What data sources can FactoLake ingest?

FactoLake can ingest data from PLCs, SCADA systems, MES, ERP, quality systems, IoT sensors, and historian databases — unifying OT and IT data in one layer.

Apache Iceberg is an open table format that supports petabyte-scale data, time-travel queries, schema evolution, and compatibility with tools like Spark, Trino, and DuckDB — avoiding vendor lock-in.

How does FactoLake support AI and optimization use cases?

FactoLake provides the historical data foundation that AI agents, predictive models, and optimization algorithms need to train, validate, and reason about plant behavior over time.

Industrial Data Lake Platform

One Data Foundationfor All Operations

FactoLake unifies machine, execution, quality, and enterprise data into one industrial data layer built on Apache Iceberg. One queryable foundation for reporting, traceability, AI, and optimization.

100%OT-IT Convergence

IcebergOpen Format

PetabyteScale Ready

Book Data Architecture Review See IIoT Foundation

DATA LAKE CONSOLEReal-time Ingestion Monitor

LIVE

SCADA / PLCOPC-UA

2.4M rows12K/sec

FactoMES EventsMQTT

890K rows3.2K/sec

FactoVision ImagesS3

45K rows120/sec

3.49M

Total Records

99.99%

Uptime SLA

4.2TB

Lake Size

SCADA, MES, ERP, quality, and vision data

One queryable industrial history

Built for traceability, analytics, and AI

Foundation for advanced optimization use cases

Client Problems

Why industrial data programs fail to create a usable source of truth.

This page is aimed at teams with data everywhere and operational clarity nowhere.

High

OT and IT data live separately

Impact severity

High

History is difficult to query in context

Impact severity

High

Point data stores do not scale cleanly

Impact severity

High

AI is attempted before the data model is ready

Impact severity

Operational Shift

From disconnected records to one industrial knowledge layer.

When plant data lives in separate systems, every answer is a manual hunt. Drag to see what changes when it all shares one foundation.

Current stateSiloed industrial data

Future stateFactoLake operating model

FactoLake operating model

All operational history sits in one structured foundation.
OT and IT data queried together, in real plant context.
Traceability and analytics work from the same source.
AI and optimization run on one clean, stable data layer.

Siloed industrial data

Machine, quality, MES, and ERP data sit in separate systems.
Teams spend more time finding data than improving the line.
Traceability and root-cause mean stitching data by hand.
AI and analytics run on fragile, one-off pipelines.

← drag to compare →

50% →

Future state

FactoLake operating model

All operational history sits in one structured foundation.
OT and IT data queried together, in real plant context.
Traceability and analytics work from the same source.
AI and optimization run on one clean, stable data layer.

How FactoLake Solves It

A data layer built for industrial operations, not just storage.

FactoLake is about context, history, and usability as much as ingestion.

Solution 01 of 04

Ingest data from across the plant stack

FactoLake takes in machine, process, execution, quality, and enterprise data from multiple systems and formats.

Proven Outcome

Plants stop treating each operational system as a separate analysis island.

Data Profile

What the industrial data mix typically looks like.

This chart-style infographic gives buyers an immediate feel for how diverse the foundation must be.

SCADA / PLC telemetry2.4M rows/day

high-frequency machine and process signals

MES execution events890K rows/day

orders, status, downtime, traceability

ERP / business transactions156K rows/day

materials, planning, and enterprise context

Vision / quality metadata45K records/day

inspection events and image-linked quality outcomes

Illustrative source mix across a connected manufacturing environment.

Data Workflow

How FactoLake turns raw signals into usable industrial history.

This is a mobile-friendly flow diagram for the data journey.

Step

Collect data from multiple plant systems

Bring in machine, execution, quality, and business events from the systems that currently stand apart.

Step

Map each event to business and plant context

Tie the data to the right asset, order, operator, batch, or production window.

Step

Store one reliable operational history

Create a queryable base that supports investigation, dashboards, and traceability without rebuilding the context each time.

Step

Expose the data to the broader stack

Use the same foundation for reporting, AI agents, optimization, and long-term industrial analytics.

Reference Architecture

How FactoLake sits inside the LeanQubit stack.

This diagram explains where the data product fits relative to machines, workflows, and intelligence.

Layer 01 · Layer 1

Operational source systems

PLCs, SCADA, MES, ERP, vision, and quality systems each generate important but incomplete pieces of the plant history.

PLCsSCADAMESERP

PLCs, SCADA, MES, ERP, vision, and quality systems each generate important but incomplete pieces of the plant history.

PLCsSCADAMESERP

Data is collected, transformed, and mapped into an industrial context model that preserves usability across domains.

IngestionSchemaContextPipelines

The unified store keeps time-series, event, and contextual history available for both operational and analytical consumption.

HistoryEventsLakehouseQueries

Dashboards, traceability, AI agents, maintenance models, and optimization services consume the same trusted historical base.

AITraceabilityPlanningAnalytics

Connected Modules

What FactoLake powers inside LeanQubit.

The data foundation exists to support the rest of the operational stack.

Module 01

FactoMES

Uses the shared history for execution analysis, traceability, and downtime investigation.

Part of LeanQubit Platform

Module 02

FactoVision

Stores and contextualizes inspection outcomes for quality analysis and model improvement.

Part of LeanQubit Platform

Module 03

FactoIQ

Consumes historical and live context for maintenance, quality, and operations intelligence.

Part of LeanQubit Platform

Module 04

FactoPlan

Benefits from execution and historical context to support better scheduling decisions and optimization.

Part of LeanQubit Platform

Buyer Value

What different stakeholders get from a real industrial data foundation.

This turns the data story into role-specific value.

Value for Operations Leader

"A more trustworthy view across production, downtime, quality, and business context without stitching together multiple reports."

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Key metrics

OEE uplift
avg across plants
0%
MTTR reduction
faster fault resolution
0%
Shift visibility
real-time coverage
0%

Implementation Model

How to build the data layer without over-scoping the program.

A phased rollout keeps the foundation useful early and scalable later.

Phase 01 · Week 1–2

Select the highest-value source systems

Start with the systems that matter most to the operational questions the plant cannot answer today.

Step 1 of 4

Start with the systems that matter most to the operational questions the plant cannot answer today.

Map assets, work orders, batches, line states, and business entities to the incoming data.

Create the operational history and validate that teams can query it in business context.

Use the foundation to power broader traceability, AI, and optimization programs.

Start your journey Typical deployment: 6–8 weeks

FAQ

Questions buyers ask before investing in the data foundation.

It is designed to unify multiple industrial and business sources into one contextualized operational history rather than just store one category of signals.

Showing 3 of 3 questions

Next Step

Build one industrial data foundation the rest of the stack can trust.

Book a data architecture review to identify where your factory history is fragmented, which sources matter most first, and how LeanQubit can unify them into one operational model.

Book Data Architecture Review See FactoIQ

Initializing Systems...

One Data Foundationfor All Operations

FactoLake unifies machine, execution, quality, and enterprise data into one industrial data layer built on Apache Iceberg. One queryable foundation for reporting, traceability, AI, and optimization.

100%OT-IT Convergence

IcebergOpen Format

PetabyteScale Ready