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Modern Marketing Multi-Channel ETL Platform

Architecture Level 1

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Modern Marketing Multi-Channel ETL High-Level-Overview

Architecture Level 2

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Modern Marketing Multi-Channel ETL Detailed Dataflow

Architecture Level 3

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Modern Marketing Multi-Channel ETL Detailed Breakdown

Modern Marketing Multi-Channel ETL Platform

1. Executive Summary

The Modern Marketing Multi-Channel ETL Platform is an end-to-end ELT solution that unifies data from Facebook Ads, Google Ads, and HubSpot into Snowflake for centralized analytics. It uses Airbyte and Fivetran for extraction and loading raw data into Snowflake staging schemas, and dbt for building incremental fact and dimension models. The platform supports up to ~10GB of daily data, includes CI/CD and automated tests, and was delivered over 7 months, on time, with a scalable and maintainable architecture. This enables marketing teams to get unified, near real-time insights across channels, improving campaign performance and ROI.

2. Architecture Overview

The platform follows a modern data stack, ELT-first architecture:

Sources: Facebook Ads API, Google Ads API, HubSpot API.

Extraction Layer: Airbyte / Fivetran connectors pull data from APIs.

Loading Layer: Raw data synced directly into Snowflake raw_staging schema.

Transformation Layer: dbt models transform raw tables into incremental facts and dimensions in transformed schemas.

Consumption Layer: BI tools like Tableau connect to Snowflake for dashboards and reports.

Connectors are scheduled hourly/daily, and dbt transforms run post-load to keep analytics up to date.

3. Technology Stack

  • Extraction / Ingestion: Airbyte (open-source) and Fivetran (managed)
  • Data Warehouse: Snowflake (cloud data warehouse)
  • Transformation / Modeling: dbt (with dbt-snowflake adapter)
  • Orchestration & CI/CD: Git + GitHub Actions / GitLab CI
  • Snowflake Features: Streams/Tasks, Multiple Schemas (raw_staging, transformed)
  • Clustering: Strategies for partitioning and performance tuning

4. Data & Warehousing Model

Raw Staging Schema (Snowflake): Tables like raw_staging.facebook_ads_insights, google_ads_campaigns, hubspot_deals. Light normalization from Airbyte/Fivetran (JSON flattening).

Transformed Schema (Snowflake): Fact Tables (e.g. fact_campaign_performance for daily impressions/clicks/spend) and Dimension Tables (users, campaigns, channels, etc.) with SCD Type 2 support.

Modeling Approach: Pure ELT (Raw in Snowflake, transformation fully in-warehouse via dbt). Incremental models using is_incremental() and unique_key configurations.

5. ETL / ELT Processing

Extract & Load (EL): Airbyte/Fivetran connectors pull data from Facebook, Google, and HubSpot APIs directly into Snowflake’s staging schema on a scheduled basis (daily/hourly).

Transform (T): dbt models build incremental facts and dimensions. SQL models enforce business rules and unify metrics across disparate channels.

Data Quality & Testing: dbt tests (not_null, unique, relationships) ensure accuracy and consistency before deployment.

6. Project Timeline (7 Months)

Project Start: April 1, 2025 | Duration: ~7 months (Delivered on time)

  • Apr 1 – Apr 15, 2025 — Kickoff: Requirements and tool setup (Snowflake, dbt, etc.).
  • Apr 16 – May 1, 2025 — Design: Architecture finalization and schema planning.
  • May 2 – May 31, 2025 — Extraction: Airbyte/Fivetran connector configuration.
  • Jun 1 – Jun 30, 2025 — Loading: Snowflake staging and basic normalization.
  • Jul 1 – Aug 15, 2025 — Transformation: dbt models (facts, dimensions, incremental logic).
  • Aug 16 – Sep 30, 2025 — CI/CD Automation: Git-based workflows and dbt tests.
  • Oct 1 – Nov 13, 2025 — Testing & Deployment: Performance validation and production rollout.

7. Testing & Deployment

Testing: dbt schema tests (unique, relationship, etc.); Integration tests via sync mocks; Performance tests ensuring daily runs under ~30 minutes with 99.9% accuracy.

Deployment: dbt runs triggered via GitHub Actions/CI on merges to main. Environments (dev/staging/prod) managed via Snowflake schemas and dbt targets.

8. Monitoring & Maintenance

Monitoring: Snowflake query history, warehouse usage reviews, dbt run logs, and CI/CD pipeline status alerts.

Maintenance: Weekly model reviews, annual audits, and schema change handling via Fivetran’s auto-schema capabilities and dbt refactors. Estimated Cost: ~$400/month.

9. Roles & Responsibilities

Methodology: Agile with 2-week sprints, focusing on iterative ELT builds.

  • 🚀 Data Engineer Lead (1): Architecture, Snowflake design, dbt project structure.
  • ⚙️ Developers (2): Connectors, dbt models, and CI/CD pipelines.
  • 📈 Marketing Analyst (1): Metric validation, KPI definition, and BI logic support.