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.
# Carbon Analytics at Scale with BigQuery
# BleepxCloud — ESG · Advanced
# Realistic starter — adapt names, regions and CIDRs to your environment.
terraform {
required_providers { aws = { source = "hashicorp/aws", version = "~> 5.0" } }
}
provider "aws" {
region = "eu-west-1" # pick a region close to users / data-residency rules
}
# FinOps: mandatory cost-allocation tags on every resource.
locals {
common_tags = {
Project = "bleepx-cloud"
Domain = "esg"
Mission = "bigquery-carbon-analytics"
ManagedBy = "terraform"
CostCenter = "REPLACE_ME"
}
}
data "aws_caller_identity" "current" {}
# This mission is design-led — model the services below as resources:
# - bigquery
# - looker-studio
# - carbon-api
Bleepx skills build on each other. Continue your learning path across Query, Lab, and Cloud.