🔄 Merging Data from Multiple Sources into One Clean Pipeline Using Google Cloud Dataflow

In today's fragmented digital ecosystem, data lives everywhere:

• Meta Ads Manager
• Google Analytics 4
• Jira & Trello
• Figma, Adobe Analytics
• Stripe, HubSpot, LinkedIn Ads

The challenge?

> "We can't make decisions because our data is scattered across 10 different platforms."

At Dezoko, we specialize in building unified data pipelines -- and for one of our clients, we used Google Cloud Dataflow to merge 10+ external data sources into a single, clean dataset in BigQuery.

💡 Use Case: Unified Business Intelligence

Our client -- a multi-brand marketing agency -- wanted:

• ✅Centralized reporting across all client accounts
• ✅Clean, deduplicated, and normalized data
• ✅Near real-time updates
• ✅Unified dashboards (across ad platforms, analytics, and CRMs)

📐 Architecture Overview

+------------------+     +------------------+  
|  Meta API        |     |  Google Ads API  |  
+------------------+     +------------------+  
        ↓                       ↓  
+------------------+     +------------------+  
|  LinkedIn API    |     |  Jira Webhooks   |  
+------------------+     +------------------+  
        ↓                       ↓  
   [Cloud Functions / Cloud Run]  
               ↓  
        [Pub/Sub Topic]  
               ↓  
        [Cloud Dataflow Job]  ← handles parsing, transformation, joins
               ↓  
          [BigQuery Table] ← Final unified schema  
               ↓  
       [Looker Studio Dashboards]

⚙️ Tools We Used

🔄 Step-by-Step: Our Data Merge Strategy

1️⃣ Data Collection via API/Webhooks

We set up:

• Pull-based fetchers (e.g., Meta, LinkedIn, Google Ads)
• Webhooks for push-based sources (e.g., Jira, Trello)
• Cron jobs for periodic data where APIs lacked webhooks
• All payloads pushed to Pub/Sub

2️⃣ Streaming with Cloud Dataflow

We built a streaming pipeline with:

• Source: Pub/Sub subscription
• Transforms:

• Schema mapping to internal standard
• Deduplication using unique keys
• Data enrichment (adding campaign names, client IDs)
• Error handling (dead-letter queues + logs)
• Sink: Merged data into BigQuery unified table

Sample Join Example:

• Match Meta Ad data with Google Analytics session
• Combine Jira ticket tags with Adobe user behavior
• Join Stripe transactions with campaign IDs

3️⃣ Normalization & Schema Enforcement

We created a single standard schema to support cross-source queries.

4️⃣ Real-Time Monitoring + Observability

• Cloud Logging: for fetch failures and API rate issues
• BigQuery Audit Logs: track each load
• Pub/Sub dead-letter: route errors to Firestore for retry
• Slack alerts for pipeline failure or delays

📈 Outcome for the Client

🔐 Enterprise-Ready Features We Implemented

• ✅Token rotation with Secret Manager
• ✅Per-client data partitioning in BigQuery
• ✅IAM roles for audit-level access
• ✅Multi-region support for latency reduction
• ✅End-to-end encryption + logging

💬 What the Client Said

> "We now see everything -- ads, product behavior, and campaigns -- in one place. Your team made our data useful again."

> -- VP of Marketing Analytics

> "Before, we had tools. Now, we have insights."

> -- CEO, Martech SaaS

📞 Want to Merge Your Disconnected Data into Google Cloud?

We help you:

• ✅Ingest from any API, webhook, or file
• ✅Clean + map data with Dataflow
• ✅Store in BigQuery, Firestore, or Cloud Storage
• ✅Monitor + visualize with Looker or custom dashboards
• ✅Automate 100% of the pipeline