Real-World Case Study: Data Engineering in the Finance Industry Using AWS

💼 Case Study: Modernizing Financial Data Infrastructure Using AWS

🏢 Client: A mid-sized financial services firm

📍 Industry: Investment Management

🎯 Objective: Build a scalable, secure, and real-time data pipeline to manage market data, customer transactions, and compliance reporting.

📌 Business Challenges

Siloed Data Sources: Data from trading platforms, CRM, market feeds, and internal databases were isolated.


Latency: Reports on transactions and trades took hours to generate.


Compliance Pressure: Regulations (e.g., MiFID II, Dodd-Frank) required real-time auditable data.


Scalability: Legacy systems couldn't handle spikes in market data or user activity.


🚀 Solution: AWS-Based Data Engineering Architecture

📷 High-Level Architecture Overview

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     [Data Sources]

  ┌─────────────┐   ┌──────────────┐   ┌────────────┐

  │ Market Data │   │ Trading App  │   │ CRM System │

  └─────┬───────┘   └────┬─────────┘   └─────┬──────┘

        │                │                   │

        ▼                ▼                   ▼

  ┌──────────────────────────────────────────────────┐

  │                AWS Kinesis Data Streams          │   <- Real-time ingestion

  └────┬────────────────────────────┬────────────────┘

       ▼                            ▼

[Lambda Functions]           [Kinesis Firehose → S3]        <- Transform + Store

       │                            │

       ▼                            ▼

[Redshift / Athena]         [Data Lake on S3]               <- Query & Analytics

       │                            │

       ▼                            ▼

[QuickSight / Power BI]      [Glue Catalog + Crawlers]     <- Reporting & Discovery

🛠️ Components Used

AWS Service Role in the Pipeline

Amazon Kinesis Ingest real-time trading and market data

AWS Lambda Perform light-weight transformations and filtering

Amazon S3 Central data lake for raw and processed data

Amazon Redshift Data warehousing and advanced analytics

AWS Glue Schema inference, ETL jobs, data cataloging

Amazon Athena Ad-hoc SQL queries on data stored in S3

Amazon QuickSight Visualization and dashboards for traders and compliance

CloudWatch + SNS Monitoring, alerting, and operational metrics


✅ Key Results

Metric Before AWS After AWS

Report Generation Time 3–4 hours < 5 minutes

Regulatory Reporting Accuracy Manual & error-prone Fully automated, 99.9% accuracy

Infrastructure Costs High (on-prem hardware) Reduced by 30% with pay-as-you-go

Data Latency ~15 minutes Sub-1 minute for most sources


🔒 Security and Compliance

Encryption at Rest & in Transit (S3, Redshift, KMS)


IAM Roles & Policies for fine-grained access control


Audit Trails with AWS CloudTrail


Data Masking and tokenization for PII data


SOC 2 & ISO 27001 alignment using AWS compliance services


🧠 Lessons Learned

Data Cataloging is crucial: AWS Glue helped bring structure to previously unstructured datasets.


Real-time isn’t always better: Not all business units needed real-time; cost saved by blending batch + stream.


Monitoring saves time: Integrating CloudWatch + SNS alerts reduced MTTR (mean time to repair) significantly.


📘 Summary

Using AWS, the firm built a modern data platform that transformed their data from a liability into a strategic asset — supporting real-time insights, regulatory compliance, and scalable analytics 

Learn AWS Data Engineering Training in Hyderabad

Read More

Building a Data Warehouse on AWS for Business Intelligence

How AWS Helps in Data Migration from On-Prem to Cloud

Implementing Machine Learning Pipelines on AWS

How AWS Powers Real-Time Data Analytics for E-commerce Platforms

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