Event-driven OT systems. Edge to Enterprise.
Transform your industrial data infrastructure
Expertise and execution that sets us apart
Building blocks for event-driven industrial systems
Industrial MQTT standard. Self-describing payloads with metadata.
Semantic data fabric. Hierarchical topic structure provides context.
Resilient remote operations. Local processing survives network outages.
Machine learning models need clean, contextualized, time-series data at scale. Modern OT architectures must prepare data for AI/ML while maintaining operational systems. Streaming pipelines enable real-time inference. Batch systems support model training and historical analysis.
Lambda architecture combines hot path streaming with cold path batch processing. Streaming handles real-time inference while batch systems retrain models on historical data. Feature store centralizes and versions engineered features, ensuring consistency between training and inference.
Data lake preserves raw data in Parquet and Delta Lake formats for future retraining. Streaming pipeline routes data through Kafka, Kinesis, or Event Hub to ML endpoints. Model deployment supports edge inference for real-time decisions and cloud scoring for complex models. Feedback loop sends predictions back to operations for continuous learning.
High-frequency time-series: Consistent sampling rates from 1Hz to 10kHz ensure temporal accuracy. Quality indicators mark each data point as good, bad, or uncertain, allowing models to filter unreliable inputs. Rich context includes asset hierarchy and relationships, providing the semantic information models need.
Feature engineering begins at the edge with pre-calculated derivatives, aggregations, and rolling statistics. Historical depth spanning years enables models to learn seasonal patterns and long-term trends. Real-time access with sub-second latency supports inference workloads. Scalability handles millions of features across thousands of assets.
Predictive maintenance: Anomaly detection from vibration and temperature patterns. Quality prediction: Real-time product quality from process parameters. Optimization: Energy consumption and yield maximization. Computer vision: Defect detection from camera feeds. Soft sensors: Virtual measurements where physical sensors aren’t feasible. Forecasting: Production, demand, and equipment failure prediction.
Deep expertise across historians, contextualization engines, and cloud platforms
Unified SCADA/MES/IIoT platform. Unlimited licensing model.
Architecture: Enterprise Administration Module for multi-site management. Hub-and-spoke architecture scales globally. Direct queries enabled through SQL-based storage.
Cloud data warehouse optimized for analytics. Separate compute and storage.
Integration: Snowpipe enables continuous loading. External stages query cloud storage directly. Data sharing without copying. Connects with Databricks, Tableau, Power BI, and Python/Java UDFs.
Unified analytics platform. OneLake centralized storage.
Enterprise Features: Direct PI System connectors available. KQL time-series databases. Native Azure security integration. Data pipelines for ETL. Real-time dashboards. Cost-effective with unified licensing. Integration with Azure IoT Hub.
Industry standard historian. Petabyte-scale deployments.
Data Management: AF hierarchy provides context. Trust mappings and security controls. OLEDB and SDK access. PI Vision for visualization. PI Integrator for relational databases. High availability with collectives.
Data contextualization engine. Transform OT data into IT-friendly formats.
Approach: Model-driven with no custom code required. ISA-95 compliant data models. Visual modeling interface. Built-in data quality checks. Version control for models. Scales horizontally.
High-performance, cost-effective. 50:1 lossless compression.
Deployment: Store-and-forward architecture. Windows or Linux deployment. No client installs needed. Built-in data visualization. Fast query performance. Ideal for large deployments.
Proven approaches from single site to global enterprise
Centralized management and distributed execution. The hub aggregates data and pushes standardized deployments to all sites. Federated queries access all historians remotely, while local sites maintain autonomous operation. Scales to hundreds of locations and is proven in Fortune 500 manufacturing, pipeline, and utility environments.
Optimize cost and performance. Hot tier provides real-time historian access for 7-30 days of operational data. Warm tier uses cloud data warehouse for 1-7 years of analytical queries. Cold tier leverages object storage like S3 Glacier for 7+ years of compliance retention. Automated lifecycle policies move data between tiers. Unified query interface spans all tiers without cost explosion. Compliance retention achieved with 70-90% storage cost reduction.
Global data fabric with local autonomy. Site-level UNS deployments maintain independence while enterprise broker federation provides global visibility. Selective data promotion sends critical information to corporate systems. Consistent namespace hierarchy spans all locations globally. Local operations remain unaffected by WAN issues. Context is preserved across federation boundaries. Scales to millions of topics while supporting hybrid cloud scenarios.
On-premise operations, cloud analytics. Zero cloud dependency for real-time control. One-way data flow to cloud preferred for security and compliance. Edge gateways provide buffering and protocol translation. Connectivity via VPN, ExpressRoute, or Direct Connect ensures reliable transport. Elastic cloud compute handles ML/AI workloads without impacting operations. Advanced analytics run without operational risk. Compliance requirements met through data residency controls. Enables phased cloud adoption without disrupting production systems.
Proven phased approach minimizes risk and delivers quick wins
2-4 weeks. Inventory systems, protocols, data volumes. Identify integration points and constraints.
Document current architecture. Review network and security. Gap analysis. Technology recommendations. ROI analysis. Phased roadmap with budget.
4-8 weeks. Single production line or small facility. Prove the architecture works.
Deploy MQTT broker and edge gateway. Implement UNS for asset subset. Test cloud integration. Measure results. Train team. Create scale-out template.
3-6 months. Expand to full facility. Standardize configurations.
Complete UNS coverage. Deploy redundancy and HA. Integrate all systems. Deploy analytics use cases. Establish monitoring procedures. Document as-built.
6-18 months. Replicate to additional sites. Enterprise architecture.
Hub-and-spoke deployment. Enterprise federation. Centralized management. Data lake or warehouse. Standardize with local flexibility. Global governance.
Ongoing. Deploy AI/ML applications. Real-time scoring and predictions.
Feature engineering pipelines. Model training on historical data. Edge or cloud inference. Feedback loops to operations. Continuous model improvement.
Continuous. Performance tuning. Cost optimization. Capability expansion.
Monitor and tune performance. Right-size infrastructure. Add new use cases. Update technologies. Knowledge sharing. Measure business impact.
Throughout all phases. High-value, low-risk improvements deployed alongside main roadmap.
Executive dashboards. Alarm analytics. Energy monitoring. OEE tracking. Remote monitoring. Mobile HMI. BI tool integration. Build momentum fast.
Flexible approaches to fit your project needs
2-4 weeks. Foundation for planning.
Deliverables: Assessment report, reference architecture, implementation roadmap, preliminary budget. Fixed-price engagement. No implementation obligation.
4-12 weeks. Implementation-ready design.
Deliverables: Complete architecture package with diagrams, specifications, configurations, and test plans. Your team or ours can implement. Time and materials or fixed price.
Ongoing. Architecture governance during implementation.
Deliverables: Production-ready system matching architecture. Documentation updates. Trained operations team. Retainer or hourly basis.
Annual. Ongoing architecture consultation.
Deliverables: Guaranteed availability. Documented recommendations. Technology roadmap updates. Strategic technology partner relationship. Monthly or annual retainer. Priority support.
Whether you’re building a Unified Namespace, integrating OT data into Snowflake, implementing Ignition at enterprise scale, or redesigning with SparkplugB and MQTT—Streamline brings proven expertise to ensure your success.