Day 4: Cloud Run and Serverless

Today's Objective

Deploy containers without managing servers. Cloud Run, Cloud Functions, Eventarc, and Pub/Sub — the serverless stack that scales to zero and back.

BigQuery Basics

BigQuery is Google's serverless data warehouse. You pay per query (first 1TB/month free). It can analyze terabytes in seconds using standard SQL.

bq CLI

# Create a dataset
bq mk --dataset my_project:my_dataset

# Load a CSV file
bq load \
  --source_format=CSV \
  --autodetect \
  my_dataset.my_table \
  gs://my-bucket/data.csv

# Run a query
bq query --use_legacy_sql=false \
  'SELECT name, COUNT(*) as count FROM my_dataset.my_table GROUP BY name ORDER BY count DESC LIMIT 10'

SQL in BigQuery Console

-- BigQuery supports standard SQL
SELECT
  DATE(created_at) as day,
  COUNT(*) as events,
  SUM(revenue) as total_revenue,
  AVG(revenue) as avg_revenue
FROM `my_project.analytics.events`
WHERE created_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND status = 'completed'
GROUP BY day
ORDER BY day DESC

-- Window functions work
SELECT
  user_id,
  event_time,
  ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time) as session_event
FROM `my_project.analytics.events`

💡

Partition your tables by date column when working with time-series data. Queries that filter by date then only scan the relevant partitions instead of the whole table — dramatically cheaper and faster: PARTITION BY DATE(created_at).

📝 Day 4 Exercise

Analyze a Public Dataset

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Day 4 Summary

BigQuery is serverless — no servers to manage. Pay per TB scanned (first 1TB/month free).
Standard SQL. Window functions, subqueries, CTEs, JOINs — it all works.
Partition by date for time-series data. Clustering further improves query performance.
Looker Studio connects directly to BigQuery for free visualization dashboards.

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← Previous

Day 3: Storage: GCS and Cloud SQL

Day 5: AI APIs and Vertex AI

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What's Next

The foundations from today carry directly into Day 5. In the next session the focus shifts to Monitoring, Logging, and CI/CD — building directly on everything covered here.

Supporting Videos & Reading

Go deeper with these external references.

YouTube

Cloud Run and Serverless — Video Walkthroughs Community tutorials and walkthroughs covering the concepts in this lesson.

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YouTube

Cloud Run and Serverless Explained Deep-dive explanations and live-coding sessions from top educators.

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Official Docs

Official Documentation Primary reference documentation for the technologies covered in this lesson.

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GitHub

Open Source Examples Real-world codebases demonstrating the patterns taught in this lesson.

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Day 4 Checkpoint

Before moving on, verify you can answer these without looking:

What is the core concept introduced in this lesson, and why does it matter?
What are the two or three most common mistakes practitioners make with this topic?
Can you explain the key code pattern from this lesson to a colleague in plain language?
What would break first if you skipped the safeguards or best practices described here?
How does today's topic connect to what comes in Day 5?

Live Bootcamp

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Thu–Fri sessions in Denver, Los Angeles, New York, Chicago, and Dallas. $1,490 per seat. June–October 2026.

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Continue To Day 5

Day 5: AI APIs and Vertex AI

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