Skip to content

Data Pipelines and ETL

This document outlines data pipeline patterns, including ETL/ELT around ConnectSoft systems. It is written for data engineers, architects, and platform owners building data pipelines for analytics and reporting.

ConnectSoft platforms generate data that flows to data warehouses, analytics platforms, and BI tools. This document describes patterns for building reliable, scalable data pipelines.

Note

Data pipelines are critical for analytics, reporting, and compliance. These patterns ensure data quality, reliability, and governance.

Typical Data Flows

Platform → Data Warehouse → BI Tools

Flow: 1. Platforms generate events and data 2. ETL pipeline extracts data 3. Data is transformed and loaded into data warehouse 4. BI tools query data warehouse for reporting

Examples: - Audit events → Data warehouse → Compliance reports - Identity events → Data warehouse → User analytics - Config changes → Data warehouse → Change tracking

Real-Time Streaming Analytics

Flow: 1. Platforms emit events to event bus 2. Stream processing consumes events 3. Real-time analytics and dashboards 4. Alerts and notifications

Examples: - User authentication events → Real-time dashboards - Bot conversations → Real-time support metrics - Config changes → Real-time monitoring

ETL vs ELT in ConnectSoft

ETL (Extract, Transform, Load)

Process: 1. Extract data from source systems 2. Transform data in processing layer 3. Load transformed data into destination

When to Use: - Complex transformations required - Data quality issues need fixing - Multiple sources need normalization - Compliance and governance requirements

Pros: - Data quality control - Complex transformations - Governance and compliance

Cons: - More complex infrastructure - Slower time to insights - Higher processing costs

ELT (Extract, Load, Transform)

Process: 1. Extract data from source systems 2. Load raw data into destination 3. Transform data in destination (data warehouse)

When to Use: - Simple transformations - Fast time to insights - Modern data warehouses (Snowflake, BigQuery, etc.) - Raw data preservation needed

Pros: - Faster time to insights - Simpler infrastructure - Raw data preservation

Cons: - Limited transformation capabilities - Data quality issues in destination - Higher storage costs

Tip

ConnectSoft Position: Prefer ELT for modern data warehouses, ETL for complex transformations and compliance requirements. Most ConnectSoft pipelines use ELT patterns with transformation in the data warehouse.

Designing Pipelines Around Platforms

Audit Platform Data Pipeline

Data Sources: - Audit events (user actions, system events) - Audit logs (structured logs) - Compliance events (retention, alerts)

Pipeline Design: - Extract audit events via API or event bus - Load raw events into data warehouse - Transform for compliance and analytics - Load into analytics tables

Use Cases: - Compliance reporting - Security analytics - User behavior analysis - Audit trail queries

Identity Platform Data Pipeline

Data Sources: - Authentication events - User registration events - Token issuance events - External IdP events

Pipeline Design: - Extract identity events via API or event bus - Load raw events into data warehouse - Transform for user analytics - Load into analytics tables

Use Cases: - User analytics - Authentication metrics - Security monitoring - User behavior analysis

Config Platform Data Pipeline

Data Sources: - Configuration changes - Feature flag changes - Tenant settings changes - Configuration snapshots

Pipeline Design: - Extract config changes via API or webhooks - Load raw changes into data warehouse - Transform for change tracking - Load into analytics tables

Use Cases: - Change tracking - Feature adoption metrics - Configuration analytics - Compliance reporting

Governance and Data Quality

Idempotency

  • Idempotency Keys - Use unique keys for pipeline runs
  • Deduplication - Remove duplicate records
  • Idempotent Loads - Ensure loads can be safely retried

Deduplication

  • Event IDs - Use unique event IDs
  • Timestamp + Source - Combine timestamp and source for uniqueness
  • Hash-Based - Use hash of key fields for deduplication

Schema Evolution

  • Schema Versioning - Version schemas for compatibility
  • Backward Compatibility - Maintain backward compatibility
  • Schema Registry - Use schema registry for schema management
  • Migration Scripts - Migrate data when schemas change

Data Quality Checks

  • Completeness - Ensure required fields are present
  • Validity - Validate data formats and ranges
  • Consistency - Check data consistency across sources
  • Timeliness - Ensure data freshness

Error Handling

  • Dead Letter Queues - Store failed records
  • Retry Logic - Retry failed operations
  • Alerting - Alert on data quality issues
  • Manual Review - Review and fix data quality issues

Example Blueprint

Audit Events → Data Warehouse Pipeline

flowchart LR
    A[Audit Platform] -->|Events| B[Event Bus]
    B --> C[ETL Service]
    C --> D[Transform]
    D --> E[Data Warehouse]
    E --> F[BI Tools]

    G[API] -->|Batch Extract| C

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#e8f5e9
    style D fill:#fff4e1
    style E fill:#f3e5f5
    style F fill:#e8f5e9
Hold "Alt" / "Option" to enable pan & zoom

Implementation Steps

  1. Event Consumption - Consume audit events from event bus
  2. Batch Extraction - Periodically extract historical data via API
  3. Transformation - Transform events for analytics
  4. Load - Load transformed data into data warehouse
  5. Quality Checks - Run data quality checks
  6. BI Integration - Expose data to BI tools

Key Considerations

  • Incremental Loads - Load only new/changed data
  • Partitioning - Partition data by date/tenant
  • Indexing - Index frequently queried fields
  • Retention - Implement data retention policies
  • Compliance - Ensure compliance with data regulations

Tip

Best Practices: 1. Incremental Loads - Load only new/changed data for efficiency 2. Idempotent Processing - Ensure pipelines can be safely retried 3. Data Quality Checks - Validate data quality at each stage 4. Monitoring - Monitor pipeline health and performance 5. Governance - Implement data governance and compliance