This content originally appeared on DEV Community and was authored by Skill Tester Techy
In a world driven by data, automation, and AI, organizations that continuously upskill their teams stay ahead of disruption.
Whether you’re building machine learning models, modernizing applications, or leading GenAI adoption, these Google-authorized courses from NetCom Learning help your teams gain hands-on expertise with measurable business impact.
Here are powerful Google Cloud learning paths to future-proof your workforce.
- Generative AI Leader – Build Strategic AI Vision Start with the Generative AI Leader course—ideal for executives and business leaders. It focuses on AI strategy, responsible AI, and transformation frameworks to help you lead organizational adoption confidently.
- Vertex AI Agent Builder – Create Intelligent Chatbots and Agents The Vertex AI Agent Builder course teaches teams to design conversational agents and automate workflows using Google’s Vertex AI platform—perfect for organizations scaling customer support or internal AI assistants.
- Gemini for Google Workspace – Automate Everyday Productivity In the Gemini for Google Workspace course, teams learn how to integrate AI assistance into Gmail, Docs, Sheets, and Meet, streamlining repetitive tasks and boosting collaboration across departments.
- Google Generative AI – Learn the Full AI Ecosystem Explore the Google Generative AI training learning path to gain a comprehensive understanding of generative AI models, including prompt design, fine-tuning, and ethical implementation.
- Machine Learning on Google Cloud – From Models to Deployment The Machine Learning on Google Cloud course equips teams with end-to-end ML pipeline skills—from data preparation to model deployment using Vertex AI, AutoML, and TensorFlow.
- Preparing for Professional Machine Learning Engineer – Validate Expertise For those pursuing certification, Preparing for Professional Machine Learning Engineer provides structured guidance aligned with Google’s exam blueprint, ensuring your team builds scalable and responsible ML systems.
- Data Engineering on Google Cloud – Build a Data Foundation The Data Engineering on Google Cloud course helps engineers design and manage data pipelines, warehouses, and processing systems using BigQuery, Dataflow, and Dataproc.
- Analyzing and Visualizing Data in Looker – Turn Data into Insights Through Analyzing and Visualizing Data in Looker, analysts learn how to create dashboards, explore datasets, and visualize KPIs that drive smarter decisions. To go deeper, your teams can take Developing Data Models with LookML to design and maintain scalable, reusable data models.
- Orchestrate BigQuery Workloads with Dataform – Automate Data Operations The Orchestrate BigQuery Workloads with Dataform course teaches how to automate data pipelines and transformations, improving analytics performance and reducing operational overhead.
- Getting Started with Google Kubernetes Engine (GKE) – Deploy Containers Easily Start your cloud-native journey with Getting Started with Google Kubernetes Engine. This course helps teams deploy, manage, and scale containerized applications efficiently using Kubernetes. Then move into Architecting with Google Kubernetes Engine to master enterprise-level architecture design, security, and automation.
- Hybrid Cloud: Modernizing Applications with Anthos – Simplify Multi-Cloud Management With Hybrid Cloud: Modernizing Applications with Anthos, your IT teams learn how to modernize legacy systems and manage workloads across on-premise and cloud environments—a must for hybrid infrastructure strategies.
- Core Cloud Engineer Pathways – Strengthen the Foundation Build solid cloud fundamentals with these essential certifications: Introduction to Data Analytics on Google Cloud – Learn how data turns into insights. Developing Applications with Google Cloud – Master app deployment and CI/CD. Preparing for Your Associate Cloud Engineer Journey – Get ready for foundational certification. Preparing for Your Professional Cloud Network Engineer Journey – Understand VPCs, hybrid connectivity, and security. Serverless Data Processing with Dataflow – Build real-time, scalable pipelines.
Why Upskilling on Google Cloud Matters Now
AI-first transformation is reshaping every industry—from financial services to retail.
Data fluency empowers teams to make faster, smarter business decisions.
Cloud-native skills ensure agility, scalability, and security for enterprise workloads.
By training with Google Cloud courses through NetCom Learning, your teams gain hands-on experience, certification readiness, and practical projects that translate learning directly into business results.
Empower Your Teams for the Next Decade of Innovation
Whether you’re upskilling 10 engineers or 200 employees, NetCom Learning’s Google Cloud training portfolio gives your organization a clear advantage.
Authorized by Google Cloud
Delivered by certified instructors
Designed for enterprise outcomes
Start your team’s upskilling journey today → Explore Google Cloud training and prepare your workforce for the future of data, AI, and cloud innovation.
Google Cloud Pub/Sub: Real-Time Messaging for Mode
In today’s data-driven world, real-time communication between systems is essential. Whether it’s streaming analytics, IoT data collection, or event notifications, businesses need reliable messaging that connects applications instantly. That’s where Google Cloud Pub/Sub steps in a fully managed, serverless messaging service built for seamless data movement. For downstream analytics, see how Pub/Sub complements Google BigQuery and reporting withGoogle Data Studio.
What Is Google Cloud Pub/Sub?
Pub/Sub (short for publish/subscribe) is Google Cloud’s global messaging middleware. It allows independent systems to communicate asynchronously by sending and receiving messages in real time.
Think of it as the connective tissue of your data ecosystem — linking services like Cloud Dataflow, BigQuery, and Cloud Functions so data flows automatically across your architecture. It also pairs well with managed databases like Google Cloud SQL for operational workloads and with API layers such as Google Apigee.
With Pub/Sub, you can:
Stream millions of events per second
Decouple services for easier scalability
Handle both real-time and batch workloads
Ensure message durability and delivery across global regions
How It Works: Publisher, Subscriber, and Topics
At its core, Pub/Sub follows a simple yet powerful model:
Publisher: Sends messages to a topic (like an event or data update).
Topic: The channel where messages are stored temporarily.
Subscriber: Receives messages from that topic.
For example, an IoT device (publisher) sends sensor data to a Pub/Sub topic, which then forwards that data to a streaming pipeline and into analytics layers such as Google BigQuery or dashboards inGoogle Data Studio. If you’re modernizing from legacy systems, this decoupled model pairs naturally with database modernization and staged database migration.
Key Benefits for Enterprises
- Real-Time Insights: Process and analyze data the moment it’s generated — ideal for AI-driven use cases like anomaly detection and personalization (explore practical AI use cases from startups).
- Scalability: Automatically handles traffic spikes without manual provisioning; fan out events to microservices exposed via Apigee.
- Reliability & Security: Durable storage and at-least-once delivery; pair with network best practices and awareness of risks such as DoS vs. DDoS when designing internet-facing producers/consumers.
- Integration: Works natively with Dataflow, Cloud Functions, BigQuery, and Cloud Run — and can trigger ML workflows including Natural Language Processing or LLM services on Vertex AI (see our look at Claude on Vertex AI).
- Cost-Efficiency: Pay for throughput and storage — no servers to manage. If you’re evaluating AI frameworks downstream, consider trade-offs like PyTorch vs. TensorFlow. Building Event-Driven Data Pipelines with Pub/Sub When combined with Dataflow, Pub/Sub becomes the foundation of a robust streaming pipeline. Data published to Pub/Sub topics can trigger real-time transformations in Dataflow before landing in BigQuery for analytics — unlocking instant dashboards in Looker Studio. For operational data, route targeted events to Cloud SQL, and expose downstream services through Apigee. Common use cases: IoT data ingestion for telemetry and alerts (great fit for AI agents in business that act on events) Fraud detection with streaming features feeding ML models Real-time dashboards and alerts (see Looker Studio) E-commerce event tracking (orders, carts, payments) supporting phased data modernization Final Thoughts As organizations embrace real-time operations, Google Cloud Pub/Sub provides a reliable backbone for event-driven systems. Its seamless integration with other Google Cloud tools makes it ideal for businesses aiming to modernize data infrastructure, improve responsiveness, and accelerate decision-making — while aligning with initiatives like database migrations and analytics on BigQuery.
This content originally appeared on DEV Community and was authored by Skill Tester Techy