90% Faster Data Preparation: Building a Google BigQuery Marketing Data Warehouse for 3.4 Billion Records
Challenge
Siphon Cloud managed 3.4 billion marketing records across fragmented MySQL environments, making data retrieval labor-intensive. Manually extracting and preparing datasets for media agencies created operational friction and reporting delays. As data volumes accelerated, the lack of a centralized architecture increased latency and made it difficult to deliver the near real-time insights required for campaign optimization and partner reporting.
Solution
Epoc Labs architected a scalable data warehouse on Google BigQuery, consolidating multiple MySQL environments into a unified analytics foundation. An automated ELT pipeline utilizing delta-change logic was implemented to integrate directly with Siphon Cloud’s proprietary marketing platform, enabling continuous synchronization of campaign data at scale.
Impact
Siphon Cloud reduced data preparation time by more than 90%, eliminating most of the manual extraction and transformation tasks. Reporting latency dropped from hours to minutes, enabling the platform to deliver campaign insights to media partners 60% faster. By establishing a single source of truth within BigQuery, Siphon Cloud can now access high-fidelity marketing performance data at scale, ensuring consistent reporting accuracy while strengthening trust with global agency partners.
Siphon Cloud transforms campaign reporting with BigQuery
Siphon Cloud, a marketing technology company specializing in multi-channel advertising analytics, processes billions of records to support campaign performance for global media agencies. Operating at this scale requires precise, high-speed data delivery. However, reliance on traditional MySQL environments created increasing operational bottlenecks as marketing data volumes expanded.
Preparing agency-ready reporting required manual extraction and reconciliation across multiple systems. This fragmented workflow slowed query performance, introduced inconsistencies between datasets, and limited the organization’s ability to deliver timely insights to media partners. Epoc Labs addressed these challenges by engineering a BigQuery-powered data platform that automates the ingestion, standardization, and delivery of marketing data across the organization at scale.
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Industry
Marketing Technology / Data Infrastructure
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Departments Using App Data
Marketing, Media Agencies
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Engagement Model
Long-term Consulting & Implementation
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Project Type / Services Offered
BigQuery Data Warehouse, ETLs, Custom Reporting
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About
Siphon Cloud is a marketing technology company specializing in data-driven campaign management. It manages over 3.4 billion marketing records and partners with leading media agencies worldwide to deliver real-time performance insights.
“Our teams can access accurate marketing performance reports in minutes instead of hours. It has completely changed how quickly we can deliver insights to our agency partners.”
Richard Seppala
Chief Data Officer
Standardizing Campaign Data Across Every Source
Epoc Labs restructured Siphon Cloud’s legacy MySQL architecture, identifying inefficiencies stemming from duplicate tables, inconsistent schemas, and fragmented data pipelines. All marketing datasets were consolidated into a unified BigQuery schema with standardized field definitions to ensure consistency across campaigns and reporting layers.
The architecture leverages BigQuery’s distributed compute model, partitioned storage strategy, and optimized query execution to process billions of marketing records efficiently while maintaining predictable performance for analytical workloads.
Delta-change pipelines ensure only new or modified records are processed.
Every dataset can now be traced from raw ingestion through final partner reporting with consistent and governed logic.
Delivering Near Real-Time Campaign Intelligence
Epoc Labs implemented a direct integration between BigQuery and Siphon Cloud’s proprietary marketing platform. This architecture enables campaign results and performance metrics to be surfaced through automated dashboards and application interfaces without manual queries.
Users can now analyze campaign performance by platform, client, or time period in near real time without manual queries. Governance policies ensure that data access remains secure while maintaining high query performance. By establishing a shared source of truth, the platform eliminates version conflicts and ensures every marketing record remains fully traceable for operational reporting, partner review, and internal auditing.
“We’ve eliminated the manual steps that used to slow down our reporting. The process is now faster, cleaner, and far more reliable.”
Richard Seppala
Chief Data Officer
Achieving Operational Impact
Modernizing Siphon Cloud’s data infrastructure significantly improved operational efficiency and reporting reliability:
- 90% reduction in data prep time through automated pipelines.
- 60% faster delivery of campaign insights to global media partners.
- Centralization of 3.4 billion marketing records into a scalable analytics platform.
- Complete elimination of manual extraction workflows previously required for reporting.
The platform now provides a scalable analytics foundation that enables Siphon Cloud to onboard new marketing data sources without redesigning its reporting infrastructure.
Establishing a data governance standard
By combining Dynamics 365, Dayforce, SQL Server, and DOMO Magic ETL, System Pavers created an auditable, governed data structure for its financial operations. Data updates are automated, and every user interaction is logged. The system supports secure role-based permissions and a transparent payout process, setting a higher bar for accuracy, accountability, and trust.
“We finally have a single, trusted view of commissions across every branch. The process is faster, cleaner, and fully traceable.”
Ahmad Roowala
Chief Technology Office
“The warehouse fundamentally changed how we operate. Data access is immediate, reports are consistent, and our teams finally work from a single version of the truth.”
Richard Seppala
Chief Data Officer
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