Case Study

Optimizing PBS Metadata Sync for Scalability and Efficiency

How I Engineered a High-Performance Metadata Sync Solution Using:

AWS Lambda, Node.js, SQS,DynamoDB, Redis, API Gateway, JWT Authentication, CloudWatch

🎯 Contractor Role: Senior Web Developer | Full-Stack Architect

Meansurable Results Through Clear Communication

📈 Impact: Faster data synchronization, cost savings, improved data consistency, and seamless API integration

The Project

PBS-Public Broadcasting Service (USA)

Objective: Automate and optimize metadata synchronization between two internal systems, ensuring no duplication and seamless consistency across millions of data points (assets, episodes, seasons, and shows).

Manual Processes

Challenge: The metadata sync relied on a manual export-import process, slowing down operations and increasing errors.

Scalability Issues

Challenge: Their previous approach couldn’t handle growing data volumes efficiently.

API Rate Limits

Challenge: Strict API quotas made real-time synchronization challenging.

Data Integrity Risks

Challenge: Inconsistencies between systems led to missing or duplicated catalog entries, affecting the user experience.

Overview

Before I joined, the platform was struggling with:

Slow Processing & Delays

Data synchronization took too long, preventing timely catalog updates.

API Rate Limits & Efficiency Issues

Processing millions of records while complying with API restrictions was a major bottleneck.

Error Handling Limitations

Failed items required manual intervention, making the workflow inefficient and costly.

Manual Data Handling

Metadata was manually exported by PBS staff as .csv files, requiring extensive human intervention.

A fully automated, scalable, and self-healing solution was necessary to improve efficiency, accuracy, and speed.

Solution Step 1

Automated Data Ingestion

Enabling seamless CSV uploads to trigger automated processing.

PBS staff could now upload .csv files to an AWS S3 bucket, triggering an AWS Lambda function.

The Lambda function performed validations, transformations, and formatting to ensure data quality.

Each data point was assigned to a dedicated SQS FIFO queue, categorized by data type (assets, episodes, seasons, shows) to ensure structured processing.

Solution Step 2

Scalable & Reliable Processing

Implementing a microservices architecture for efficient and parallel execution.

AWS Lambda functions were triggered for each SQS queue, processing data independently in a highly scalable manner.

DynamoDB ensured persistence, preventing data loss and enabling fast lookups.

Failed data entries were routed to a dedicated error queue, allowing for retry mechanisms and human intervention where necessary.

Solution Step 3

Secure Data Synchronization

Ensuring seamless, secure integration between systems.

OAuth & JWT Authentication ensured all API communications were secure.

AWS IAM Policies enforced strict role-based access control, preventing unauthorized access.

Encrypted Data Storage & Secure APIs guaranteed data integrity and compliance.

Solution Step 4

Performance Optimization

Maximizing speed, reliability, and efficiency.

Batch Processing replaced item-by-item handling, significantly improving efficiency.

Redis Caching reduced redundant processing and accelerated failed-item reprocessing.

Auto-scaling with SQS & Lambda dynamically adjusted resources based on demand, optimizing performance and cost.

The Results

Real Business Impact

Maximizing speed, reliability, and efficiency.

MetricBeforeAfter
Sync TimeSlow (Manual)Automated & Real-time
API Processing SpeedLimited by Rate QuotasOptimized via Queuing & Batching
Data LossFrequent Due to Manual ErrorsZero Loss with Automated Recovery
Operational CostHigh Manual EffortReduced via Automation & Scaling
Catalog ConsistencyProne to Duplicates & InconsistenciesSeamless, Accurate Sync

Client Feedback

Rafael is not just a developer; he’s a problem solver. His ability to deliver scalable, efficient, and high-quality code under tight deadlines is unmatched. He played a crucial role in automating our data systems, improving our workflow, and increasing efficiency. Highly recommend him for any startup looking for a hands-on engineer who understands business needs.

Rob Boros - Director of Client Delivery at Itero Group

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