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List of notes for this specialization + Lecture notes & Repository & Quizzes + Home page on Coursera. Read this note alongside the lecture notes—some points aren't mentioned here as they're already covered in the lecture notes.

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DataOps - Automation

Overview

• Recall:
  • DataOps: Set of practices and cultural habits centered around building robust data systems and delivering high quality data products.
  • DevOps: Set of practices and cultural habits that allow software engineers to efficiently deliver and maintain high quality software products.
• DataOps → Automation, Observability & Monitoring, Incident Response (we don’t consider this in this week because it’s cultural habits side of DataOps)
• Automation → CD/CI
• Infrastructure: AWS CLoudFormation, Terraform

Conversation with Chris Bergh (DataOps — Automation)

• DataOps Definition: Methodology for delivering data insights quickly, reliably, and with high quality.
• Inspiration: Derived from lean manufacturing, focusing on efficiency and adaptability.
• Goal: Build a “data factory” to produce consistent and modifiable data outputs.
• Problem in Traditional Data Engineering: Failures are due to flawed systems, not technology or talent.
• Key Principle: Build systems around code (e.g., testing, observability) for reliability.
• Testing: Essential for minimizing future errors and ensuring code quality.
• Iterative Development: Deliver small updates, get feedback, and iterate quickly.
• DataOps vs. DevOps: Both focus on quick, quality delivery; DataOps specifically targets data workflows.
• Don’t Be a Hero: Avoid taking on too much; build systems to support your work.
• Automation and Validation: Always test and validate; measure everything for reliability.
• Proactive Systems: Build environments that ensure long-term success and reduce stress.
• Balance Optimism with Systems: Don’t rely on hope—verify and automate processes for efficiency.