For many organizations, data platforms evolve over time rather than being designed for scale from day one. As data volumes grow, architectures that once performed adequately can become a constraint, slowing operations, impacting customer experiences, and limiting future growth.
That was the challenge facing one financial technology organization whose data transformation environment was struggling to keep pace with increasing demands. HTEC helped redesign the platform, delivering a faster, more scalable foundation while eliminating a critical customer-facing performance problem.
The Challenge
The organization’s data transformation pipelines were built entirely on Azure Data Factory (ADF) and Microsoft SQL Server. Heavy transformation workloads were executed through MSSQL-based processing, creating significant performance limitations as data volumes increased.
The business faced a strict operational requirement: complete end-to-end processing within a six-hour window. However, pipeline execution routinely exceeded acceptable limits, creating growing concerns about reliability and scalability.
Two major bottlenecks were responsible:
- Slow data reads from Microsoft SQL Server.
- Resource-intensive transformation logic running on a single-node SQL engine, creating performance issues during large-scale joins and aggregations.
The impact extended beyond backend operations. Reporting and analytics services depended on an OData endpoint that was also constrained by the existing architecture. As processing delays accumulated, customer-facing reports began timing out, leading to poor user experiences, stale data, support escalations, and declining confidence in the platform.
The organization needed more than incremental tuning—it needed an architectural shift.
What HTEC Brought to the Party
HTEC worked alongside the client to modernize the data platform using Databricks as the primary transformation engine.
Rather than replacing existing investments, HTEC redesigned the architecture to leverage the strengths of each technology. Azure Data Factory remained in place as the orchestration layer, while Databricks became the platform responsible for large-scale data processing and transformation.
A key component of the engagement was the migration and reengineering of transformation workloads from T-SQL into Spark and PySpark. This was not a simple code conversion exercise. Existing transformation logic had to be redesigned to operate effectively within a distributed computing model, taking advantage of Databricks’ scalability and performance characteristics.
HTEC also redesigned the reporting architecture so that customer-facing OData services could read directly from Databricks rather than relying on SQL Server as an intermediary serving layer. This removed a major source of latency and eliminated the reporting bottlenecks users had been experiencing.
What the Client Could Not Have Achieved Without HTEC and Databricks
The organization would likely have continued missing its six-hour processing target, further eroding trust among its own customers.
The existing approach relied on a compute model that was increasingly mismatched to the volume and complexity of the data being processed.
Data flows had to be rethought and redesigned around distributed processing principles, including partitioning strategies, join optimization, and incremental data handling.
HTEC helped address the root cause of the problem by modernizing the underlying architecture and aligning the platform to the realities of rapidly growing data volumes.
The Specific Skills HTEC Brought
The success of the program depended on a combination of deep engineering expertise and practical architectural judgment.
HTEC contributed:
- Data engineering and software engineering expertise, enabling complex transformation logic to be redesigned for a distributed processing environment.
- Platform and architecture experience, allowing the team to determine where Azure Data Factory continued to add value as an orchestration layer and where Databricks delivered superior performance for compute-intensive processing.
- Change Data Capture (CDC) architecture and implementation, helping the client move from full reprocessing to efficient incremental data pipelines.
- Modern pipeline design practices, creating a foundation that balances performance, maintainability, and future scalability.
Value Delivered
The modernized architecture delivered measurable results across operational performance, customer experience, and future scalability.
Key outcomes included:
- Pipeline runtime reduced from consistently missing a six-hour target to completing in under two hours, representing approximately a threefold improvement.
- Incremental processing through Databricks, eliminating the need to reprocess all data during every execution cycle.
- Implementation of Change Data Capture (CDC), ensuring only changed data is processed and enabling more frequent refresh cycles.
- Elimination of reporting timeouts, dramatically improving responsiveness for customer-facing analytics and reporting services.
- A scalable platform for future growth, capable of accommodating increasing data volumes without requiring another major architectural overhaul.
Why This Matters to Technology Leaders
For CTOs
This project demonstrates how modernizing the processing layer can address technical debt while creating a foundation for long-term scalability. By aligning architecture with workload demands, CTOs can reduce operational risk and avoid recurring performance crises as data volumes grow.
For Directors of Engineering
The result is a more maintainable and extensible platform that engineering teams can operate confidently. Modern pipeline design, distributed processing, and incremental data handling reduce complexity and eliminate the need for ongoing firefighting.
For Product and Support Teams
Perhaps most importantly, the customer-facing symptoms disappear. Faster reporting, reliable data delivery, and the elimination of timeout issues lead to fewer escalations, improved customer satisfaction, and greater confidence in the platform.
Turning Data Platforms into Strategic Assets
By combining HTEC’s data engineering and architecture capabilities with the power of Databricks, this organization transformed a critical performance bottleneck into a scalable, future-ready data platform—delivering tangible business value while creating a foundation for continued growth.
