Скачать или смотреть Data Engineering Explained: The Complete Playbook (Spark, ETL, & Architecture)

What does it actually take to build the infrastructure that powers AI and big data? Today, we’re cracking open the Data Engineer’s Playbook.

In this video, we move beyond the buzzwords to breakdown the core architecture of modern data engineering. From the differences between OLTP vs. OLAP and Data Warehouses vs. Data Lakes, to the engine room of Apache Spark and performance tuning—this is your end-to-end guide to the data ecosystem.

Whether you are preparing for a data engineering interview, migrating from software development, or just want to understand how data pipelines actually work, this playbook covers the essential tech stack.

In this video, we cover: ✅ The 3 Pillars of Data Engineering (Production, Transformation, Serving) ✅ Data Modeling: Star Schema vs. Snowflake Schema ✅ Why Apache Spark replaced Hadoop MapReduce ✅ How to build robust ETL Pipelines using Airflow and Kafka ✅ The "Shuffle" problem and performance optimization

👇 Table of Contents:
00:00 - The Demand for Data Engineers (35% Growth)
01:20 - Section 1: What is Data Engineering? (The Chef Analogy)
02:08 - OLTP vs. OLAP Explained
02:44 - Section 2: Storing & Modeling Data (Warehouse vs. Lake)
03:25 - Star Schema vs. Snowflake Schema
04:04 - Section 3: The Spark Processing Engine
05:17 - Transformations, Actions, and Lazy Evaluation
06:23 - Section 4: Building the Data Pipeline (ETL)
07:20 - Section 5: Optimizing Performance (Tuning the Engine)
08:45 - Section 6: Data Governance, Quality & The Future of AI

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