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Pachyderm

United States
San Francisco
2014
Private
< $10m
51 - 200
Open website

Pachyderm is cost-effective at scale, enabling data engineering teams to automate complex pipelines with sophisticated data transformations across any type of data. The company's unique approach provides parallelized processing of multi-stage, language-agnostic pipelines with data versioning and data lineage Tracking. Pachyderm delivers the ultimate CI/CD engine for data.

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Pachyderm is a provider of Industrial IoT application infrastructure and middleware, and analytics and modeling technologies, and also active in the automotive, and oil and gas industries.
Technologies
Application Infrastructure & Middleware
API Integration & Management
Analytics & Modeling
Machine Learning
Use Cases
Autonomous Transport Systems
Predictive Maintenance
Industries
Automotive
Oil & Gas
Services
Data Science Services
System Integration
Pachyderm’s Technology Stack maps Pachyderm’s participation in the application infrastructure and middleware, and analytics and modeling IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies1
Optimizing Autonomous Driving with IoT: A Case Study of Woven Planet and Pachyderm
Woven Planet, a subsidiary of Toyota, is focused on building the safest mobility in the world with a particular emphasis on automated driving. The Automated Mapping team at Woven Planet is tasked with creating automotive-grade maps for use in automated and autonomous-driving vehicles. This requires the use of aerial orthographic projection, a method that has been used in the development of consumer-grade navigational maps. However, using this data to meet the rigorous requirements of automated driving at a continental scale is a significant challenge. The maps for automated driving applications need a level of detail, accuracy, and precision far beyond those of their consumer-grade counterparts. This requires processing large volumes of data. The Automated Mapping team needed an orchestration system that could scale to meet elastic workloads, easily toggle between structured and unstructured datasets, and provide long-lived pipeline stability for continuous, region-based map updates.
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