Ecosystem Integration¶

System Ecosystem¶

The FFG Pipeline operates within a broader data ecosystem, integrating with multiple systems and serving various stakeholders.

Integration Architecture¶

graph TB
    subgraph "Upstream Systems"
        A1[SAP ERP]
        A2[Retail Partners]
        A3[MDM System]
        A4[Price Management]
    end

    subgraph "FFG Pipeline"
        B1[Data Ingestion]
        B2[Processing Engine]
        B3[Data Lake]
    end

    subgraph "Downstream Systems"
        C1[Power BI]
        C2[Planning Systems]
        C3[Reporting Tools]
        C4[Analytics Platform]
    end

    subgraph "Support Systems"
        D1[Azure Key Vault]
        D2[Power Automate]
        D3[Monitoring]
        D4[Alerting]
    end

    A1 --> B1
    A2 --> B1
    A3 --> B1
    A4 --> B1

    B2 --> B3
    B3 --> C1
    B3 --> C2
    B3 --> C3
    B3 --> C4

    D1 -.-> B2
    D2 -.-> B2
    D3 -.-> B2
    D4 -.-> B2

Upstream Systems¶

SAP ERP System¶

Integration Type: Direct SQL Query

Tables: pl_global (profit & loss data)
Frequency: Daily extraction
Data Volume: ~10M records/month
Key Fields: posting_date, material_key, amount_lc

Retail Partner Systems¶

Integration Type: File Upload

Format: Excel files (Coty_DSA_Price_Promo_Data.xlsx)
Delivery: Manual/automated upload to Azure
Frequency: Weekly/monthly
Validation: Schema validation, data quality checks

Master Data Management (MDM)¶

Integration Type: SQL Database Views

Connected MDM domains: - Material Master: Product attributes - Customer Master: Customer hierarchies - Brand Hierarchies: House/Brand relationships - Geographic Data: Reporting units, regions

Price Management System¶

Integration Type: File Upload

RSP Database: Recommended selling prices
Floor Prices: Minimum price thresholds
Update Frequency: Monthly
Currency: Multi-currency with FX conversion

Core Pipeline Components¶

Azure Blob Storage¶

Role: Primary data staging area

Structure:

domain-finance-ffg/
├── mapping_files/
│   ├── incoming/
│   ├── processed/
│   └── archive/
├── sellout_data/
│   ├── incoming/
│   ├── processed/
│   └── archive/
├── rsp_database/
│   ├── incoming/
│   ├── processed/
│   └── archive/
└── logs/
    └── refresh_completed/

Databricks Platform¶

Role: Processing engine and data lakehouse

Components: - Spark Clusters: Auto-scaling compute - SQL Warehouse: Query engine - Delta Lake: Storage layer - Jobs: Scheduled orchestration

SQL Data Warehouse¶

Role: Analytical data store

Schemas: - dm_finance_ffg: FFG-specific tables - gold_finance: Enterprise finance data - gold_mdm: Master data

Downstream Systems¶

Power BI¶

Integration: Direct Query / Import

Datasets: - Sales Performance Dashboards - Price Corridor Analysis - Ventilation Reports - KPI Scorecards

Connection: - SQL endpoint for live queries - Scheduled refresh for imported data - Row-level security implementation

Planning & Forecasting¶

Integration: Table exports

Use cases: - Demand planning inputs - Price optimization - Promotional planning - Budget allocation

Reporting Tools¶

Integration: SQL views and APIs

Reports: - Financial statements - Sales analytics - Market performance - Competitive analysis

Advanced Analytics¶

Integration: Data science workspace

Applications: - Predictive modeling - Anomaly detection - Trend analysis - Optimization algorithms

Support Systems¶

Azure Key Vault¶

Role: Secrets management

Stored credentials: - Service principal credentials - Database connection strings - API keys - Storage account keys

Integration:

scope = "azurekv-scope"
client_id = dbutils.secrets.get(scope, "sp-client-id")

Power Automate¶

Role: Workflow automation

Workflows: - Pipeline completion notifications - Error alerting - File arrival triggers - Report distribution

Webhook integration:

webhook_url = config.POWER_AUTOMATE_WEBHOOK_URL
payload = {"status": "completed", "timestamp": timestamp}
response = requests.post(webhook_url, json=payload)

Monitoring & Logging¶

Application Insights¶

Performance metrics
Error tracking
Custom events
Dependency mapping

Log Analytics¶

Centralized logging
Query capabilities
Alert rules
Dashboard creation

Alerting System¶

Alert channels: - Email notifications - Slack integration (#ffg-pipeline) - Teams notifications - PagerDuty (critical)

Alert types: - Pipeline failures - Data quality issues - Performance degradation - SLA breaches

Data Governance¶

Data Catalog¶

Tool: Azure Purview

Capabilities: - Data discovery - Lineage tracking - Classification - Glossary management

Access Control¶

Implementation: RBAC + ACLs

Levels: - Storage account (Azure RBAC) - Database/Schema (Databricks) - Table/Column (Fine-grained) - Row-level (Power BI)

Compliance¶

Standards: GDPR, SOX

Controls: - Data encryption - Audit logging - Retention policies - PII handling

External Integrations¶

Cloud Services¶

Azure Active Directory: Authentication
Azure Monitor: Infrastructure monitoring
Azure DevOps: CI/CD pipelines
Azure Cost Management: Usage tracking

Third-Party Tools¶

Git: Version control
JIRA: Issue tracking
Confluence: Documentation
ServiceNow: Incident management

Integration Patterns¶

File-Based Integration¶

# Pattern for file processing
def process_incoming_file(file_path):
    df = load_file(file_path)
    df_processed = transform(df)
    write_to_lake(df_processed)
    archive_file(file_path)

API Integration¶

# Pattern for API calls
def fetch_from_api(endpoint, params):
    response = requests.get(endpoint, params=params)
    data = response.json()
    return spark.createDataFrame(data)

Database Integration¶

# Pattern for database queries
def query_database(query):
    df = spark.sql(query)
    return df.cache()