From Stock Prices to Sales Performance: Orchestrating the Platform— Part 6

2026-07-12 · Source: Data Engineering on Medium · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

This post details the orchestration of previously developed data processing components (Parts 1-5) into an automated, self-running system. It outlines the creation of four distinct pipelines: one for customer, product, and sales data; another for IBM stock data; a third for ADLS-sourced price data; and a final pipeline for the Gold layer. The decision to split pipelines was practical, allowing isolated re-runs and simplified troubleshooting. The author implemented explicit dependency management, using "Invoke Pipeline" activities to ensure the Gold layer's stored procedures only execute after all upstream data sources (customer, stock, price history) have successfully completed, including a 3600-second "Wait" for an external Power Automate flow. Complex fan-in dependencies for Gold procedures were verified by inspecting the `dependsOn` array in the activity's code view, rather than relying solely on the visual canvas.

Key takeaway

For MLOps Engineers orchestrating complex data flows, prioritize modular pipeline design to enable isolated testing and re-runs. Explicitly manage dependencies using "Invoke Pipeline" activities and verify fan-in logic via code view's `dependsOn` array, rather than relying solely on visual canvas representations. This approach ensures data freshness and operational robustness, preventing silent data drift and simplifying troubleshooting in production environments.

Key insights

Effective data pipeline orchestration prioritizes modularity and explicit dependency management over monolithic designs.

Principles

• Split pipelines for isolated re-runs.
• Reference stored procedures by name.
• Enforce dependencies with AND logic.

Method

Orchestrate data pipelines by chaining activities on success, using Invoke Pipeline for cross-pipeline dependencies, and verifying `dependsOn` arrays for complex fan-in logic.

In practice

• Use "Invoke Pipeline" for cross-pipeline triggers.
• Verify `dependsOn` array in code view.
• Wrap SQL logic in stored procedures.

Topics

• Data Orchestration
• Data Pipelines
• Medallion Architecture
• Microsoft Fabric
• Dependency Management
• Stored Procedures

Best for: Data Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.