Back to Catalog
Data Science
Cloud
Serverless Analytics
Cloud-based analytics with automatic scaling and pay-per-query pricing without infrastructure management.
Intent & Description
📋 Context
Managing analytics infrastructure is complex. Serverless analytics platforms automatically scale resources and charge based on actual query usage.
Real-world Use Case
Sporadic analytics workloads, variable query patterns, and teams wanting to focus on queries rather than infrastructure.
Source
Advantages
- No infrastructure management
- Automatic scaling
- Pay-per-query pricing
- Fast deployment
Disadvantages
- Cold start latency
- Cost unpredictability
- Limited customization
- Vendor lock-in risk
Implementation Example
# Serverless Analytics Example (BigQuery) from google.cloud import bigquery
client = bigquery.Client()
query = """ SELECT product_category, SUM(revenue) as total_revenue FROM `project.dataset.sales` WHERE date >= "2026-01-01" GROUP BY product_category """
# Query runs on serverless infrastructure results = client.query(query).to_dataframe()