Data Engineering: Why Prompts Are Dead (Use Loops)
Stop repeating manual tasks in Cloud Code. Learn how to use AI-powered automation to create an efficient data pipeline and avoid errors.
This video is aimed at developers seeking code optimization and wanting to stop wasting time on repetitive manual processes. We analyze how the incorrect use of AI can stall your workflow in Cloud Code and how to fix it immediately.
You will learn how to implement a structured plan with five essential cycles for your data pipeline: extraction, transformation, loading, testing, and error correction. We show in practice how to configure a local database to replace BigQuery, ensuring greater agility and control in your development.
If you want to master AI-powered automation in your daily work, subscribe to receive new development tutorials every week. Leave a comment about which stage of your data pipeline usually generates the most problems.
🚀 What you will learn:
Why manual prompts fail in complex pipelines.
How to design reliable AI loops with the G-CAVS framework.
Practical example: Automatic validation of CSV files. Practical Example: ETL Pipeline.
How to avoid excessive costs with guardrails and iteration limits.
🔻🔻 Other Important Links 🔻🔻
Spark, SQL, GIT, AIRFLOW and AWS Training:
https://codifike.com.br/treinamentos
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COUPON: codifike_jetbrains_promo
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