Consolidating data from multiple sources into a single, governed analytics environment — reducing reporting latency by over 50%.
Our client operates a technology platform that connects brands with advocates to drive authentic engagement at scale. With rapid growth came increasing data complexity — multiple source systems, fragmented reporting, and limited visibility into business performance.
They needed a trusted data foundation that could unify their data landscape, enable self-service analytics, and establish the governance required as the business scaled into new markets.
Four interconnected challenges were limiting the client’s ability to make data-driven decisions at the pace the business demanded.
Critical business data was scattered across multiple operational systems, third-party APIs, and manual spreadsheets with no unified view.
Business teams waited days for reports that should have been available in hours. Manual ETL processes created bottlenecks and inconsistencies.
No row-level security or role-based access meant either over-sharing sensitive data or restricting access entirely — neither option acceptable at scale.
Without a governed data layer, advanced analytics and AI initiatives had no reliable foundation to build upon.
A five-phase delivery spanning architecture design, data engineering, analytics enablement, and governance implementation.
We designed a modern data architecture on AWS and Snowflake, defining the ingestion patterns, transformation layers, and data models that would support both current reporting and future AI workloads.
Automated pipelines were built to ingest data from the client’s operational databases, third-party APIs, and event streams into Snowflake — replacing fragile manual processes.
We implemented a layered transformation approach with raw, curated, and presentation layers — ensuring data quality, consistency, and traceability across the entire pipeline.
Amazon QuickSight dashboards were connected to Snowflake, delivering self-service analytics with governed access. Business teams could explore data independently without waiting for ad-hoc reports.
Row-level security, role-based access controls, and data classification policies were implemented to ensure compliant, secure access as the organisation scaled.
Key technology choices that enabled performance, governance, and scalability.
Central analytics platform with elastic compute, zero-copy cloning for development, and native support for semi-structured data from APIs and event streams.
Explore our Snowflake capabilities →Cloud-native infrastructure leveraging S3 for staging, Lambda for event-driven processing, and IAM for secure cross-service authentication.
Self-service business intelligence connected directly to Snowflake, with embedded dashboards, row-level security, and scheduled report distribution.
Reporting latency reduced by over 50%, with business teams accessing dashboards in hours rather than waiting days for manual reports.
Row-level security and role-based controls enabled teams to access exactly the data they need — no more, no less — with full audit trails.
The platform was designed to scale with the business, supporting new data sources, additional teams, and future AI workloads without re-architecture.
With clean, governed data in Snowflake, the client now has the foundation to pursue advanced analytics and machine learning initiatives with confidence.
“Pravidha delivered an end-to-end data platform across AWS and Snowflake, consolidating data from multiple sources into a single, governed analytics environment. The solution reduced reporting latency by over 50% and enabled secure access to data through row-level security, while establishing a scalable foundation for advanced analytics and future AI initiatives.”
Let’s discuss how a governed data platform can accelerate your analytics and AI journey.
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