LabVIEW remains one of the most powerful tools for engineers working in test, measurement, automation and embedded control. As we settle in 2025, mastering advanced LabVIEW techniques is essential to developing scalable, maintainable and high-performance applications.
This guide categorizes the most critical LabVIEW skills based on different engineering disciplines, ensuring that professionals focus on the most industry-relevant capabilities.
Essential for All LabVIEW Engineers
These skills form the foundation for anyone working with LabVIEW, regardless of industry or application.
1. Advanced LabVIEW Architecture (QMH, Actor Framework, DQMH)
Queued Message Handler (QMH): For event-driven and parallel processing.
Actor Framework (AF): For complex, modular applications with dynamic message handling.
Delacor Queued Message Handler (DQMH): An alternative to AF, optimized for large-scale applications.
Why it matters: A well-structured architecture makes applications more scalable, maintainable and efficient.
2. Event-Driven Programming & Parallel Execution
User events and dynamic event handling to create responsive user interfaces.
Producer-Consumer loops for separating acquisition and processing tasks.
Parallel loop execution to utilize multi-core processors effectively.
Why it matters: Efficient event-driven programming ensures responsiveness and optimized performance.
3. Performance Optimization & Debugging Techniques
Profiling code using NI Desktop Execution Trace Toolkit (DETT) and VI Profiler.
Reducing memory leaks by optimizing arrays and avoiding unnecessary copies.
Leveraging in-place element structures to improve execution speed.
Why it matters: Well-optimized LabVIEW code ensures fast execution, minimal resource consumption and stability.
Essential for Test & Measurement Engineers
For engineers working with automated test, validation and measurement systems, mastering these skills is crucial.
1. Hardware Abstraction & Driver Development
Creating a Hardware Abstraction Layer (HAL) for modular instrument control.
NI-VISA, NI-DAQmx and low-level driver programming for precise hardware interaction.
FPGA-based I/O control using LabVIEW FPGA and CompactRIO for real-time applications.
Why it matters: A well-structured HAL allows reusability across different test platforms, reducing maintenance efforts.
2. Data Management, Logging & Cloud Integration
Using TDMS (Technical Data Management Streaming) for high-speed data logging.
Integrating LabVIEW with databases (MySQL, SQLite, PostgreSQL) for structured data storage.
Cloud-based data exchange via, REST APIs and AWS/Azure IoT.
Why it matters: Efficient data logging and cloud integration enable remote monitoring, predictive maintenance, and big data analysis.
Essential for Embedded & Real-Time Engineers
For engineers working on real-time control, embedded systems and deterministic applications, these skills are vital.
1. LabVIEW Real-Time & FPGA Programming
Developing real-time control applications on CompactRIO (cRIO) and PXI.
Custom FPGA logic development to offload CPU-intensive processing.
Synchronization and deterministic loop execution with NI-TimeSync and TSN (Time-Sensitive Networking).
Why it matters: Real-time and FPGA processing provide low-latency, high-reliability control for industrial automation, robotics and medical devices.
2. Synchronization & Timing (TSN, NI-TimeSync)
Using hardware-timed loops for high-precision applications.
Implementing TSN (Time-Sensitive Networking) for distributed real-time systems.
Ensuring precise timestamping for sensor fusion and closed-loop control.
Why it matters: Precise synchronization is critical for applications like power grid monitoring, autonomous vehicles and precision manufacturing.
Advanced Skills for Industry 4.0 & AI Engineers
As industries embrace AI, machine learning and IIoT (Industrial Internet of Things), LabVIEW engineers must adapt to these emerging trends.
1. Python & LabVIEW Interoperability
Calling Python scripts from LabVIEW using LabVIEW-Python Node.
Using PyVISA for instrument control, complementing LabVIEW’s hardware interface.
REST API and gRPC communication between LabVIEW and Python-based web services.
Why it matters: Python integration unlocks AI-powered analysis, advanced data processing and cloud automation.
2. Machine Vision & AI-Powered Automation
Developing AI-based inspection systems using NI Vision Development Module (VDM).
Real-time image processing with OpenCV, TensorFlow and NI AI Toolkit.
Automating defect detection and quality control using deep learning.
Why it matters: AI-powered vision systems reduce manual inspection costs, enhance accuracy, and improve production efficiency.
Essential for Software Engineering & DevOps in LabVIEW
For teams developing large-scale LabVIEW applications, applying software engineering principles is critical.
1. Unit Testing, CI/CD & Code Quality Enforcement
Unit testing with JKI VI Tester or NI Unit Test Framework (UTF).
Applying CI/CD pipelines (Jenkins, GitHub Actions, Bitbucket Pipelines) for LabVIEW deployment.
Version control with Git to track changes and collaborate efficiently.
Why it matters: Applying DevOps best practices in LabVIEW improves code reliability, collaboration, and deployment automation.
2. NI SystemLink & Remote LabVIEW Applications
Using NI SystemLink for centralized test data management and system monitoring.
Deploying web-based dashboards with LabVIEW NXG Web Module for remote access.
Why it matters: LabVIEW applications are moving towards remote monitoring, IIoT, and cloud-connected test systems.
LabVIEW is evolving, and so should your expertise. Whether you're working in test automation, real-time control, embedded systems, or AI-driven automation, mastering these essential LabVIEW skills will keep you at the forefront of the industry.
What’s next?
Apply these skills to real-world projects.
Explore AI and IoT integrations to future-proof your LabVIEW career.
Participate in NI forums, LabVIEW user groups, and advanced LabVIEW training.
In accordance with McKinsey Electronics’ effort to empower STEM professionals in the GCC area, the company hosts LabVIEW Core 1 & 2 on-site training programs designed for university professors and industry professionals seeking to elevate their expertise. This comprehensive program empowers participants to create hands-on, industry-relevant projects providing students with practical skills, bridging the gap between academia and real-world applications. Don’t miss this opportunity to advance in your career, enhance your teaching, research and professional impact.
By staying ahead in 2025, you'll be equipped to develop scalable, high-performance and future-ready LabVIEW applications!