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BigQuery

The serverless data warehouse that changed analytics: terabyte queries in seconds, zero infrastructure, AI built in.

FOCUS · ANALYTICS WITHOUT IRONYou load the data and you query: no clusters, no vacuum, no excuses
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YoctoIT material for clients and partners · Google Cloud, BigQuery, Gemini and the other products mentioned are trademarks of Google LLC.
01 · What it is

BigQuery, made clear.

BigQuery turned analytics into a service: no cluster to size, storage and compute separated, SQL queries on terabytes in seconds, and you pay for what you query (or reserved capacity). Around it: native streaming, ML in SQL (BQML), Gemini writing the queries and BigLake on the data lake.

Serverless
zero infrastructure: the warehouse is an API
Seconds
terabyte scans: Google's columns and Google's scale
BQML
machine learning in SQL: forecasts without leaving the warehouse
BigQuery
OFFICIAL GOOGLE CLOUD BRANDING · BIGQUERY
CONSOLE REALE · BIGQUERY EXPLORER · FONTE: GOOGLE CLOUD DOCS
REAL CONSOLE · BIGQUERY EXPLORER · SOURCE: GOOGLE CLOUD DOCS
02 · How to use it well

The things that make the difference.

L'architettura

BI, data science, applicationswho queries
SQL engine
Streaming
BQML & Gemini
queries · real time · intelligence
Separate columnar storageit grows on its own, it costs little
BigLake on the data lakefiles too, without copying them
The warehouse as a service, for real

Modeling that saves money

Partitions, clustering and materializations: the same question can cost 100x less if the data is designed well.

Slots or on-demand

Reserved capacity for the constant workloads, on-demand for exploration: the mix is FinOps.

The ERP inside

Pipelines from SAP, IBM i and your databases: the company's estate finally queryable together.

Self-service governance

Authorized datasets, views and policy tags: data democracy without anarchy.

03 · In depth

Slots, storage and the serverless engine

BigQuery separates storage (columnar, on consumption, with 7-day time travel) and compute (slots: on-demand per TB scanned or capacity with the editions and autoscaling). Tables are partitioned (date/integer) and clustered to cut the scans; materialized views speed up recurring patterns; BigLake extends the engine to Parquet/Iceberg on object storage; Omni queries S3 and Azure too. Governance goes through datasets, policy tags and row/column level security.

  • Slot & edition — on-demand to start, capacity+autoscaler at cruise: the cost designed
  • Partizioni+cluster — scans cut to the bone: the query pays only for what it touches
  • Time travel — 7 days of undo: the table restored to a timestamp
  • BigLake/Iceberg — the open lake under the same engine and the same security
  • Policy tag — column security declared in the catalog: PII masked by role
  • Gemini in BigQuery — SQL generated and explained: the analyst accelerated
04 · Numbers and lifecycle

The numbers that matter.

PB
query scale without infrastructure to manage
7 gg
the time travel included
99,99%
SLA on the Enterprise Plus edition
0
clusters to administer: true serverless
BigQuery is governed on the model and the slots: partitions, editions and cost control tuned by us — analytics without surprise bills.
05 · Use cases

Where it really pays off.

Corporate BI

The warehouse behind Looker and the executive reports.

Log analytics

Billions of events queried on demand.

ML on the warehouse

Churn, forecasts and segmentations in SQL.

BigQuery removed the excuses from analytics: we remove the waste and bring the method.