Follow the request/data flow through the architecture.
Work through each concept and mark it understood. This unlocks the knowledge check.
Serverless, petabyte-scale SQL data warehouse.
Why it matters
Analyze huge datasets with standard SQL, no infrastructure.
When to use it
Use for analytics/BI and ML on large data. Not for transactional row-level updates.
Example
SELECT region, SUM(co2_tonnes) AS emissions FROM `proj.esg.readings` WHERE _PARTITIONDATE >= '2025-01-01' GROUP BY region ORDER BY emissions DESC;
Gotcha (exam trap)
You pay per byte scanned — SELECT * on an unpartitioned table can be shockingly expensive. Partition and cluster.
A realistic main.tf for this mission. Read it top to bottom — it mirrors the concepts above.
# BigQuery: Serverless Analytics at Petabyte Scale
# BleepxCloud — GCP · Intermediate
# Realistic starter — adapt names, regions and CIDRs to your environment.
provider "google" {
project = var.project_id
region = "europe-west1"
}
variable "project_id" { type = string }
# Partitioned + clustered table keeps scan cost (and bill) down.
resource "google_bigquery_table" "readings" {
dataset_id = google_bigquery_dataset.esg.dataset_id
table_id = "readings"
time_partitioning { type = "DAY"; field = "reading_date" }
clustering = ["region"]
}
Bleepx skills build on each other. Continue your learning path across Query, Lab, and Cloud.