Unified Cross-Platform Intelligence Across 20+ Data Sources

Fragmented Data Across 20+ Platforms

Campaign performance, CRM records, email engagement, webinar registrations, LMS completions, and form submissions lived across separate platforms including Facebook Ads, HubSpot, Mailchimp, Dotdigital, Zoom, YouTube, Google Ads, LinkedIn, Jotform, SurveyMonkey, SendGrid, and a Moodle-based LMS - making unified reporting and attribution extremely difficult.

No Centralized Reporting Infrastructure

The client had no centralized warehouse, no ETL pipelines, and no staging architecture. Every report required manual effort, and cross-platform metrics could not be reliably combined or compared.

API Complexity and Platform Limitations

Each platform exposed data differently, some through native connectors, others through undocumented or inconsistent HTTP APIs. Several platforms introduced additional complexity through API limits, OAuth issues, and unreliable pagination behavior.

CRM Scale and Data Integrity

The HubSpot environment contained over hundreds of thousands of activity records across calls, emails, meetings, tasks, notes, and SMS requiring carefully designed backfills, date-range splits, and deduplication logic to ensure accurate ingestion at scale.

Nested LMS API Workloads

The Moodle-based LMS required deeply nested API execution patterns (courses → enrolled users → completion status per user). This workload exceeded the practical limits of low-code orchestration alone and required a cloud-native compute approach.

01.

Make.com Orchestration Across 20+ Platforms

For the majority of platforms, NeenOpal implemented Make.com scenarios using native and HTTP modules to orchestrate automated weekly ingestion. Raw JSON payloads are stored directly in BigQuery, while staging views handle parsing, type casting, deduplication, and business transformations.

02.

Cloud Run for Complex Nested APIs

For the LMS integration, NeenOpal implemented a Python-based Google Cloud Run service to manage nested API execution, pagination, progressive inserts, BigQuery deduplication checks, and timeout handling. This eliminated excessive Make.com credit consumption while maintaining scalability and reliability.

03.

HubSpot CRM

NeenOpal built end-to-end ingestion pipelines across all major HubSpot objects including Contacts, Companies, Deals, Calls, Emails, Meetings, Tasks, Notes, and SMS along with association tables linking CRM entities together for downstream analytics.

04.

SCD Type 2 for Deal Stage Tracking

To preserve historical CRM state changes, NeenOpal implemented Slowly Changing Dimension Type 2 (SCD2) logic for deal stage tracking. This maintained a complete audit history of every deal movement using valid_from, valid_to, and is_current logic.

05.

Consistent Raw → Staging → Mart Architecture

Every platform follows the same three-layer design: Raw tables retain immutable JSON payloads Staging views standardize, deduplicate, and transform source data Mart views combine platforms into reporting-ready business datasets

06.

Platform-Specific Engineering Solutions

Several complex engineering challenges had to be addressed across the platform ecosystem, including: HubSpot 10k record API limitations Zoom Events broken pagination behavior YouTube Analytics OAuth account mismatches Deeply nested Moodle LMS API execution patterns LinkedIn Ads native analytics limitations Multi-account Google Ads ingestion complexity Inconsistent JSON payload structures across platforms

Unified marketing intelligence across 20+ platforms

Consolidated data from more than 20 marketing, CRM, webinar, and engagement platforms into a single source of truth. This enabled teams to analyze performance and customer interactions holistically instead of working with isolated datasets.

Fully automated reporting workflows

Eliminated manual exports and repetitive reporting activities through automated ingestion and transformation pipelines. Weekly data refreshes ensured stakeholders always had access to timely and reliable insights.

Centralized BigQuery Warehouse Replacing Fragmented Spreadsheets

Replaced scattered spreadsheets and disconnected reports with a centralized Google BigQuery warehouse. This provided a scalable foundation for consistent data management and simplified access to business metrics.

Scalable Raw → Staging → Mart Architecture for Reliable Analytics

Implemented a structured multi-layer architecture that separates raw ingestion, transformations, and reporting datasets. This improved data quality, maintainability, and ensured analytics remained reliable as data volumes grew.

Complete CRM History with SCD Type 2 Deal Tracking

Introduced SCD Type 2 logic to preserve every change in deal stages and maintain a full historical record. This enabled accurate trend analysis and complete visibility into the customer lifecycle.

Cross-Platform Attribution and Engagement Visibility

Connected campaign, CRM, webinar, email, and learning platform data to provide end-to-end visibility into customer engagement. Teams could better understand how interactions across channels contributed to marketing outcomes.

Foundation Established for Future Expansion and Advanced Analytics

Built a cloud-native and extensible data platform capable of supporting additional integrations and evolving business requirements. The architecture provides a strong foundation for advanced analytics, attribution models, and AI-driven insights.