---
title: "Short note: Amazon cloud"
subject: "Big Data Technologies"
chapter: 1
key: "semester_8/big_data_technologies:short-note-amazon-cloud"
source: https://www.bctengineer.com/q/semester_8/big_data_technologies/short-note-amazon-cloud
site: BCT Engineering
---

# Short note: Amazon cloud

Big Data Technologies · Chapter 1: Introduction to Big Data

- Asked in: 2073 Bhadra, 5 marks
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_8/big_data_technologies/short-note-amazon-cloud
- Whole chapter: https://www.bctengineer.com/md/semester_8/big_data_technologies/chapter-1

## Question

Write a short note on Amazon cloud.

## Short answer

Amazon Web Services (AWS) provides on-demand cloud computing services such as EC2 virtual servers, S3 object storage, EMR managed Hadoop/Spark, and Redshift data warehouse. Users pay only for resources consumed, enabling elastic scaling without upfront hardware investment.

## Answer

**Amazon cloud** means **Amazon Web Services (AWS)**, a cloud computing platform from Amazon that provides computing, storage, database and analytics services over the Internet on a pay-as-you-go basis. Users rent resources instead of buying and maintaining hardware.

#### Main services relevant to big data
| Service | Purpose |
|---|---|
| EC2 (Elastic Compute Cloud) | Resizable virtual servers |
| S3 (Simple Storage Service) | Scalable object storage for any amount of data |
| EMR (Elastic MapReduce) | Managed Hadoop/Spark clusters for processing |
| DynamoDB | Managed NoSQL key-value/document database |
| Redshift | Petabyte-scale data warehouse |
| RDS | Managed relational databases |
| Kinesis | Real-time data streaming |
| Glue / Athena | ETL and SQL queries over data in S3 |
| Lambda | Serverless code execution |

#### Example
Log files are uploaded to S3, an EMR cluster of EC2 instances runs a MapReduce or Spark job, the result goes back to S3 or Redshift, and the cluster is shut down to stop billing.

#### Features and advantages
- Elasticity: scale up or down on demand
- Low cost: no upfront investment, pay for use
- High availability through regions and availability zones
- Security and global reach
- Integration with Hadoop ecosystem

#### Limitations
Dependence on Internet and vendor, data-transfer costs, and data-privacy concerns.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. Which pairing of service and purpose is correct?
   A. EC2 - object storage
   B. S3 - virtual servers
   C. EMR - managed Hadoop/Spark clusters
   D. Redshift - serverless code execution
   Answer: C. EMR provides managed Hadoop/Spark; EC2 gives virtual servers, S3 object storage, Redshift a data warehouse, Lambda serverless code. (Key point: AWS service purposes)

2. In the example, what is done after the EMR job finishes?
   A. Logs are deleted from S3
   B. The cluster is left running
   C. Results go to S3 or Redshift and the cluster is shut down to stop billing
   D. Data is moved to EC2 disks only
   Answer: C. Results are saved to S3 or Redshift and the cluster is shut down so billing stops, a benefit of pay-as-you-go. (Key point: Typical EMR workflow)