Mastering Extraction Pipelines in Cognite Data Fusion

In Industrial DataOps, extracting data from a source is only half the battle—you must also prove it arrived safely. When traditional data extraction scripts fail on remote servers, they often die silently. This "Black Box" problem leads to stale dashboards, corrupted machine learning models, and broken trust with data consumers who discover the outage days later.

Mastering Extraction Pipelines in Cognite Data Fusion is a technical deep dive designed to eliminate these silent failures. This course empowers Data Engineers to build professional-grade, self-monitoring data integrations that provide 24/7 observability.

Moving beyond basic ETL setup, you will learn how to decouple your extraction logic from your monitoring. You will explore how to architect a continuous "heartbeat" mechanism, deploy configurations remotely from the cloud, and enforce rigorous security standards using OIDC Service Principals. By the end of this training, you will be able to transform fragile, unmonitored scripts into robust, "always-on" data pipelines.

What You Will Learn:

• Architect for Observability: Implement the "Heartbeat" mechanism (Seen/Success/Failure statuses) to ensure real-time visibility into the health of your integrations.
• Manage Configuration at Scale: Use Remote Configuration to version-control and update extractor logic (SQL queries, API limits) directly from the CDF cloud, eliminating the need for manual SSH server access.
• Secure Your Integrations: Apply the principle of least privilege using Microsoft Entra ID (OIDC) and Service Principals, replacing legacy API keys with robust identity management.
• Operationalize Monitoring: Configure "Dead Man's Switch" alerting logic to trigger automated email notifications the exact moment a data flow is interrupted.
• Troubleshoot Like a Pro: Utilize Run History logs and Markdown-based documentation features to drastically reduce Mean Time to Recovery (MTTR) during outages.

Who Should Take This Course?

Data Engineers responsible for building, deploying, and maintaining data integrations in CDF.

Prerequisites:

• Fundamental knowledge of Cognite Data Fusion (Assets, Raw, Data Sets).
• Basic understanding of Python scripting (ability to read standard SDK code snippets).
• Familiarity with API authentication concepts (OAuth 2.0 / OIDC).