orchestration & pipeline logic
🪣 MinIO — S3-compatible object storage (Gold layer)
🔥 Apache Spark — Distributed data processing & transformation
🧊 Apache Iceberg — Open table format for reliable lakehouse storage
🔍 Trino — Fast, distributed SQL query engine on top of Iceberg
🐘 PostgreSQL — Iceberg metadata catalog layer
🐳 Docker — Fully containerized, reproducible environment

📚 What You'll Learn:

• How to design and implement a multi-layer Lakehouse (Bronze → Silver → Gold)
• How to ingest and process tick data through a full ETL pipeline
• How to query Iceberg tables with Trino for analytics
• How to tie together a modern open-source data stack end to end
• How to use the processed data in a machine learning model

🔗 Resources:

📊 Quant Data Manager: https://strategyquant.com/quantdatama...
💻 GitHub Repo: https://github.com/AlgoDeveloper400/B...

If you found this helpful, don't forget to like, subscribe, and hit the 🔔 bell so you never miss a new video! I upload weekly!!
Here are the video timestamps so you can skip to the part you like the most:

00:00 Introduction – End-to-End Data Lakehouse + ML Pipeline Overview
05:11 Data Processing – Ingestion & Transformation Workflow
10:51 Exploratory Data Analysis (EDA)
12:28 YAML Configuration for Apache NiFi & Data Lakehouse Setup
15:31 Docker Container Startup & Environment Initialization
16:31 Apache NiFi Flow Design & Pipeline Configuration
20:40 Data Lakehouse Setup Scripts (Infrastructure & Tables)
27:47 Machine Learning Pipeline – Training & Evaluation
33:33 MLflow UI – Experiment Tracking & Model Registry
35:17 Live Model Inference Demo
36:53 Business Context & Use Case Explanation
40:00 Live Predictions & Production Simulation

#DataEngineering #ETLPipeline #DataLakehouse #ApacheSpark #ApacheIceberg #Trino #MinIO #Docker #Python #PostgreSQL #BigData #DataArchitecture #OpenSourceData #DataPipeline #LakehouseArchitecture #SparkSQL #TickData #QuantitativeFinance #DataEngineering2026 #OpenLakehouse