Global Freight Forwarders Incremental Ingestion of Logistics Data (Microsoft Fabric)

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Company Background

  • Global logistics provider handling high-volume shipments

  • Requires timely data for tracking and SLAs

  • Modernizing ingestion using Microsoft Fabric

Current Situation

  • Manual daily logistics file uploads

  • Full data reprocessing every ingestion run

  • Inconsistent data availability for dashboards

Challenges Identified

  • Manual uploads with no automation

  • Full-file ingestion causing processing delays

  • No watermark to track processed data

  • Unreliable data freshness for operations

  • Variable source schemas across systems

  • Limited monitoring for failures




Call to Action

  • Design automated incremental ingestionB ingestion in Fabric

  • Replace manual processes with reliable pipelines

Objective

  • Process only new or updated events

  • Track progress using watermark table

  • Load delta records into Bronze layer

  • Update watermark after successful runs

  • Run pipeline on fixed schedule

Architecture Overview

  • Fabric pipeline with lookup and incremental load

Watermark Strategy

  • Created Lakehouse watermark tracking table

  • Stored last processed event timestamp

  • Used as checkpoint for ingestion runs

Lookup-Based Incremental Design

  • Lookup retrieves latest watermark value

  • Determines extraction window per run

  • Ensures accurate incremental processing

Incremental Copy Activity

  • Filtered records using watermark timestamp

  • Loaded only delta records to Bronze

  • Prevented duplicate data ingestion

Watermark Update Logic

  • Updated watermark after successful ingestion

  • Used pipeline trigger time as checkpoint

  • Maintained continuous incremental boundaries

Scheduling and Automation

  • Configured scheduled pipeline execution

  • Automated extraction, load, and updates

  • Eliminated all manual data uploads

Deliverables Achieved

  • Fully automated incremental ingestion pipeline

  • Improved performance and reduced data waste

  • Delivered fresh data for operational reporting

  • Removed manual effort from engineering workflows














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