# Serverless Analytics

> Cloud-based analytics with automatic scaling and pay-per-query pricing without infrastructure management.

- **Category**: Data Science
- **Subcategory**: Cloud
- **Canonical URL**: https://designpattern.fyi/patterns/serverless-analytics/

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## Description
**Context**: Managing analytics infrastructure is complex. Serverless analytics platforms automatically scale resources and charge based on actual query usage.


## Use Cases
Sporadic analytics workloads, variable query patterns, and teams wanting to focus on queries rather than infrastructure.



## Implementation Example

```python
# 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()
```



## Trade-offs


### Advantages

- - No infrastructure management

- - Automatic scaling

- - Pay-per-query pricing

- - Fast deployment




### Considerations & Drawbacks

- - Cold start latency

- - Cost unpredictability

- - Limited customization

- - Vendor lock-in risk







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**Reference**: [Original Source](https://cloud.google.com/bigquery)