A small CSV arrives in S3 and needs a quick transformation. What is the simplest fit?
Why C is the answer
An S3 event that invokes a Lambda function
Lambda suits small, event-driven work.
In detail
- A small file suits an S3 event and a Lambda function.
- Lambda has a run-time limit, so it suits small jobs.
Use case
An illustrative scenario, not a real incident.
A small CSV lands in S3, and a Lambda function reformats it within seconds.
See it in different ways
flowchart TD
A{"Transform job"} -->|"Small, event-driven"| B["AWS Lambda"]
A -->|"Serverless Spark ETL"| C["AWS Glue"]
A -->|"Big clusters you tune"| D["Amazon EMR"]mindmap
root((Transform))
Glue
Serverless Spark
Job bookmarks
EMR
Custom tuning
Lambda
Small files
DataBrew
No code
Redshift
Stored proceduressequenceDiagram participant P0 as Amazon S3 participant P1 as Lambda participant P2 as Output bucket P0->>P1: Object created P1->>P2: Write result
Key point: Lambda suits small event-driven work.
Why the other options are wrong
- A. A manual download by an analyst each morning
- Manual work does not scale.
- B. A provisioned Redshift cluster used only for that file
- A cluster for one file is wasteful.
- D. An EMR cluster that stays running all day
- An always-on cluster is far too much.