Скачать или смотреть ETL vs ELT: The Deep Dive

For decades, ETL (Extract, Transform, Load) was the standard. But with the rise of the Cloud, a single letter swap changed the entire industry to ELT. But is ETL truly obsolete?

In this video, we break down the technical differences between these two data pipeline architectures. We explain why the cost of on-premise storage forced us to use ETL, and how Cloud Warehouses like Snowflake and BigQuery enabled the ELT revolution.

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In this video, you will learn:

The 3 Core Actions: Extract, Transform, Load explained.

The ETL Constraints: Why high storage costs forced us to clean data outside the warehouse.

The Cloud Paradigm Shift: How elastic compute power changed the game.

The ELT Advantage: Why loading raw data offers better agility and speed.

When to use ETL: The critical role of Compliance (GDPR/HIPAA) and PII masking.

Final Verdict: Latency, Flexibility, and Cost comparison.

⏱️ Timestamps: 0:00 - Intro: Two Acronyms, Massive Difference 0:37 - The Building Blocks (E, T, L) 1:21 - The ETL Era (The Historical Constraint) 2:44 - Is ETL Dead? (Security & Compliance) 3:06 - The Paradigm Shift: Rise of the Cloud ☁️ 3:59 - The ELT Modern Workflow 5:07 - Head-to-Head Comparison (Latency & Cost) 5:40 - Conclusion: The Modern Data Stack

#DataEngineering #ETL #ELT #BigData #SystemDesign #Snowflake #FluentData