01SYSTEMS
System Foundations
Linux · Networking · Security · Shell · Processes · Storage · Troubleshooting
OPEN SOURCE • DATA • EMBEDDED • SECURITY • AI
Build deeper
technical capability.
Advancing Technology. Empowering People.
Engineering-led training and technology consulting across Linux, DevOps, cloud-native systems, data engineering, big data and Apache Spark, programming, embedded systems, AAOS, security, AI Engineering, LLM systems, RAG and agentic applications.
Training • Consulting • Data Engineering • Spark • Embedded • AAOS • Security • AI Engineering
ENGINEERING-LED
Concepts taught from first principles.
HANDS-ON
Labs designed around real systems.
OPEN TECHNOLOGY
Open standards and open-source tools wherever practical.
PRODUCTION PERSPECTIVE
Architecture, failures, troubleshooting and operations included.
Technology capability map
Technology is connected.
Learning should be too.
Modern engineering roles span multiple layers. Foundations feed automation, automation feeds cloud and data platforms, software connects the layers, and intelligent systems still depend on reliable infrastructure and data underneath.
Explore the relationships02AUTOMATION
Automation & Delivery
Git · Python · Ansible · CI/CD · Infrastructure Automation
03CLOUD + DATA
Cloud-Native & Data Platforms
Containers · Docker · Kubernetes · Big Data · Apache Spark · Kafka · Trino · Lakehouse · Airflow · Observability
04SOFTWARE + EMBEDDED
Software & Embedded Engineering
Python · Rust · C · C++ · Embedded Systems · AAOS · APIs · Concurrency · Systems Programming
05AI ENGINEERING
Intelligent Systems & AI Engineering
AI Engineering · Machine Learning · LLMs · Embeddings · Vector Search · RAG · MCP · Tool Use · AI Agents · Agentic AI
Trace
LinuxNetworkingContainersKubernetes
01
System FoundationsSYSTEMS
ShellProcessesStorageTroubleshooting
02
Automation & DeliveryAUTOMATION
Infrastructure Automation
03
Cloud-Native & Data PlatformsCLOUD + DATA
DockerLakehouseAirflow
04
Software & Embedded EngineeringSOFTWARE + EMBEDDED
Systems Programming
05
Intelligent Systems & AI EngineeringAI ENGINEERING
Machine LearningEmbeddingsVector SearchTool Use
Training
Learn from fundamentals to architecture.
The homepage stays concise. Each domain now has a dedicated training page with representative modules, labs, prerequisites, outcomes and customization guidance.
01
Linux & Open Source
LinuxNetworkingPerformanceTroubleshootingOpen Infrastructure
FOUNDATIONS → PRODUCTION
View Training Topics 02
DevOps & Platform Engineering
GitCI/CDContainersKubernetesAutomationObservabilityPlatform Engineering
BUILD → AUTOMATE → OPERATE
View Training Topics 03
Data Engineering & Big Data
Big DataApache SparkPySparkKafkaFlinkTrinoAirflowLakehouse
INGEST → PROCESS → STREAM → SERVE
View Training Topics 04
Programming & Systems
PythonRustCC++GoNetworkingConcurrency
CODE → ENGINEER
View Training Topics 05
Embedded Systems
Embedded C/C++Embedded LinuxBSPDriversInterfacesDebugging
DEVICE → PLATFORM
View Training Topics 06
Automotive & AAOS
AAOSCar ServiceHAL/VHALAIDLVehicle PropertiesDiagnostics
ANDROID → VEHICLE
View Training Topics 07
Security Engineering
Linux SecurityDevSecOpsContainersKubernetesApplication SecurityAI Security
UNDERSTAND → HARDEN → VERIFY
View Training Topics 08
AI Engineering
AI/ML FundamentalsLLMsRAGVector SearchMCPAI AgentsEvaluationMLOps / LLMOps
LLMS → RAG → AGENTS → PRODUCTION
View Training Topics Learning methodology
Understand. Experiment. Build. Debug. Apply.
01
WHY
Understand the engineering problem.
02
CONCEPT
Build the mental model.
03
VISUALIZE
See architecture and data movement.
04
LAB
Experiment with the technology.
05
BUILD
Implement realistic systems.
06
DEBUG
Learn failure modes and diagnosis.
07
ARCHITECTURE
Understand production design decisions.
Lab experience
Learn by operating real systems.
Representative scenarios move beyond command copying: observe, change, break, diagnose and explain the system.
Diagnose a Linux service failure.
Build a secure automation workflow.
Deploy a multi-node Kubernetes environment.
Create observability with Prometheus and Grafana.
Build a Spark/PySpark batch data pipeline and tune a slow job.
Stream events through Kafka and process them with Spark Structured Streaming.
Build a Python service.
Explore memory safety using Rust.
Build an LLM RAG pipeline.
Design an AI agent with tools and memory.
Consulting
Complex systems need engineering judgment.
The homepage stays concise. Each consulting domain now has a dedicated page covering client situations, assessment scope, representative engagements, deliverables and intended outcomes.
Architecture & Technical Advisory
- —Current-state architecture
- —Target-state design
- —Technology evaluation
- —Integration patterns
- —Resilience and operability
Linux & Open Infrastructure
- —Linux architecture
- —Service design
- —Storage and filesystems
- —Networking
- —Security hardening
DevOps & Platform Engineering
- —CI/CD architecture
- —Infrastructure automation
- —Container platform design
- —Kubernetes architecture
- —Platform engineering
Data Engineering & Big Data
- —Data platform architecture
- —Apache Spark / PySpark
- —Kafka and event streaming
- —Trino / distributed SQL
- —Lakehouse design
Embedded Systems
- —Embedded Linux architecture
- —BSP integration
- —Boot and startup
- —Drivers and interfaces
- —IPC / networking
Automotive & AAOS
- —AAOS architecture
- —Car Service and Car APIs
- —Vehicle Properties
- —HAL / VHAL
- —AIDL
Security Engineering
- —Linux hardening
- —Network exposure
- —DevSecOps
- —Container / Kubernetes security
- —Application / API security
AI Engineering
- —AI / LLM architecture
- —RAG and retrieval
- —Vector search
- —Agentic workflows
- —MCP / tools
Cloud & Platform Architecture
- —Cloud landing architecture
- —Networking and connectivity
- —Identity and access
- —Compute / container platforms
- —Observability
Training
Training informs consulting.
Teaching forces complex systems to be understood clearly, structurally and from first principles.
TRAIN
↕
BUILD
↕
SOLVE
↕
TEACH
↕
BUILD
↕
SOLVE
↕
TEACH
Consulting
Consulting informs training.
Real architecture, troubleshooting and implementation experience keeps training grounded in practical engineering problems.
Why this approach
01
Fundamentals before tools.
Tools change. Engineering principles remain.
02
Explain why before how.
Commands make more sense when learners understand the system underneath them.
03
Labs are part of the curriculum.
Not demonstrations added at the end.
04
Failures are learning material.
Troubleshooting builds deeper engineering judgment.
05
Architecture matters.
Advanced learners should understand systems, not only components.
06
Open wherever practical.
Prefer open technologies and open standards when appropriate.
Engagement models
Different requirements need different formats.
Individual Training
Structured skill development.
Corporate Training
Programs aligned to team objectives.
Technical Workshops
Focused deep-dive sessions.
Architecture Labs
Scenario-driven advanced programs.
Custom Programs
Curricula built around an organization's stack.
Consulting
Architecture, troubleshooting and implementation advisory.
Delivery: Online · On-site · Hybrid — subject to program scope and availability.
Path builder
Build your learning path.
A deterministic starting path—not fake AI personalization. Use it to see how capabilities stack together.
Recommended sequence
PythonLLM FundamentalsEmbeddingsRAGTool UseMCPAgent ArchitectureEvaluationDeployment
Final scope depends on prerequisites, delivery duration and engineering objectives.
Process
From requirement to capability.
01
Understand
Current environment, skill level, goals and constraints.
02
Design
Create training or consulting scope.
03
Build
Develop curriculum, labs, architecture or solution approach.
04
Deliver
Execute the engagement.
05
Apply
Connect outcomes to real engineering requirements.
What defines the experience
Proof should come from the work—not invented numbers.
Technical clarity
Practical depth
Architecture context
Troubleshooting mindset
Open engineering
Representative case studies
See the engineering method applied to real-world problem patterns.
Architecture, performance, security and diagnostics become more credible when the reasoning is visible.
01Data Engineering & Big Data
Recovering a slow Spark data pipeline
A representative performance-engineering scenario for a Spark/PySpark workload that becomes slower and less predictable as data volume and transformation complexity grow.
Read the scenario02DevOps & Platform Engineering
Assessing Kubernetes production readiness
A representative architecture-review scenario for a Kubernetes platform that works functionally but needs stronger production resilience, security and operability.
Read the scenario03AI Engineering
Taking a RAG system from demo to production
A representative AI-engineering scenario for a RAG prototype that produces promising demos but lacks measurable retrieval quality, evaluation and production controls.
Read the scenarioRepresentative engineering scenario. It illustrates the type of problem-solving approach GNU Group can provide; it is not presented as a named-client testimonial or a claim of specific measured results.
Why GNU Group
Technical credibility should be inspectable.
The goal is not to sound bigger than the work. It is to make the engineering approach, scope and expected deliverables clear enough for a client to judge.
Why GNU GroupConnected engineering
Training and consulting span systems, platforms, data, embedded, security and AI without pretending those layers are independent.
Clear enough to verify
Evidence-led
Architecture and troubleshooting recommendations begin with current state, constraints and observable behavior.
Clear enough to verify
Knowledge transfer
Consulting should improve the client's own engineering capability, not create unnecessary dependency.
Clear enough to verify
Proof without fabrication
No invented client counts, success percentages, testimonials or partner badges.
Clear enough to verify
ENGINEERING KNOWLEDGE
From the field to the knowledge base.
Technical articles connect fundamentals, architecture, implementation and production operations.
AAOS
AAOS Architecture Fundamentals
A systems-level view of Android Automotive OS, vehicle integration and the engineering boundaries that matter.
Data Engineering
Apache Spark for Production Data Engineering
A practical engineering guide to Spark architecture, PySpark pipelines, partitioning, performance, streaming and production operations.
Embedded Systems
Embedded Linux Production Basics
A practical introduction to the layers that turn Linux into a reliable embedded platform.
FAQ
Questions before the first conversation.
Yes. Programs can begin with first principles before progressively moving into implementation, troubleshooting and architecture.
Yes. Advanced programs can focus on architecture, internals, troubleshooting, performance and production engineering rather than introductory material.
Yes. Programs combine conceptual explanation, visual architecture, guided labs, experiments, failure scenarios and practical exercises.
Yes. Corporate programs can be adapted around the organization's technology stack, engineering environment, skill gaps and desired outcomes.
The site represents major capability areas rather than every possible technology. Send the requirement and we can assess fit and scope.
Yes. Consulting covers architecture, Linux/open infrastructure, DevOps and platforms, data engineering and big data, Apache Spark, embedded systems, AAOS, security, cloud architecture and AI engineering. The Consulting Catalogue describes representative situations, assessment scope, engagements, deliverables and outcomes.
Deliverables depend on scope and can include current-state findings, architecture recommendations, reference designs, risk registers, performance or security evidence, PoCs, implementation roadmaps, runbooks and knowledge-transfer sessions.
Implementation availability depends on scope and engagement model. We will distinguish advisory work from hands-on implementation in the agreed statement of work.
No. The AI track covers machine learning fundamentals, LLM architecture, embeddings, vector databases, RAG, MCP, tools, agents, evaluation and production AI systems.
Agentic AI systems combine models with tools, memory, workflows and decision logic so software can perform multi-step tasks rather than only generate responses.
Because modern engineering products cross layers. Data platforms depend on distributed systems, storage, networking and observability; embedded and automotive platforms depend on operating systems and security; cloud and AI systems depend on software, infrastructure, automation and reliable data pipelines.
Open-source and freely accessible tools are preferred wherever practical and appropriate to the engineering requirement.
Online, on-site and hybrid delivery can be supported where available and agreed for the program.
Certificate availability depends on the program. Contact us for details.
Engineering capability is connected
Build capability. Solve harder problems.
Whether the requirement is training, architecture, cloud platforms, data engineering and Spark, embedded/AAOS engineering, security, troubleshooting or AI engineering and intelligent-system development, start with the engineering problem.
Start with the engineering problem
Tell us what you're trying to learn or build.
Share the technical context, current state and desired outcome. The response can then focus on scope rather than sales theatre.
Your information will only be used to respond to your enquiry.