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Enterprise Systems, Data InfrastructureBusiness
B2B
Data Migration, Pipeline Automation, Azure Optimization, Reporting, QA
JMS needed a scalable, cost-effective migration of their legacy Oracle database (IFS) to Azure Databricks. We implemented a powerful data pipeline using Azure Data Factory, optimized for performance, cost, and reliability, migrating over 1.7 billion records with a 90% cost reduction from initial projections.
Designed robust Azure Data Factory pipelines to handle both creation and updating of tables using chunking logic and size-based filtering.
Transformed Parquet data into Delta format for high-performance analytics, automated table creation, and update workflows.
Reduced pipeline cost to ~$250 (down 90%) through optimized batching, parallelism for small tables, and serialized high-volume data transfers.
An in-app dashboard that automatically analyzes driver behavioral data and generates personalized reports for improved coaching.
Fully migrated enterprise-scale Oracle datasets with verified accuracy.
Dropped full-run costs from $2500+ to around $250 via optimized flow and resource tuning.
Automated update logic ensured high data accuracy and near-zero manual patching.
JMS now operates on a robust, repeatable data migration and sync framework.
Enterprise Data Migration & Sync
Infrastructure Cost Optimization
Real-Time Table Updates & Sync
Custom Data Warehousing & Analytics
Automated Enterprise-Grade Reporting
We believe in turning ideas into reality and we're ready to join your journey. Reach out to us and let's start discussing your project.
