GCP Study Hub

A comprehensive guide to Cloud Logging in Google Cloud Platform, covering how to collect, store, search, and analyze logs from GCP services and applications for effective monitoring and troubleshooting.

Cloud Build triggers automate your CI/CD pipeline by initiating builds based on repository events. Learn when and how to use branch, tag, pull request, and manual triggers in GCP.

Moving from on-premises Hadoop and Spark to Cloud Dataproc requires strategic thinking. This guide explains when migration makes sense and how to approach it effectively.

When you've built data transformations in PySpark but realize BigQuery would be more efficient, the Spark BigQuery Connector lets you bridge both platforms seamlessly.

Understanding the three fundamental data types and how Google Cloud handles each is essential for designing effective AI and analytics systems.

Understand the four main types of generative AI models—text, image, video, and audio—and see how they work through concrete examples as the field evolves toward multimodal systems.

Finding BigQuery logs in Cloud Logging requires understanding how Google Cloud organizes and labels different query activities. This guide explains the practical filtering techniques that help you locate the logs that actually matter.

Understanding the differences between built-in, custom, and external cloud monitoring metrics helps you make better decisions about observability strategy, cost management, and operational complexity.

A comprehensive guide to configuring Cloud Monitoring alerts for Memorystore Redis memory usage, helping you proactively manage your Redis instances before they become overloaded.

BigQuery offers both exact and approximate aggregation functions. Understanding when to use each can dramatically improve query performance while maintaining acceptable accuracy for your business needs.