Accelerating Data Onboarding with a Scalable Platform for Diverse Vendors

Success Story

A fast-growing data distributor faced mounting pressure to aggregate information from multiple third-party vendors whose datasets varied widely in format, quality, and volume.

Historically reliant on legacy systems and ad hoc integrations, the organisation struggled to keep up with an ever-expanding pool of data providers. This complexity not only slowed time-to-market for new vendor onboarding but also led to resource inefficiencies in the cloud infrastructure.

Recognising the need for a more flexible and resilient solution, the distributor decided to build a modern data platform capable of agile ingestion, dynamic reconfiguration, and machine learning (ML) capabilities.In addition to streamlining ingestion, the business aimed to extend its revenue streams by offering configurable data exposure to its enterprise clients. Sales and business development teams required a user-friendly way to define precisely what data segments were accessible for each client, while the technical teams demanded a robust back-end capable of maintaining performance and data integrity at scale.

41% faster onboarding

32% reduction in cloud resource

Increased data pipeline throughput by 35%

Challenges

The distributor’s existing setup struggled under the weight of disparate data sources. Files arrived in a variety of formats—ranging from JSON to XML—and the quality of each dataset varied considerably. Legacy infrastructure further complicated matters, creating a tangle of systems with overlapping functions and inefficient resource usage. This patchwork environment left little room for agility, an essential trait for a business attempting to rapidly onboard new data providers.

At the same time, the organisation sought to capitalise on emerging sales opportunities by offering diversified monetisation models. Clients demanded tailored data feeds that could evolve quickly in response to market shifts. Relying on static, hard-coded configurations risked losing out on lucrative deals, as potential customers required both reliable data and the flexibility to adapt feeds in near real time. Finally, the steep cost of poorly tuned cloud services placed additional pressure on the technical team to find more efficient operational strategies.

Our Solution

Leveraging AWS services in conjunction with GraphQL (using an Apollo Server layer) allowed for flexible data queries and customised data exposure. This architecture supported the rapid creation of dynamic feeds, empowering the business development unit to specify which data slices were available to each client segment.

Crucially, the new platform’s codebase emphasised maintainability and efficient workflows. Automated CI/CD pipelines kept deployment cycles short, mitigating downtime and accelerating feature rollouts. By closely monitoring infrastructure usage, the team uncovered opportunities for cost optimisation—reducing unneeded compute instances and storage overhead.

With an AI and ML layer now in place, the distributor also gained deeper insights into data usage patterns, enabling predictive analytics to fine-tune resource allocation and anticipate future scaling needs.

Leveraging AWS services in conjunction with GraphQL (using an Apollo Server layer) allowed for flexible data queries and customised data exposure.

This architecture supported the rapid creation of dynamic feeds, empowering the business development unit to specify which data slices were available to each client segment.

Results & Business Impact

Significant Reduction in Cloud Costs
A newly optimised infrastructure and intelligent resource allocation strategies led to around 30% lower cloud spending, freeing budget for further innovation.
Faster, More Reliable Data Onboarding
Agile ETL workflows and improved vendor integrations accelerated the onboarding of new data sources by 40%, allowing for quicker revenue capture.
Enhanced Data Quality and Flexibility
A combination of automated checks, manual override portals, and Lakehouse structuring ensured that data remained both trustworthy and easy to query, even as the volume of incoming information rose.
Increased Throughput for ML Tasks
Thanks to Databricks and an AI-ready infrastructure, pipeline throughput improved by 35%, enabling more sophisticated analytics and data-driven insights.
Adaptable Monetisation Models
Fine-grained control over data exposure allowed sales teams to customise offerings for each client, expanding the distributor’s customer base and revenue opportunities.

Looking Ahead

Buoyed by the success of its new data platform, the distributor plans to deepen its investment in machine learning to provide predictive data quality assessments and anomaly detection. Additional enhancements to the portal for manual validations are also on the roadmap, aiming to provide an even more user-friendly interface for data stewards and business analysts.

Further refinements in resource optimisation, including serverless architectures and more granular load balancing, are under consideration to maintain a cost-effective operation at scale. By continually refining how data is ingested, processed, and monetised, the organisation is poised to maintain a competitive edge in a rapidly evolving market, providing ever more timely and actionable datasets to an expanding roster of clients.