How do you keep cloud costs predictable for energy data volumes?

Through tiered storage, partition strategies tuned to time-series patterns, and per-team cost observability.

Can the platform run in our on-prem data center?

Yes. We deliver everything as Helm charts on Kubernetes, with object storage and compute layers that work on-prem, in your private cloud or next to a cloud data warehouse.

How do you handle PI data without disrupting OT?

We mirror PI tags read-only into the lakehouse via PI Web API and PI to Cloud connectors. PI itself remains the OT source of truth.

How long does an energy data warehouse implementation take?

It depends on how many source systems the first release loads, the state of your meter and asset master data, the reporting deadlines it must meet, and where the warehouse must run. We set the plan after a scoping call.

Can we start with a fixed-scope MVP?

Yes. The first release is scoped to one workflow, such as AMI interval data feeding one analytics workload, and later releases add sources and marts.

How do you use AI in delivery without risking quality?

AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests and security checks as any other code. Which tools may see your code or data is agreed with you at the start.

Energy / Data Platform

Energy Data Warehouse

Utility and energy data sits in PI, AMI head-ends, trading systems and spreadsheets, and no two teams agree on the numbers. Agnotic Technologies builds the energy data warehouse that brings it together, with the lineage and access controls your reporting needs.

15-Minute Scoping Call

Open formats

Iceberg or Delta tables on object storage

Read-only

PI and SCADA mirrored without touching OT

1 platform

For ops, trading, and ESG analytics

Trusted by global innovators

Industry Overview

What is an energy data warehouse?

An energy data warehouse is a central store that joins operational, meter, market, weather and financial data from a utility or energy operator, such as PI historian tags, AMI interval reads and trading positions, so every team queries one governed copy. Agnotic Technologies builds energy data warehouses on open formats, next to the systems you already run.

Industry Challenges

Why is energy data so hard to use?

Fragmented data costs in compliance risk, engineering velocity, and missed market opportunity.

Tooling Sprawl: Each team brings its own preferred database, and no two agree on what's authoritative.
Slow Insight: Asking 'what was the wind farm doing during last month's price spike?' takes a week of cross-team digging.
Compliance Risk: ESG and regulatory reports rely on copied-and-pasted snapshots without lineage.
Cloud Cost Surprise: Naive lifts to Snowflake or BigQuery balloon costs when energy data volumes hit production.

Our Approach

How do we build an energy data warehouse?

We bring the modern data stack, but tuned for the realities of OT data, time-series workloads, and energy-specific access patterns.

Open Lakehouse Foundations

Iceberg or Delta tables on object storage, no proprietary formats, no vendor lock-in.

Time-Series First

Specialized layouts and indexes for AMI, SCADA, and PI data that keep query times low.

Cost-Aware Layouts

Tiered storage, partitioning, and compaction strategies tuned to energy workload patterns.

Capabilities

What goes into the warehouse?

Built for the people who actually use energy data every day.

Unified Source Connectors

PI, OSI Monarch, Itron, Landis+Gyr, OPC-UA, FERC EQR, ISO market feeds.

Time-Series Optimization

Storage layouts and indexes tuned for AMI and SCADA query patterns.

Modeling Layer

dbt-based modeling with energy-specific semantic layer for common metrics.

Self-Service BI

First-class connectivity to Tableau, Power BI, Looker, Hex, and Streamlit.

Notebook Workspaces

Jupyter, Hex, and Databricks workspaces with row-level access controls.

Streaming Ingestion

Real-time ingestion of operational data sits next to batch with consistent semantics.

Lineage & Catalog

Column-level lineage from raw source to dashboard, exposed in OpenMetadata or DataHub.

Access Controls

Row, column, and tag-based access controls aligned to your IAM.

Cost Observability

Per-team, per-workload cost attribution and proactive optimization.

How It Works

How is an energy data warehouse designed?

Open formats, open standards, and operational discipline.

Source Connectors

Streaming and batch connectors for OT, AMI, market, and enterprise sources.

Bronze Layer

Raw, append-only ingest tables with full source metadata and replay capability.

Silver Layer

Cleaned, conformed, and joined entities: meters, intervals, assets, accounts.

Gold Layer

Business-ready marts: ESG, trading, operations, customer.

Semantic Layer

dbt + cube semantic layer for consistent metrics across BI tools.

Governance

Catalog, lineage, access controls, and cost observability across the whole stack.

Engineering Stack

Technology stack

Pragmatic, modern, and not a rewrite of your historian.

Lakehouse

Apache IcebergDelta LakeTrinoSpark

Streaming

KafkaFlinkMaterializeArroyo

Modeling

dbtCubeSQLMesh

BI & Notebooks

TableauPower BIHexStreamlitJupyter

Catalog

OpenMetadataDataHubUnity Catalog

Compute

SnowflakeDatabricksDuckDBTrino on Kubernetes

Measured Impact

What should the warehouse measure?

Measures we build reporting around, agreed with you before launch.

Freshness

Time from source to query

Measured per source system.

Coverage

Sources and assets loaded

Tracked as connectors go live.

Self-service

Questions answered without a data ticket

Through shared models and BI.

Cost

Storage and compute per workload

Reported per team.

Use Cases

Which workloads run on it?

What teams actually do on top of our energy lakehouses.

Operations Analytics

Cross-asset reliability, performance, and KPI dashboards.

Trading & Quant

Tick stores, position keeping, backtesting, and strategy development.

ESG & Carbon

Source-of-truth for emissions calculations and disclosure exports.

Customer Analytics

AMI-driven customer segmentation, program targeting, and rate design.

ML & Forecasting

Feature stores and training data for forecasting and anomaly detection.

Regulatory Reporting

FERC, NERC, and state-level reporting from one auditable source.

Integrations

Which systems does it connect to?

Integrations we build for OT, AMI, ERP, market, and BI tools.

Historian

OSIsoft PI

AMI

Itron OpenWayLandis+Gyr

ERP

SAP S/4HANAWorkday

Asset Mgmt

Maximo

Warehouse

SnowflakeDatabricks

BI

TableauPower BI

Compliance-First Development Services Backed by Global Standards

We build secure, scalable products designed for privacy, interoperability, and regulatory readiness from day one across every sector we serve.

General Data Protection Regulation

Implement lawful consent flows, data minimization, and secure processing for global data privacy.

Service Organization Control 2

Verified controls for security, availability, and confidentiality of enterprise data systems.

Information Security Management

Adhering to the international gold standard for managing information security risks.

Quality Management Systems

Ensuring consistent quality in software delivery and operational process optimization.

Our Edge

Why Global Leaders Choose Us

We combine deep technical expertise with industry-specific knowledge to deliver solutions that aren't just functional, but transformational.

Enterprise-Grade Security

We implement rigorous security protocols and compliance standards (HIPAA, GDPR, SOC2) across all industrial solutions to protect sensitive data.

High-Performance Scaling

Our architectures are built to handle massive data loads and user bases, keeping performance steady whether you're serving ten or ten million.

Accelerated Time-to-Market

Using our suite of internal tools and proven frameworks, we cut setup work so your first release reaches users sooner.

Embedded AI Integration

Beyond simple wrappers, we build deep-learning integrations and predictive analytics directly into the core of your industry-specific workflows.

Engagement Model

How does an engagement work?

A scoping call, a fixed-scope first release, then scale and support.

Scoping call

We map your sources, reporting needs, data residency rules and the teams who will query the warehouse.

Fixed-scope first release

One workload, such as AMI analytics, from source connector to dashboard, working end to end.

More sources and marts

Further systems and business marts added on the same models.

Scale and support

Our team supports the pipelines, models and costs as your data grows.

Facts and sources

Which rules and data sources shape an energy warehouse?

Short facts we design around, each linked to its primary source.

FERC requires each public utility, and each non-public utility with more than a de minimis market presence, to file an Electric Quarterly Report covering its services for every calendar quarter.
Source: 18 CFR 35.10b (eCFR)
The U.S. Energy Information Administration's open data API offers time-series data sets organized by energy category, including electricity, natural gas, petroleum and coal.
Source: EIA Open Data
NIST SP 800-82 Rev. 3, Guide to Operational Technology (OT) Security, gives guidance on securing OT such as industrial control systems while addressing their performance, reliability and safety requirements.
Source: NIST SP 800-82 Rev. 3 (NIST CSRC)

Voice of success

We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.

“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”

Aaron Phelan

Founder, Benchmark

“Agnotic combines deep technical expertise with strong domain knowledge. They understand the business context, anticipate challenges, and make collaboration smooth and effective.”

John Pasmore

Founder, Latimer

“Agnotic is the best technical team we evaluated. Their engineering excellence made our work dramatically easier and allowed us to stay focused on what matters most for maternal care outcomes. They took full ownership of the technical execution, and we are always happy to continue working together.”

Kim Smith

Founder, My Lauren

Related services

Where should you start?

Detailed pages for the energy data work teams ask us about.

Energy software development

All the grid, field and trading software we build for energy operators.

Energy and utilities analytics

Dashboards and models that run on top of the warehouse.

Oil and gas field operations app

Field data capture that feeds the same warehouse.

The Same Engineering Patterns, Across Other Industries

The technical patterns behind this platform translate naturally into adjacent verticals.

Manufacturing

Apply the same telemetry and predictive analytics patterns to factory floors and process plants.

Transportation

Extend grid-edge data pipelines to fleet electrification, charging logistics, and route planning.

Gov & Research

Power national energy datasets, climate modeling, and regulatory disclosure platforms.

Frequently Asked Questions

No. We routinely sit alongside existing Snowflake, BigQuery, or on-prem warehouses, federating queries across them. Migration is an option: never a prerequisite.