🔄 DataFlow ETL Pipeline

Customer Data Integration & Analytics Platform

3
Data Sources
5
Processing Stages
100K
Records/Day
99.9%
Uptime SLA

📊 Business Objectives & End Users

Primary Objective

  • Consolidate customer data from multiple sources
  • Provide unified view for analytics and reporting
  • Enable real-time data-driven decision making
  • Ensure data quality and consistency

End Users

  • Business Analysts (data exploration)
  • Data Scientists (ML model training)
  • Marketing Team (campaign targeting)
  • Customer Success (account insights)
  • Executive Dashboard (KPI monitoring)

Business Value

  • Reduce manual data reconciliation (80% time savings)
  • Improve data accuracy and completeness
  • Enable faster business insights
  • Scale data processing capacity

📥 Data Input Overview

Source 1: CRM API (Salesforce) Format: JSON REST API ~50K records/day Customer profiles Real-time sync Source 2: Orders DB (MySQL) Format: SQL queries ~30K orders/day Transaction data Hourly batch Source 3: CSV Export ~20K records/day Support tickets (S3) ETL Pipeline AWS Lambda + Airflow • Data validation • Schema transformation • Deduplication • Enrichment Data Warehouse (BigQuery) Unified customer view 360° analytics Historical trends ML-ready datasets ✓ GDPR compliant Customer data Order data Support data Processed

⚙️ Data Processing Pipeline

1. Data Ingestion Pull from sources API + SQL + S3 Raw data storage 2. Validation Schema checks Data quality rules Error logging 3. Transformation Normalize formats Map fields Type conversions 4. Deduplication Fuzzy matching Customer ID merge Conflict resolution Master record creation 5. Enrichment Geo-location lookup Industry tagging Score calculations 6. Load Write to warehouse Update indexes Trigger downstream ✓ Complete Pipeline Configuration Orchestration: • Apache Airflow (DAG scheduling) • AWS Lambda (serverless compute) • S3 (intermediate storage) Monitoring: • CloudWatch logs & metrics • PagerDuty alerts

✨ Functional Features

Data Validation

  • JSON schema validation for API data
  • SQL constraint checks for database records
  • Custom business rule engine
  • Automated error notifications

Intelligent Deduplication

  • Fuzzy string matching (Levenshtein distance)
  • Multi-field entity resolution
  • Confidence scoring for matches
  • Manual review queue for uncertain cases

Data Enrichment

  • Geo-location from IP/address
  • Company firmographic data
  • Industry classification
  • Customer lifecycle scoring

🛡️ Non-Functional Features

Performance

  • Processes 100K records in <30 minutes
  • Parallel processing across 10 Lambda workers
  • Optimized SQL queries with indexes
  • Incremental data loading strategy

Reliability

  • 99.9% uptime SLA
  • Automatic retry with exponential backoff
  • Dead-letter queue for failed records
  • Point-in-time recovery capability

Security & Compliance

  • End-to-end encryption (TLS 1.3)
  • GDPR-compliant data handling
  • Role-based access control (RBAC)
  • Audit logging of all data access

🏗️ System Architecture

Layer 1: Data Sources CRM API Salesforce REST OAuth 2.0 Orders DB MySQL 8.0 Read replica CSV Files S3 Bucket Daily exports Layer 2: Processing Airflow DAGs Python 3.11 Orchestration & scheduling Lambda Functions Node.js 20 Data transformations Layer 3: External Services Geo API Location enrichment MaxMind GeoIP2 Clearbit Company data Firmographics API Layer 4: Output & Storage BigQuery Data warehouse Redis Cache Query acceleration Technology Stack Languages & Frameworks: • Python 3.11 (data processing) • Node.js 20 (Lambda functions) • SQL (data queries) AWS Services: • Lambda (serverless compute) • S3 (object storage) • CloudWatch (monitoring) • IAM (access control) Dependencies: • pandas, SQLAlchemy (Python)

🚀 Deployment & Usage

Deployment Model

  • Cloud-hosted (AWS)
  • Serverless architecture
  • Multi-region for redundancy
  • Infrastructure as Code (Terraform)

Prerequisites

  • AWS account with appropriate IAM roles
  • Salesforce API credentials
  • MySQL read replica access
  • BigQuery project setup

Typical Workflow

  • 1. Configure data source connections
  • 2. Deploy Airflow DAGs
  • 3. Run initial backfill
  • 4. Monitor daily incremental runs
  • 5. Query unified data in BigQuery
Data Sources
Processing Logic
External Services
Output/Storage
Data Quality
Enrichment