Telemetry from 6M machines was trapped in internal silos. Unifying it into a shared platform lets customers deploy predictive AI.
A technology provider for the construction sector processing 3 billion daily data points from over 6 million connected machines.
Data across finance, engineering, and revenue operations was isolated in separate silos, creating massive internal inefficiencies. Furthermore, the...
“If we had built a platform ourselves, it would not have evolved at the same pace. Having that innovative engineering muscle that Databricks has—releasing hundreds of new features—is the key reason for us choosing it. It allows our customers to focus on eliminating downtime rather than maintaining infrastructure or scaling a platform. They just get the outcomes.”
Trackunit's Customer analytics platform is part of this use case:
IoT and telematics platform for construction equipment and fleet management.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Related implementations across industries and use cases
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Data surges delayed insights for hours. Long-context AI models now analyze complex real estate documents in seconds.
Custom analytics required months of full-stack development. Now, self-serve AI apps connect analysts directly to data models.
Managing 200TB of player data required heavy manual oversight. Now, serverless pipelines let scientists deploy models themselves.
Regulatory docs the size of phone books slowed model cycles to months. An AI agent now codes features and writes documentation in hours.
Data silos blocked real-world simulation. Now, teams validate site physics and safety risks on a unified digital twin.
Scattered expertise across 40 engineering topics slowed global teams. Now, an AI agent instantly surfaces secure, curated best practices.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Telemetry from 6M machines was trapped in internal silos. Unifying it into a shared platform lets customers deploy predictive AI.
A technology provider for the construction sector processing 3 billion daily data points from over 6 million connected machines.
Data across finance, engineering, and revenue operations was isolated in separate silos, creating massive internal inefficiencies. Furthermore, the...
“If we had built a platform ourselves, it would not have evolved at the same pace. Having that innovative engineering muscle that Databricks has—releasing hundreds of new features—is the key reason for us choosing it. It allows our customers to focus on eliminating downtime rather than maintaining infrastructure or scaling a platform. They just get the outcomes.”
Trackunit's Customer analytics platform is part of this use case:
IoT and telematics platform for construction equipment and fleet management.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Related implementations across industries and use cases
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Data surges delayed insights for hours. Long-context AI models now analyze complex real estate documents in seconds.
Custom analytics required months of full-stack development. Now, self-serve AI apps connect analysts directly to data models.
Managing 200TB of player data required heavy manual oversight. Now, serverless pipelines let scientists deploy models themselves.
Regulatory docs the size of phone books slowed model cycles to months. An AI agent now codes features and writes documentation in hours.
Data silos blocked real-world simulation. Now, teams validate site physics and safety risks on a unified digital twin.
Scattered expertise across 40 engineering topics slowed global teams. Now, an AI agent instantly surfaces secure, curated best practices.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.