Case Study

AVEVA PI to Snowflake Integration

Architected and developed a containerized data pipeline to bring operational time-series data from AVEVA PI historian into Snowflake data warehouse, enabling near real-time KPI reporting and analytics for data analysts, engineers, operations, and management.

Data Engineering AVEVA PI Snowflake C# / .NET Core Docker Fivetran

Executive Snapshot

Objective
Bridge operational technology (OT) and information technology (IT) by bringing AVEVA PI historian data into Snowflake for enterprise-wide KPI reporting and analytics.
My Role
Solutions Architect & Developer. Designed architecture, developed C# containerized integration service, and wrote SQL transformations for data normalization and Snowflake staging.
Duration
6 months (design → production)
Scope
AVEVA PI → SQL Server → Snowflake • Near real-time data pipeline
Architecture
PI AF SDK → C# Service (Docker) → SQL Server → Fivetran → Snowflake
AF SDK / Web API ETL / ELT SQL Transformations TLS Encryption Cloud Data Warehouse

Outcome

  • Enabled near real-time KPI reporting from operational data for decision-makers across the enterprise.
  • Improved data accessibility by bringing OT data into a cloud-based analytics platform accessible by analysts, engineers, and management.
  • Reduced reporting latency from hours/days to minutes through automated data pipelines.
  • Established scalable foundation for advanced analytics, machine learning, and operational intelligence.
Decisions could be made more quickly and reliably due to this architecture bridging OT and IT data silos.

Problem

Operational data critical for KPI reporting and business intelligence was locked in the AVEVA PI historian system, isolated from enterprise analytics tools. Data analysts, engineers, and management lacked timely access to this operational time-series data for reporting, trending, and decision-making. Manual data exports were time-consuming, error-prone, and couldn't scale to support near real-time analytics requirements.

Solution

  • Architected a multi-tier data pipeline connecting AVEVA PI to Snowflake through SQL Server as an operational data store.
  • Developed a containerized C# .NET Core integration service using AF SDK/Web API to read PI data, transform/aggregate it, and bulk insert into SQL Server.
  • Implemented SQL transformations to normalize operational data and prepare it for analytics consumption.
  • Configured Fivetran cloud connector for automated replication from SQL Server to Snowflake staging environment.
  • Built Snowflake staging-to-warehouse transformations for EVENT data warehouse tables optimized for BI tools.
  • Deployed using Docker containers for scalability, portability, and easy maintenance.

Architecture & Data Flow

┌────────────────────────────────────────────────────┐ │ PI Data Archive + AF │ │ - Collectives, AF, Analytics │ └────────────────┬───────────────────────────────────┘ │ (HTTPS / AF SDK) C# .NET Core Integration Service (Docker) • Reads PI via AF SDK / Web API • Transforms & aggregates data • Bulk inserts into SQL │ (ODBC / ADO.NET) ┌────────────────▼───────────────────────────────────┐ │ Enterprise SQL Server │ └────────────────┬───────────────────────────────────┘ │ Fivetran Cloud Connector HTTPS + JDBC (TLS) ┌────────────────▼───────────────────────────────────┐ │ Snowflake (COPY INTO / staging) │ └────────────────────────────────────────────────────┘
Data Flow Narrative
  1. Source Layer: AVEVA PI Data Archive and Asset Framework (AF) store time-series operational data from PLCs, SCADA systems, and instrumentation across facilities.
  2. Integration Layer: Custom C# .NET Core microservice deployed as Docker container connects to PI using AF SDK and Web API over HTTPS. The service reads time-series data, applies transformations and aggregations, then performs bulk inserts into SQL Server using ADO.NET/ODBC.
  3. Operational Data Store: Enterprise SQL Server acts as an intermediate staging layer, normalizing operational data and preparing it for enterprise consumption.
  4. Replication Layer: Fivetran cloud connector automates data replication from SQL Server to Snowflake using JDBC over TLS-encrypted connections, ensuring secure and reliable data transfer.
  5. Analytics Layer: Snowflake receives data via COPY INTO commands into staging tables, where SQL transformations reshape and load data into EVENT data warehouse tables optimized for BI reporting and analytics.
Architecture follows enterprise data integration best practices with layered approach: extract (PI), transform (C#/SQL), load (Snowflake), separating concerns for maintainability and scalability.

Technology Stack

Technology Purpose Implementation Details
AVEVA PI System Operational Historian PI Data Archive with Asset Framework (AF) storing time-series data from production operations, Collectives for high availability, and Analytics for calculations.
C# / .NET Core Integration Microservice Custom-built containerized service using AF SDK and Web API to extract, transform, and load operational data with configurable aggregation windows and error handling.
SQL Server Operational Data Store Enterprise-grade relational database serving as intermediate staging layer with stored procedures for data normalization and transformation logic.
Fivetran Cloud ELT Platform Automated CDC/ELT pipeline replicating data from SQL Server to Snowflake with schema drift detection, incremental updates, and monitoring.
Snowflake Cloud Data Warehouse Scalable analytics platform hosting EVENT data warehouse tables with optimized schemas for Power BI, Tableau, and custom analytics workloads.
Docker Containerization Packaging and deployment platform ensuring consistent runtime environment, easy scaling, and simplified maintenance across environments.
TLS / HTTPS Security Protocols End-to-end encryption for all data in transit: PI AF SDK over HTTPS, SQL Server connections with TLS, Fivetran-Snowflake over TLS.

Integration Implementation Details

AVEVA PI → SQL Server (C# Integration Service)
  • Data Extraction: AF SDK and Web API for querying time-series data with configurable time ranges and sampling intervals
  • Transformation Logic: In-memory data aggregation, outlier filtering, unit conversions, and data quality checks
  • Bulk Loading: ADO.NET bulk insert operations with batching, connection pooling, and transaction management
  • Error Handling: Retry logic, dead letter queues, logging, and alerting for data pipeline failures
  • Performance: Parallel processing, asynchronous operations, and optimized memory management
  • Deployment: Docker container with health checks, resource limits, and orchestration via Docker Compose
SQL Server → Snowflake (Fivetran ELT Pipeline)
  • CDC Replication: Fivetran captures changes from SQL Server transaction log for near real-time sync
  • Schema Management: Automatic detection and replication of schema changes (new columns, tables)
  • Incremental Updates: Efficient incremental data loading based on watermarks and change tracking
  • Data Staging: Initial load into Snowflake staging schema before transformation to warehouse
  • SQL Transformations: Snowflake stored procedures and tasks for staging-to-warehouse ETL
  • Security: TLS-encrypted connections, credential management, and role-based access control

Value Added to Operations

Business Impact
  • Data Democratization: Made operational data accessible to data analysts, engineers, and management through familiar BI tools
  • Decision Velocity: Enabled faster, data-driven decisions with near real-time KPI visibility
  • Operational Intelligence: Unlocked advanced analytics capabilities including trend analysis, anomaly detection, and predictive maintenance
  • Cost Optimization: Eliminated manual data exports and reduced reliance on specialized PI System access
  • Scalability: Cloud-based architecture supports growing data volumes and user base without infrastructure constraints
Technical Excellence
  • OT/IT Convergence: Successfully bridged operational technology and information technology data silos
  • Data Quality: Implemented validation, normalization, and cleansing logic ensuring high-quality analytics
  • Reliability: Built robust error handling, monitoring, and alerting for 24/7 data pipeline operations
  • Security: Maintained end-to-end encryption and followed enterprise security standards
  • Maintainability: Containerized deployment and modular architecture simplified updates and troubleshooting
  • Layered Architecture: Using SQL Server as an intermediate layer provided flexibility for transformations and decoupled PI from Snowflake dependencies.
  • Incremental Development: Phased approach (single tag → tag group → full deployment) reduced risk and enabled iterative refinement.
  • Data Quality First: Investing in validation and cleansing logic upfront prevented downstream analytics issues.
  • Stakeholder Engagement: Regular demos to analysts and engineers ensured solution met actual business needs.
  • Documentation Matters: Comprehensive documentation of data lineage, transformations, and refresh schedules improved adoption and trust.
  • Schema Evolution: Handling PI AF structure changes required flexible schema mapping and automatic drift detection.
  • Time Zone Handling: Standardizing timestamps across PI (local time), SQL Server, and Snowflake (UTC) required careful design.
  • Performance Tuning: Optimizing bulk insert batch sizes and aggregation windows to balance latency and throughput.
  • Network Reliability: Implementing retry logic and buffering to handle intermittent network issues between on-premises and cloud.
  • Monitoring & Alerting: Building comprehensive observability for data pipeline health, latency, and data quality metrics.
  • Containerization: Docker provided consistent deployment across dev, test, and production environments.
  • Configuration Management: Externalized configuration enabled environment-specific settings without code changes.
  • Idempotency: Designed integration logic to be safely rerunnable, simplifying recovery from failures.
  • Data Validation: Implemented automated checks comparing source vs. destination record counts and data distributions.
  • Security by Design: TLS encryption, credential vaulting, and minimal privilege access throughout the pipeline.