GCP Study Hub

A comprehensive guide to using Datastream for replicating on-premises databases to Google Cloud with real-time change data capture and synchronization.

A practical guide to choosing between Google Cloud's Dataproc and Dataflow services, exploring the trade-offs between managed Hadoop/Spark clusters and serverless Apache Beam pipelines.

Google Cloud Datastream is a fully managed, serverless service for change data capture and real-time database replication. This guide explains how Datastream works and when to use it.

Understanding the distinction between data ingestion and data storage is fundamental to building effective data systems on Google Cloud Platform.

Master the technique of debugging missing messages in Google Cloud Dataflow streaming pipelines by temporarily converting to batch mode and comparing outputs.

Master Cloud Composer triggering patterns with practical examples comparing scheduled, event-driven, and manual approaches for Google Cloud certification exam success.

Understanding BigQuery role scopes is critical for building secure data platforms. This guide explains the trade-offs between granting permissions at different levels and how to choose the right approach.

Choosing between Transfer Appliance and Storage Transfer Service depends on data volume and network bandwidth. This guide explains the decision criteria and shows you how to calculate transfer times.

Learn the key differences between Cloud Workflows, Cloud Functions, and Cloud Composer to choose the right GCP service orchestration tool for your automation needs.

Updating streaming pipelines without losing data requires understanding the trade-offs between hot updates and graceful drains. This guide explains both approaches for Google Cloud Dataflow.