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GCP Guides & Tutorials
Google Cloud for data — BigQuery, Dataflow, Pub/Sub, Vertex AI, Cloud Storage and the Professional Data Engineer path.
GCP articles
GCP Roadmap 2026 for Data Engineers
On Google Cloud the warehouse is the centre of the data stack, so this roadmap starts inside BigQuery, in the free sandbox, and moves outward in seven rings: storage and access, loading, table design, transformation, streaming and operations. Each ring has a build, and the renamed services are listed.
BigQuery Explained: Google's Serverless Data Warehouse
What “serverless” means in BigQuery, why its bill depends on the bytes a query reads, and the three habits that keep that number small: select only needed columns, partition and cluster tables, and check a query’s cost before running it.
Dataflow Tutorial: Stream and Batch Processing on GCP
Dataflow runs Apache Beam pipelines as a managed service, with one programming model for batch and streaming. This tutorial explains the model, runs a real pipeline locally on 77,033 taxi trips with a batch branch and a windowed branch, and covers windows, watermarks, late data, templates and how to run on Dataflow.
Google Cloud Storage: Buckets, Classes and Best Practices
Google Cloud Storage holds the raw files of almost every data platform on Google Cloud. This guide covers buckets and naming, choosing a location, the storage classes with their minimum durations and retrieval fees, lifecycle rules and how conflicts between them are resolved, soft delete, and access control.
Pub/Sub Explained: Messaging and Streaming on GCP
Pub/Sub carries messages from the systems that produce events to the systems that use them, without either knowing about the other. This guide explains topics, subscriptions and the three delivery types, models why a message can arrive more than once, and covers ordering, dead-letter topics, retention and cost.
Vertex AI Tutorial: ML on Google Cloud
Vertex AI is now called Gemini Enterprise Agent Platform, and its machine learning tools work as before. This tutorial follows one model from data to predictions, explains the contract your training code must follow, and trains a real model locally to show what testing on a later day reveals.