Context at the source
Raw tags are not insight. Fuuz joins machine signals to the work order, lot, recipe, operator and quality result they belong to, before the data leaves the plant.
Fuuz and Databricks
From The Machine To The Model, Without The Integration Tax
Fuuz is the industrial operations platform that gathers OT data at the source and gives it context. Databricks is where that data becomes analytics, machine learning and AI. A native Fuuz connector for Databricks links the two, so industrial organizations get one chain from the sensor on the line to the model in the lakehouse, and the applications to act on what it finds.
The chain
How Data Flows From Plant To Lakehouse
Fuuz sits between your operation and Databricks. It collects the data where it is made, adds the context that makes it meaningful, runs the applications people use every day, and delivers the same trusted data to Databricks.
What the partnership means
Operational Data You Can Trust, Where Your Analytics Live
Industrial organizations have two hard problems: getting trustworthy data out of the plant, and doing something useful with it once they have it. Fuuz and Databricks each take one, and hand off cleanly to the other.
A native connector
Fuuz has a native Databricks connector, so contextualized operational data moves into your Databricks environment without a custom pipeline to build and maintain.
One platform for the plant floor
The same Fuuz platform that collects the data also runs the applications operators use, so analytics and operations start from one model.
Fewer systems to stitch
Edge collection, data modeling, application development and delivery to the lakehouse live in one platform, not in a chain of separate tools and custom code.
Built for many plants
A central view across sites, with each plant keeping its own operational applications, so enterprise analytics does not depend on every plant running the same stack.
A path from insight to action
What Databricks learns can inform the applications people use on the floor, so analysis does not stay in a report.
The operational intelligence layer
Fuuz Is Where Operations Happens
Fuuz is an industrial operations platform. It connects to equipment and enterprise systems, keeps operational data in a persisted Unified Namespace with a model you define, and runs the applications that manufacturers and logistics operators use every day.
That makes Fuuz the operational intelligence layer: the place where raw signals become orders in progress, assets in state, lots in genealogy and tasks for people. Databricks takes that contextualized data and does what a lakehouse does best at enterprise scale.
Because the applications run on the same platform as the data, teams build real-time applications, or start from an accelerator, without leaving their operational platform to do it.
- Edge connectivity with store-and-forward and outbound-only connections
- A persisted Unified Namespace and a data model you own
- Real-time applications built low-code and pro-code on one platform
- Accelerators to start from, or build your own
- One GraphQL API over everything
Fuuz brings
EdgeGateways connect PLCs, machines, scales and scanners.
ContextA model that ties data to orders, lots, assets and people.
ApplicationsOperations software, built or started from an accelerator.
GovernanceOne access model and audit trail across everything.
At the plant
Problems Solved On The Floor
Fuuz runs the work itself, so the plant fixes problems where they happen, and the data behind each fix is already contextualized for Databricks.
Machines that do not talk to the business
Machine state, downtime and OEE tied to the work order, lot and operator, so a stoppage is a business event, not a tag change.
Machine monitoringPaper and spreadsheets on the floor
Work orders, operator screens, labels and production tracking in one system, so the record is made as the work is done.
Production executionQuality escapes and slow recalls
Inspections, nonconformances and full genealogy, so you can trace a lot forward and back.
QualityUnplanned downtime
Maintenance work orders and equipment history connected to the machines that need them.
MaintenanceInventory you cannot trust
Receiving, put-away, picking and shipping with scanners and labels, so counts match the shelf.
Inventory trackingA plant that stops when the network does
Edge buffering and a site tier keep production running through network drops and ERP maintenance windows.
ArchitectureAcross the enterprise
Problems Solved Across The Business
This is where the chain pays off: every plant's contextualized data reaches Databricks through the native connector, while each plant keeps running on its own applications.
No single view across plants
One data model across sites, so a metric means the same thing in every plant and enterprise reporting does not depend on how each plant is wired.
Multi-siteData science that starts with raw tags
Analysts and data scientists receive operational data that already carries its orders, lots, assets and quality results, and spend their time on models, not decoding signals.
Insights that never reach the floor
What the analysis finds can inform the applications operators use every day, so improvement does not stay in a report.
AI enablementERP and plant systems that do not fit together
Fuuz connects to the ERP you run, or several, and keeps the plant floor logic out of the ERP, while Databricks gets the combined picture.
ERP extensionSupply chain you cannot see end to end
Receipt to ship, with warehouse, yard and transportation activity connected to production data.
Supply chainAudit and governance at scale
One access model and audit trail across the platform, so data you send to Databricks has a controlled, traceable source.
Data governanceHow the chain works
Four Steps From Signal To Insight
1
Collect
The Fuuz Gateway connects PLCs, machines, scales and scanners with outbound-only connections, and buffers data through network drops.
2
Contextualize
The Unified Namespace and your data model join each signal to the work order, lot, asset and person it belongs to.
3
Deliver
The native Fuuz connector delivers contextualized operational data to Databricks, alongside the enterprise data you already keep there.
4
Analyze and act
Data teams analyze and model in Databricks. Operations teams act in real-time Fuuz applications built on the same data.
The difference
A Stitched Stack Or One Chain
A stitched-together stack
- Edge collection, historian and middleware from different vendors
- Custom code to map tags to business context, rebuilt for each plant
- A separate effort to build the applications operators use
- Data teams spend their time decoding signals
- Every new use case adds another integration to maintain
Fuuz with Databricks
- Collection, context and applications on one platform
- A model that carries business context from the start
- Operations software built or started from an accelerator on the same data
- Data teams receive data that already means something
- New use cases build on what is already connected
Who it is for
Built For Operations And Data Together
Operations and plant leaders
Real-time applications for the floor, with the data behind them available to the enterprise.
IT and OT teams
One governed platform between the plant and the lakehouse, with outbound-only connections from the plant.
Data and analytics teams
Contextualized operational data to work with, instead of raw tags to interpret.
Multi-site manufacturers
Enterprise analytics across plants, with each plant running on its own applications.
Logistics and supply chain
Warehouse, yard and transportation operations connected to the same data as production.
Partners and integrators
One platform to deliver the operational layer, with Databricks for the analytics layer.
Become a partnerQuestions
Common Questions
What is the Fuuz and Databricks partnership?
Fuuz and Databricks are technology partners. Fuuz collects and contextualizes operational data from the plant and delivers it to Databricks, where organizations run analytics, machine learning and AI.
Do I need Databricks to use Fuuz?
No. Fuuz is a complete industrial operations platform with its own persisted data layer, applications and accelerators. Databricks adds enterprise-scale analytics and AI on the data Fuuz collects.
Does Fuuz replace Databricks?
No. Each does a different job. Fuuz runs operations and gives operational data its context. Databricks is the lakehouse for analytics and AI across the enterprise.
Is there a connector?
Yes. Fuuz has a native Databricks connector, so operational data moves from Fuuz into your Databricks environment without custom integration code. See connectors.
Can I build applications without leaving Fuuz?
Yes. Teams build real-time applications on the platform, or start from an accelerator for work such as manufacturing execution and warehouse management, and extend them.
Where does the data go, and who controls it?
Your operational data is modeled in your own Fuuz enterprise, which has its own database, under one access model and audit trail. See data governance. Talk to our team about what you want delivered to Databricks.
How do we get started?
Talk to our team. We will help you map the data you want in Databricks, and the applications you want on the floor.
Databricks and the Databricks logo are trademarks of Databricks, Inc.
Get started
Build The Chain From Machine To Model.
Tell us what you want to know about your operation. We will show you how Fuuz and Databricks get you there.
Prefer to talk? Call 1-877-801-FUUZ (3889)