Services & Solutions

Historian & Lakehouse Connectivity

Transform ephemeral real-time data into a permanent enterprise asset—preserving high-frequency OT telemetry in historians while unifying IT and OT datasets in cloud lakehouses for analytics, AI, and compliance.

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Purpose & Pain Points Solved

A Unified Namespace gives you real-time operational visibility, but without historians and lakehouses, that data vanishes the moment it's consumed. This service ensures every critical data point is preserved, contextualized, and ready for analytics—today and decades from now.

Lost High-Frequency Data

Critical sensor data arriving at millisecond intervals gets downsampled or discarded because systems can't handle the volume or lack storage infrastructure.

Impact: Loss of detail needed for root cause analysis, quality investigations, and AI model training

Fragmented Historical Data

Production data in SCADA historians, quality data in QMS databases, maintenance data in CMMS—no unified view for comprehensive analytics.

Impact: Inability to correlate events across systems or perform cross-functional analysis

Inaccessible Long-Term Archives

Years of valuable operational data exist but is trapped in proprietary formats, aging systems, or offline tape backups that are impossible to query.

Impact: Cannot leverage historical patterns for predictive models or trend analysis

No Unified Analytics Platform

Data scientists need to cobble together data from multiple sources, each with different APIs, query languages, and access methods.

Impact: Weeks or months wasted on data preparation instead of generating insights

What This Service Enables

High-Frequency Preservation

Capture and store sensor data at millisecond intervals—never lose the detail needed for root cause analysis

Unified Historical View

Combine OT and IT data in a single lakehouse—query across production, quality, and business data effortlessly

Enterprise Brain for AI

Years of contextualized data ready for AI/ML—your lakehouse becomes the training ground for predictive models

Two-Tier Architecture

Our historian-lakehouse architecture balances real-time performance with long-term analytics capabilities.

Real-Time Layer (Historian)

High-frequency OT data storage with sub-second latency

Data Types:

Sensor readings (100ms - 1s intervals)
Machine states and alarms
Process parameters
Quality measurements

Common Use Cases:

Real-time monitoring
Process trending
Alarm analysis
Shift reports

Retention: Typical: 1-3 years at full resolution

Integrated Layer (Lakehouse)

Unified IT/OT data with business context for analytics

Data Types:

Aggregated OT data (1-minute to hourly rollups)
ERP data (orders, inventory, BOMs)
Quality & traceability records
Maintenance and work orders

Common Use Cases:

BI dashboards
Cross-functional analytics
Compliance reporting
AI/ML model training

Retention: Typical: 5-10+ years (potentially indefinite)

Enabling Tools & Technologies

We leverage best-in-class historians and cloud-native lakehouse platforms to build scalable, long-term data infrastructure.

Industrial Time-Series Databases

Purpose-built historians for high-frequency industrial data with specialized compression, retention policies, and time-series querying.

Key Capabilities:

TDengine: Ultra-fast TSDB with 10x compression for IoT/IIoT workloads
OSIsoft PI System: Industry-standard historian with 30+ years proven reliability
InfluxDB: Open-source TSDB with native MQTT and OPC UA support
TimescaleDB: PostgreSQL-based TSDB with SQL compatibility
Lossy and lossless compression for long-term storage
Automated data rollup and aggregation policies
Sub-millisecond query performance on billions of points

Cloud Data Lakehouse Platforms

Modern architectures that combine the flexibility of data lakes with the structure and governance of data warehouses.

Key Capabilities:

Snowflake: Cloud data platform with automatic scaling and zero-copy sharing
Databricks Lakehouse: Delta Lake + Spark for unified analytics and AI
AWS: S3 + Athena/Redshift for serverless lakehouse architecture
Azure Synapse Analytics: Integrated analytics service for big data and warehousing
Support for structured, semi-structured, and unstructured data
ACID transactions and schema evolution
Native integration with BI tools and ML platforms

Data Pipeline & ETL Tools

Robust connectors and pipeline tools for moving data from OT/IT sources to historians and lakehouses at scale.

Key Capabilities:

N3uron: IIoT platform with native historian and cloud connectors
HighByte Intelligence Hub: Contextualized data delivery to any destination
Apache Kafka: Distributed streaming for high-volume data pipelines
AWS IoT Analytics: Managed pipelines for IoT data processing
OPC Router: Visual ETL for industrial data workflows
Real-time streaming and scheduled batch transfers
Data quality validation and transformation at ingestion

Governance & Security

Enterprise-grade data governance, access control, and compliance frameworks for industrial data at rest.

Key Capabilities:

Fine-grained access control (RBAC) at table and column level
Data classification and tagging for compliance (GDPR, CCPA)
Encryption at rest and in transit (AES-256, TLS 1.3)
Audit logging for all data access and modifications
Data lineage tracking from source to consumption
Retention policies for regulatory compliance (21 CFR Part 11, etc.)

Cloud storage and database infrastructure

Cost-Effective at Scale

Cloud lakehouses cost 10-50x less than traditional data warehouses for long-term storage. Store decades of data for the cost of a few months in legacy systems—enabling AI/ML projects that were previously cost-prohibitive.

How We Deploy at Your Site

Our systematic approach delivers production-ready historian and lakehouse infrastructure with validated data quality.

1. Historian Deployment & Configuration

3-5 days

Activities:

Select historian architecture: on-premise (OSIsoft PI, TDengine) or cloud-managed (InfluxDB Cloud)
Deploy historian infrastructure with appropriate sizing for data volume and retention
Define tag database schema aligned with ISA-95 equipment hierarchy
Configure data compression algorithms and rollup policies
Set up high-availability clustering for mission-critical environments
Connect historian to OPC UA servers, MQTT brokers, or real-time data sources

Deliverable: Production historian receiving and storing high-frequency OT data

2. Lakehouse Architecture & Setup

3-5 days

Activities:

Choose lakehouse platform based on analytics requirements (Snowflake, Databricks, AWS)
Design data lake zones: raw, curated, analytics-ready (bronze/silver/gold)
Create object storage buckets or containers with lifecycle policies
Define schemas for unified IT/OT datasets (star schema, data vault, or wide tables)
Set up compute clusters or warehouses for query and analytics workloads
Configure network connectivity and VPN/Direct Connect for hybrid access

Deliverable: Cloud lakehouse infrastructure ready to receive integrated data

3. Data Ingestion Pipeline Configuration

4-6 days

Activities:

Configure real-time streaming: MQTT → historian for high-frequency sensor data
Set up contextual data pipelines: enriched OT/IT data → lakehouse
Implement batch transfers: historical backfill and periodic aggregates to cloud
Configure data transformation rules and quality checks
Enable store-and-forward for network resilience
Set up monitoring and alerting for pipeline health

Deliverable: Automated data pipelines feeding historian and lakehouse

4. Data Quality & Validation

2-3 days

Activities:

Validate data completeness: ensure all expected tags/streams are present
Check timestamp accuracy and data alignment across sources
Verify compression ratios and storage efficiency
Test query performance on representative workloads
Validate data lineage and audit trail functionality
Confirm retention policies and archival processes

Deliverable: Validated data quality and query performance baselines

5. Analytics & Visualization Integration

3-4 days

Activities:

Connect BI tools (Power BI, Tableau, Grafana) to historian and lakehouse
Deploy process visualization dashboards for operations teams
Set up SQL/Python notebooks for data science exploration
Configure ML platform access (SageMaker, Azure ML, Databricks)
Create example queries and reports for common use cases
Enable self-service analytics with proper access controls

Deliverable: Analytics and visualization tools consuming historian and lakehouse data

6. Documentation, Training & Handoff

2-3 days

Activities:

Document data models, schemas, and tag hierarchies
Create query examples and best practices guide
Train operations teams on historian trending and reporting
Train data teams on lakehouse access patterns and SQL queries
Deliver runbooks for monitoring, backup, and maintenance
Establish ongoing support and escalation procedures

Deliverable: Complete documentation and trained teams ready for production use

Typical Implementation Timeline

17-26 Days

From initial infrastructure deployment to production-ready historian and lakehouse

Business Benefits

Unlimited Data Retention

Store years or decades of high-frequency operational data with efficient compression—never lose critical historical context again.

Unified Analytics Platform

Single query interface for all IT and OT data—no more stitching together data from disparate systems.

AI/ML Ready Datasets

Clean, contextualized datasets with OT and IT context ready for training predictive models without months of data prep.

Real-Time & Historical Analysis

Query live data alongside years of history—spot patterns, trends, and anomalies that span multiple time horizons.

Compliance & Auditability

Immutable audit trails, access logs, and retention policies that satisfy regulatory requirements (FDA, ISO, etc.).

Scalable Performance

Cloud-native architecture scales compute and storage independently—handle growing data volumes without re-architecture.

Traditional vs. Modern Architecture

Traditional Approach

Data retention limited by expensive proprietary storage
Siloed systems with no unified analytics
High-frequency data downsampled or discarded
Manual data exports for analysis
Weeks of data prep for ML projects
Limited scalability without re-architecture

Historian + Lakehouse

Years or decades of data with cost-effective cloud storage
Single query interface for all IT/OT data
Full-resolution data retained for critical assets
Real-time and historical queries in same platform
Analytics-ready datasets with IT/OT context
Elastic scaling of compute and storage independently

Common Use Cases

Predictive Maintenance with Historical Patterns

Stream vibration and temperature data to historian at 1-second intervals. ML models query years of historical data from lakehouse to learn normal behavior patterns and detect anomalies indicating impending failures.

Data Flow:

Sensors → MQTT → Historian (real-time) → Lakehouse (hourly aggregates + anomalies)

Outcome: 30-50% reduction in unplanned downtime through early failure detection

Product Quality Root Cause Analysis

When quality issues arise, analysts query lakehouse to correlate process parameters (from historian), material batches (from ERP), and operator assignments (from MES) to identify root causes across weeks or months of production.

Data Flow:

Process data + IT systems → Lakehouse with unified schema

Outcome: Root cause analysis time reduced from days to hours

Energy Optimization & Sustainability

Store power meter data at 1-minute intervals in historian. Aggregate to lakehouse with production schedule, weather data, and utility pricing for ML-driven energy optimization and carbon reporting.

Data Flow:

Power meters → Historian → Lakehouse + Weather API + ERP data

Outcome: 5-15% energy cost reduction through usage optimization

Process Digital Twin Development

Feed years of process data from historian into lakehouse where data scientists build physics-informed ML models. These digital twins predict product quality based on current process parameters.

Data Flow:

Historical process + quality data → Lakehouse → ML platform → Digital twin model

Outcome: Proactive quality control and reduced waste/scrap

Ready to Build Your Data Foundation?

Stop losing valuable operational data. Build a historian-lakehouse architecture that preserves every detail and enables AI-driven insights for years to come.

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