Google BigQuery Training Course: Analyse, model and optimise your data at scale

The Google BigQuery training course starts from a peculiarity that throws people at first: there is no server to size, no index to create, no cluster to restart. You write SQL, and it answers — even across terabytes. So the difficulty moves elsewhere: into modelling, into partitioning, and above all into cost, because a badly written query does not become slow, it becomes expensive.

Over 3 days, you work on what actually makes the difference: the columnar architecture and what it changes about the way you write SQL, partitioning and clustering (the two levers that cut the bill), nested and repeated fields that spare you expensive joins, materialised views, streaming ingestion, access governance down to row level, and the choice between on-demand billing and reserved capacity. Every optimisation is measured: before, after, in euros.

This course extends our Google Cloud Professional Data Engineer Training Course, which covers the whole Google data chain, and our Google Cloud Associate Cloud Engineer Training Course for the infrastructure foundations. On the streaming side, our Apache Kafka Training Course illuminates the concepts you meet again in Pub/Sub. To compare ecosystems, see our AWS Data Engineer Training Course (Redshift, Athena), our Azure Data Lake, Synapse, Data Factory and Spark Training Course and our Azure Databricks Training Course. Finally, our Google Cloud Digital Leader Training Course places BigQuery within the wider data strategy.

Sessions are guaranteed from a single registrant (except in cases of force majeure). A preliminary discussion takes place between the participant and/or a company representative to fully take into account the participant’s profile. Assessment: quizzes, role-play and practical exercises. A certificate of completion is issued at the end of the training. This training covers the analytics portion of the Google Cloud Professional Data Engineer certification syllabus (exam not included). This training is part of our Cloud Computing Training Courses catalogue. Discover our other cloud training courses to master architectures, services and best practices on AWS, Azure and GCP. To get started on AWS, we recommend our AWS Cloud Practitioner Training Course as a prerequisite, which lays the foundations of the Amazon Cloud.

By the end of the training, participants will be able to:

• Explain BigQuery’s architecture (columnar storage, separated compute) and what it implies.
• Write performant SQL queries and read an execution plan.
• Partition and cluster tables to reduce the data scanned.
• Use nested and repeated fields to avoid expensive joins.
• Ingest data in batch and in streaming (Storage, Dataflow, Pub/Sub).
• Govern access down to column and row level.
• Choose between on-demand billing and reserved capacity, and steer cost.

• SQL practice is essential: you should be able to write a join, an aggregation and a subquery without hesitation. It is the only genuinely blocking prerequisite.
• Some initial Google Cloud experience (projects, IAM, Cloud Storage) is desirable but not required: the necessary concepts are covered. Some grounding in analytical modelling will make the advanced modules easier.
• Because every participant is unique, a personalised discussion with our expert allows us to design a training course perfectly aligned with their objectives.

• Data analysts and BI developers working with large volumes.
• Data engineers building warehouses and pipelines on Google Cloud.
• Database administrators moving towards cloud analytics.
• Data leads who must keep the cost of their analytical workloads under control.