Case Study: Migrating Data Integration to AWS Cloud Infrastructure at Fintech

A few years ago, I led the migration of a critical data integration system from an on-premise environment to AWS cloud infrastructure for a fintech company. This system handled sensitive daily debit card transaction data from financial institution (FI) clients and transformed it into reward points for end-users. My role encompassed designing and implementing a cloud-native solution to manage data ingestion, transformation, and storage, ensuring data security and compliance throughout the process.

Challenges

1. Data Sensitivity and Security: The transaction data was highly sensitive and required encryption both at rest and in transit. It needed dual-key encryption for secure decryption and re-encryption.

2. Scalability: The on-premise system struggled to handle increasing volumes of data. The new solution had to scale seamlessly with growing data ingestion rates.

3. Real-Time Processing: The transformation process needed to be fast enough to support near real-time availability of reward points.

4. Minimal Downtime: Migrating a live system required minimal disruption to the ongoing data processing pipeline.

   
   

Solution Architecture

The cloud solution consisted of three main components:

1. Data Ingestion

2. Data Transformation

3. Data Storage and Access

   
   

1. Data Ingestion

FI clients provided daily transaction data in encrypted files via SFTP. These files were ingested into an AWS S3 bucket.

• S3 Bucket Configuration:

  • Versioning and encryption were enabled for the bucket.

  • Server-side encryption with AWS Key Management Service (KMS) was used, requiring dual keys for decryption.

Code Snippet: Ingestion Function Using Boto3

import boto3
import os
from cryptography.fernet import Fernet

s3 = boto3.client('s3')
kms = boto3.client('kms')


def upload_to_s3(file_path, bucket_name, s3_key):
    # Read and encrypt the file
    with open(file_path, 'rb') as file:
        data = file.read()
        encrypted_data = encrypt_data(data)

    # Upload encrypted file to S3
    s3.put_object(Bucket=bucket_name, Key=s3_key, Body=encrypted_data)

def encrypt_data(data):
    # Encrypt data using KMS
    response = kms.encrypt(
        KeyId='alias/my-key-alias',
        Plaintext=data
    )
    return response['CiphertextBlob']

2. Data Transformation

Once the encrypted files were uploaded to S3, an AWS Lambda function was triggered to process the files. The function decrypted the files, transformed the transaction data using Python Pandas, and prepared it for loading into a database.

Steps:

1. Retrieve and decrypt the file.

2. Load the data into a Pandas DataFrame.

3. Normalize the transaction data.

4. Convert transactions to reward points based on predefined rules.

   
   

Code Snippet: Lambda Transformation Function

import boto3
import pandas as pd
from cryptography.fernet import Fernet

s3 = boto3.client('s3')
kms = boto3.client('kms')

def lambda_handler(event, context):
    bucket_name = event['Records'][0]['s3']['bucket']['name']
    s3_key = event['Records'][0]['s3']['object']['key']
    
    # Download and decrypt the file
    response = s3.get_object(Bucket=bucket_name, Key=s3_key)
    encrypted_data = response['Body'].read()
    data = decrypt_data(encrypted_data)
    

    # Load data into DataFrame
    df = pd.read_csv(data)
    

    # Transform data
    df['reward_points'] = df['amount'] * 0.01  # Example conversion rule

    # Save transformed data
    transformed_file = '/tmp/transformed_data.csv'
    df.to_csv(transformed_file, index=False)
  
    # Upload transformed data to S3
    upload_to_s3(transformed_file, 'transformed-data-bucket', 'transformed/'+s3_key)

def decrypt_data(encrypted_data):
    # Decrypt data using KMS
    response = kms.decrypt(
        CiphertextBlob=encrypted_data
    )
    return response['Plaintext']

3. Data Storage and Access

The transformed data was stored in an AWS RDS (Relational Database Service) instance using PostgreSQL. This enabled fast querying and reporting on the reward points data.

• Database Schema:

  • A transactions table stored normalized transaction data.

  • A reward_points table stored computed reward points.

Code Snippet: Loading Data into RDS

import psycopg2
import pandas as pd

def load_data_to_rds(transformed_file):
    # Connect to RDS
    conn = psycopg2.connect(
        host='my-rds-endpoint',
        database='fintech_db',
        user='admin',
        password='password'
    )
    cursor = conn.cursor()    

    # Load data into DataFrame
    df = pd.read_csv(transformed_file)
    

    # Insert data into RDS
    for index, row in df.iterrows():
        cursor.execute(
            """
            INSERT INTO reward_points (transaction_id, amount, reward_points)
            VALUES (%s, %s, %s)
            """,
            (row['transaction_id'], row['amount'], row['reward_points'])
        )
   
    conn.commit()
    cursor.close()
    conn.close()

Results

1. Improved Scalability: The new cloud-native solution could handle a 5x increase in daily transaction volume without any performance degradation.

2. Enhanced Security: The use of dual-key encryption and AWS KMS ensured compliance with industry standards for data security.

3. Reduced Latency: The transformation and loading process time was reduced by 40%, enabling near real-time availability of reward points.

4. Operational Efficiency: Automated data ingestion, transformation, and loading reduced manual intervention and operational overhead.

   
   

Key Learnings

1. Cloud-Native Design: Leveraging AWS services like S3, Lambda, and RDS significantly simplified the architecture and improved scalability.

2. Security Best Practices: Ensuring encryption both at rest and in transit is critical when dealing with sensitive financial data.

3. Automation: Automating the entire pipeline from ingestion to transformation and loading improved reliability and efficiency.

Conclusion

This migration project demonstrated the benefits of adopting a cloud-first approach for data integration in a fintech environment. The scalable, secure, and efficient solution enabled the company to better serve its FI clients and end-users while reducing operational complexity. This case study highlights my expertise in cloud migration, data engineering, and secure data processing in a highly regulated industry.