Project Name
Azure Synapse and Spark to Apache NiFi Pipeline Migration for a Canadian Pasta Manufacturer
The client is a leading pasta manufacturer in Canada, producing over 45,000 tonnes of premium pasta annually across 3 production facilities in Ontario and Quebec. Distributing 120+ SKUs to retail and foodservice customers across 8 provinces, the company employs over 1,200 people and generates approximately CAD 320 million in annual revenue. Known for premium-quality products made with high-grade Canadian durum semolina and advanced Italian production technology, their data engineering team of 12 engineers managed pipeline operations across both cloud and on-premises environments. As operations scaled, their existing five-step data pipeline created compounding problems. Data moved from Azure Data Lake Storage (ADLS) to Azure Synapse Analytics, through Apache Spark jobs for CSV-to-Parquet conversion, into a cloud SQL Server, and finally to an on-premises SQL Server. This architecture delivered increasing data latency and growing operational overhead.
For a manufacturer where production scheduling, inventory replenishment, and quality control decisions depend on current data, that latency had real consequences. Decisions were being made on yesterday’s numbers. The client partnered with Ksolves, an AI-First Company, to replace the Azure Synapse and Spark-based architecture with a simpler, lower-cost pipeline delivering near-real-time data availability without adding infrastructure complexity.
The client faced the following challenges:
- High Azure Compute Costs: The existing architecture relied heavily on Azure Synapse Analytics and Spark-based processing, incurring substantial DWU charges and Spark execution costs. As data volumes scaled, these costs increased disproportionately, making the pipeline financially inefficient for the organization.
- Limited Near-Real-Time Data Ingestion: The batch-oriented loading process did not support near-real-time ingestion. Achieving lower latency would have required additional Synapse and Spark resources, driving costs higher and worsening the budget problem rather than resolving it.
- High Pipeline Complexity and Operational Overhead: The five-step architecture, covering ADLS, Azure Synapse, Spark CSV-to-Parquet jobs, cloud SQL Server, and on-premises SQL Server transfer, created multiple failure points and handoff risks. Each step required monitoring, debugging, and manual intervention whenever a component failed.
- On-Premises-to-Cloud Synchronization Risk: The final transfer from cloud SQL Server to on-premises SQL Server introduced a synchronization risk. Any failure at this step created data inconsistency between the cloud analytics environment and the on-premises systems that plant managers and logistics teams relied on daily.
- No End-to-End Pipeline Visibility or Data Lineage: The multi-step Synapse and Spark architecture provided no native end-to-end pipeline visibility or data lineage tracking. When data anomalies appeared in operational reports, tracing issues back to the source required manual log inspection across five separate systems, significantly increasing mean time to resolution.
Ksolves deployed a streamlined, event-driven Apache NiFi architecture to replace the five-step Synapse and Spark pipeline, using AI-assisted pipeline analysis to map all existing data flows before any configuration change was made.
- Apache NiFi Cluster Setup: A secure, highly available 3-node Apache NiFi cluster was deployed to ensure fault tolerance, scalability, and consistent data flow management across all ingestion paths.
- Pipeline Architecture Replacement: NiFi reads directly from Azure Data Lake Storage using the ListAzureBlobStorage and FetchAzureBlobStorage processors, processes files on arrival without requiring batch Spark execution, and writes transformed output directly to both the cloud SQL Server and the on-premises SQL Server via NiFi JDBC processors. This eliminated the separate Azure Synapse and Spark processing steps entirely, reducing the pipeline from five steps to three.
- Integration with Reporting Tools: Processed data was integrated directly with visualization and reporting tools via direct SQL Server connection, enabling near-real-time refresh of production and inventory dashboards without an additional ETL layer.
- File-Level Tracking and Data Lineage: NiFi Provenance tracking was enabled across all flows, providing full data lineage, operational transparency, and easier troubleshooting without manual log inspection across multiple systems.
Technology Stack
| Category | Legacy Architecture (Replaced) | Modern Architecture (NiFi-Based) |
|---|---|---|
| Data Integration | Azure Synapse Analytics | Apache NiFi (3-node HA cluster) |
| Processing | Apache Spark (CSV-to-Parquet jobs) | NiFi event-driven flow processors |
| Object Storage | Azure Data Lake Storage (ADLS) | Azure Data Lake Storage (ADLS) |
| Cloud Database | Microsoft SQL Server | Microsoft SQL Server (cloud, retained) |
| On-Premises Database | Microsoft SQL Server (on-prem) | Microsoft SQL Server (on-prem, retained) |
| Lineage and Monitoring | Manual log inspection across 5 systems | NiFi built-in Provenance tracking |
| Cloud Provider | Microsoft Azure | Microsoft Azure |
- Azure Synapse and Spark Costs Eliminated: Replacing Azure Synapse Analytics and Spark-based processing with Apache NiFi removed Synapse DWU charges and Spark execution hours from the monthly Azure bill entirely, delivering a measurable reduction in cloud infrastructure spend.
- Near-Real-Time Data Availability Achieved: Event-driven NiFi ingestion reduced end-to-end pipeline latency from the previous batch cycle to near-real-time availability, giving production planners and operations teams access to current data during the working day rather than the following morning.
- Pipeline Complexity Reduced from 5 Steps to 3: The new NiFi architecture replaced the five-service legacy pipeline with a streamlined 3-step flow, eliminating two Azure services and reducing the number of failure points across the data engineering stack.
- Spark Jobs Replaced with Zero-Code NiFi Flows: All Spark processing jobs were reimplemented as event-driven NiFi processors, eliminating Spark cluster startup overhead and making pipeline changes configurable in the NiFi UI without requiring developer involvement for every update.
- Full Data Lineage Established Across All Flows: NiFi Provenance now tracks every file and record through the complete pipeline, reducing the mean time to diagnose data issues from manual log inspection across five separate systems to a single, unified view in the NiFi Provenance interface.
“Our data pipeline was costing us far more than it should, and we were still working off yesterday’s data for production planning. Ksolves replaced the entire Azure Synapse and Spark architecture with Apache NiFi in a way that was far simpler and faster than we expected. We now have near-real-time visibility into our production and inventory data, our Azure costs have reduced substantially, and when something needs changing in the pipeline, it takes minutes rather than days. We are continuing to work with them on the next phase for exactly that reason.”
—Head of Data and Technology, Leading Canadian Pasta Manufacturer (name withheld by request)
By migrating from Azure Synapse and Spark to Apache NiFi, Ksolves helped the Canadian pasta manufacturer eliminate unnecessary cloud costs, reduce pipeline complexity from five services to three, and achieve near-real-time data availability for production and operations teams. The migration delivered immediate operational improvements and opened the door to Phase 2 of the client’s data platform evolution. Their return for Phase 2 is the strongest proof of the engagement’s value.
As an AI-First Company, Ksolves brings AI-driven pipeline analysis and data engineering expertise to every NiFi engagement. For food manufacturers and industrial enterprises managing hybrid Azure data pipelines, our Apache NiFi Development Company services deliver the cost efficiency and operational clarity that complex Synapse and Spark architectures cannot match.
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