Ooyala

At Ooyala, delivering personalized video content at scale required processing massive amounts of real-time user data. Ooyala’s algorithms consumed approximately 12 million user engagement events per hour, updating behavior-based video-to-video similarity graphs for nearly 200 million users. These dynamic models adjusted user interest profiles based on real-time viewing activity. My role focused on developing internal tools to enhance cloud storage management, supporting Ooyala's mission to deliver personalized videos to millions of users across diverse platforms.

Fig. 1: Ooyala’s Playback feature on multiple platforms

Fig. 1: Ooyala’s Playback feature on multiple platforms

Fig. 2: Data Storage Architecture with AWS, EC2, S3

Fig. 2: Data Storage Architecture with AWS, EC2, S3

Leveraging Big Data

I developed a web application using Ruby, Sinatra, and MySQL to provide engineers with an intuitive interface for monitoring server instance data, leveraging cron jobs for periodic updates. To enhance performance, I implemented bloom filters to efficiently exclude irrelevant data during joins and optimized processes by utilizing inherent data characteristics, such as limited behavior data for new video content.

1. Web Application Development

  • Designed and implemented a user-friendly web interface for engineers to monitor server-based instance data.

  • Leveraged Ruby, Sinatra, and MySQL to build a responsive tool capable of retrieving and displaying server data via periodic cron jobs.

  • Enhanced operational efficiency by streamlining data monitoring processes.

2. AWS Integration

Utilized key AWS services to scale and optimize video delivery:

  • Amazon S3: Supported S3 bucket reuse (limited to 100 per account) and optimized cache-control headers for enhanced performance.

  • Amazon CloudFront: Enabled secure SSL delivery using custom certificates.

  • Amazon EC2 & VPC: Facilitated scalable compute environments for processing billions of log files.

  • AWS Direct Connect: Ensured cost-effective, high-speed data transport and storage.

3. Chef Configuration Management

  • Integrated Chef cookbooks into internal tools for storing and distributing sensitive configuration data to client systems.

  • Automated configuration management across diverse environments, enabling efficient customization of servers through Chef recipes.

  • Streamlined deployment processes for physical and virtual systems across multiple operating systems and geographic regions, reducing manual intervention and improving consistency.

4. OpenStack Private Cloud

  • Supported the integration of OpenStack to implement a cost-effective private cloud infrastructure for personalized video delivery.

  • Enabled scalable storage and processing capabilities to meet the demands of Ooyala’s extensive user base.

5. Performance Optimization

  • Enhanced data joins by utilizing bloom filters to eliminate irrelevant data early in the process.

  • Leveraged data-specific characteristics, such as prioritizing limited behavior data for new video content, to improve processing efficiency and scalability.

Impact and Significance

1. Enabling Scalable Personalization

The internal tools I developed empowered Ooyala to process massive data streams and dynamically adjust video recommendations in real-time. These enhancements supported seamless scalability, ensuring millions of users received personalized video recommendations across multiple devices.

2. Improving Operational Efficiency

By automating server configurations with Chef and optimizing AWS and OpenStack integrations, I significantly reduced manual processes, improving operational efficiency and consistency across platforms.

3. Supporting Data-Driven Innovation

My contributions facilitated data-driven decision-making through robust performance optimizations, ensuring Ooyala could deliver high-quality, personalized video experiences while minimizing resource overhead.

By optimizing large-scale systems and driving innovation, this internal tool has left a lasting impact on the efficiency and effectiveness of personalized media delivery, setting a benchmark for global video streaming platforms. This technical solution enabled the continuous delivery of personalized video content to billions of users worldwide.

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