AWS Data Pipeline Requirements Document

Project Name

AWS Automated Data Pipeline

Objective

Design and implement a scalable, secure, and automated AWS-based data pipeline to ingest, transform, store, and analyse data from multiple sources. The pipeline should support batch and near real-time processing while ensuring high availability, data quality, and monitoring.

Business Requirements

• Automate data ingestion from source systems.

• Minimise manual intervention in data processing.

• Enable centralised data storage and analytics.

• Support reporting and dashboarding requirements.

• Ensure data security and compliance standards.

Functional Requirements

Data Ingestion

• Extract data from databases, APIs, CSV files, and cloud storage.

• Schedule ingestion jobs daily/hourly as required.

• Support incremental and full data loads.

Data Processing

• Validate incoming data for quality and completeness.

• Transform data according to business rules.

• Handle data cleansing, deduplication, and enrichment.

Data Storage

• Store raw data in Amazon S3 (Raw Layer).

• Store transformed data in Amazon S3 (Processed Layer).

• Maintain historical data for auditing and reporting.

Data Analytics

• Enable querying through Amazon Athena.

• Support dashboards using Amazon QuickSight.

• Provide aggregated datasets for business reporting.

Non-Functional Requirements

Performance

• Process daily data volumes within the defined SLA.

• Support scalable architecture for future growth.

Security

• Encrypt data at rest and in transit.

• Implement IAM roles and least-privilege access.

• Enable audit logging through AWS CloudTrail.

Reliability

• Pipeline availability target: 99.9%.

• Automated retry mechanisms for failed jobs.

• Backup and recovery procedures for critical datasets.

Monitoring

• Monitor jobs using Amazon CloudWatch.

• Configure alerts for failures and SLA breaches.

• Maintain execution logs for troubleshooting.

Proposed AWS Services

Layer	AWS Service
Data Ingestion	AWS Glue, Lambda, EventBridge
Data Storage	Amazon S3
Data Processing	AWS Glue ETL
Metadata Catalog	AWS Glue Data Catalogue
Query Layer	Amazon Athena
Reporting	Amazon QuickSight
Monitoring	Amazon CloudWatch
Security	IAM, KMS, CloudTrail

Data Flow

1. Source systems send data to Amazon S3.

2. AWS Glue Crawlers discover and catalogue datasets.

3. AWS Glue ETL jobs transform and validate data.

4. Processed data is stored in S3 curated buckets.

5. Athena queries curated datasets.

6. QuickSight dashboards consume Athena datasets.

7. CloudWatch monitors pipeline execution and generates alerts.

Deliverables

• AWS Architecture Diagram

• Glue Jobs and Crawlers

• S3 Data Lake Structure

• Athena Tables and Views

• QuickSight Dashboards

• Monitoring and Alert Configuration

• Technical Documentation

Success Criteria

• Automated end-to-end pipeline execution.

• Accurate and validated reporting data.

• Scalable architecture supporting future business growth.

• Reduced manual effort and operational overhead.

Example 2: Healthcare Laboratory Reporting Pipeline

Objective: Monitor daily test volumes and revenue.

Source

• LIS (Laboratory Information System)
• Patient Visit Data

Pipeline Flow

1. Data is
   exported daily to S3.
2. AWS Glue ETL processes patient and revenue records.
3. Data stored in curated S3 folders.
4. Athena creates KPI views.
5. QuickSight dashboards display:
   • Revenue
   • Visit Count
   • Home Visit Revenue
   • Wellness Revenue
   • MTD/LMTD/YOY Metrics

Output

• Daily KPI Dashboard
• Branch Performance Report
• Doctor Referral Analysis