# 🔄 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

## ⚙️ Data Processing Pipeline

## ✨ 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

## 🚀 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