Start here in GCP
The most useful guides in this topic, not just the most recent.
GCP Guides & Tutorials
Google Cloud for data — BigQuery, Dataflow, Pub/Sub, Vertex AI, Cloud Storage and the Professional Data Engineer path.
GCP articles
How to Start a Career in Google Cloud in 2026
Google Cloud is the third of the big clouds by use, and most of its users work with another cloud too. But in some areas it is unusually strong: data, AI, mobile apps, and India. This guide uses survey data to show where those openings are, then sets out a 2026 start that spends nothing until you are ready.
Vertex AI Pipelines for MLOps on GCP
A model trained once in a notebook goes stale. A pipeline retrains it the same way every time and decides whether the new one is good enough. This guide explains components, artifacts and the compiled graph on what was Vertex AI Pipelines, and compiles and runs a retraining pipeline with a promotion gate.
Building a Real-Time Dashboard with GCP
A real-time dashboard is a chain, and it is as fresh as its slowest link. This guide builds one on Google Cloud, from events in Pub/Sub to a per-minute table in BigQuery and a report in Data Studio, and replays an hour of real events to show why recent numbers change after they first appear.
GCP Professional Data Engineer Certification Guide
The Professional Data Engineer exam tests design judgement more than commands. This guide sets out the facts from Google's exam page, the five sections and their weights, a table matching the product names in the exam guide to the names in today's documentation, and a way to prepare by building.
Migrating to BigQuery: A Practical Playbook
Moving a warehouse to BigQuery is less a copy job than a proof: that the SQL still means the same thing and the numbers still match. This playbook follows Google's iterative method, then reproduces two steps locally: a translator that gets one statement wrong, and a copy that passes row counts while 23 rows differ.
Scheduling Queries in BigQuery
A scheduled query is SQL that BigQuery runs for you on a timetable. This guide covers the run parameters, destination table templates checked against Google's own examples, backfills, credentials and notifications, and shows on real data why an appending statement doubles on a rerun while a MERGE does not.