Two-Pronged Data Architecture for CTV/OTT Ad Delivery: Open-Source vs. Google Cloud Platform (GCP)
This post builds upon the previous discussion of a data architecture for CTV/OTT ad delivery. Here, we explore two strategic approaches:
Strategy 1: Open-Source and Cloud-Agnostic
Focus: Optimizing for cost and flexibility.
Infrastructure: Primarily bare-metal servers for core operations. Utilize Google Cloud Platform (GCP) for handling peak loads.
Data Processing: Apache Airflow for orchestrating data pipelines. Real-time streaming pipelines written with the Python Airflow API.
Data Warehouse: Presto or Spark for querying historical and aggregated data.
Backups: Regularly back up data to an offsite location or cloud storage service (e.g., AWS S3, Azure Blob Storage) for disaster recovery.
Data Warehouse Calls: Applications and analysts can call the data warehouse using their chosen tools (e.g., SQL clients, BI tools) compatible with Presto or Spark.
Benefits:
Reduced Cost: Leverages cost-effective bare-metal servers for most operations.
Cloud Agnostic: Easier to switch cloud providers (AWS, Azure) in the future with minimal code changes.
Open-Source Focus: Familiar open-source tools readily available for wider talent pool recruitment.
Challenges:
Management Overhead: Requires managing and maintaining bare-metal infrastructure.
Scalability Limitations: Scaling bare-metal infrastructure on-demand can be slower than cloud solutions.
Strategy 2: Leveraging Google Cloud Platform (GCP)
Focus: Maximizing scalability and leveraging managed services offered by GCP.
Infrastructure: Primarily GCP services like Compute Engine for virtual machines and Kubernetes Engine for container orchestration.
Data Processing: Cloud Workflows for orchestrating data pipelines. Real-time streaming pipelines written with Apache Beam or Cloud Dataflow.
Data Warehouse: BigQuery for a fully managed data warehouse solution.
Backups: GCP offers automatic backups for its services, ensuring data redundancy.
Data Warehouse Calls: Applications and analysts can call BigQuery using the GCP console, SQL clients, or BI tools with BigQuery compatibility.
Benefits:
Scalability: Easy to scale resources up and down to handle fluctuating workloads.
Managed Services: Reduces operational overhead with Google managing the infrastructure.
GCP Integration: Seamless integration with other GCP services for a unified data platform.
Challenges:
Vendor Lock-In: Switching to another cloud provider might be more complex due to reliance on GCP services.
Potential Cost: May be more expensive compared to the open-source approach, especially for smaller datasets.
Data Volume and Revenue Estimation:
Data volume in a CTV/OTT ad delivery system depends heavily on the number of viewers and the frequency of ad impressions. As a reference, the linked blog post (https://www.vplayed.com/blog/how-does-netflix-make-money/) mentions millions of viewers for a new show release. Here's a rough estimate based on this information:
Viewers: Millions (example: 5 million)
Ad Impressions per Viewer: Varies depending on ad strategy (example: 3 ads per hour, 2 hours of viewing = 6 impressions)
Data per Impression: ~1 KB (estimated for basic event data)
Total Data Volume: 5 million viewers * 6 impressions/viewer * 1 KB/impression = 30 GB (This is a very rough estimate and can vary significantly)
Ad Revenue Estimation:
Ad revenue is even more difficult to estimate as it depends heavily on factors like:
Ad type: Cost-per-thousand impressions (CPM), cost-per-click (CPC), or cost-per-acquisition (CPA) models.
Target audience: Demographics and interests influence ad value.
Competition: Market dynamics affect ad pricing.
However, industry reports suggest CPM rates for CTV/OTT ads can range from $5 to $30. Using the above assumptions and a conservative $5 CPM:
Estimated Ad Revenue: 5 million viewers * 6 impressions/viewer * $0.005/impression = $150,000 (This is a highly simplified estimate and should not be considered a guaranteed value)
Additional Distributed Processing Frameworks:
Flink: A popular open-source stream processing framework similar to Apache Spark Streaming.
Kafka Streams: A stream processing library built on top of Apache Kafka, allowing real-time data processing within the streaming platform.
Storm: Another open-source real-time processing framework, though less widely used compared to Apache
Computational Requirements:
The computational requirements for this system will vary depending on the chosen strategy and the specific event scale (number of viewers, ad impressions). Here's a breakdown for each strategy:
Open-Source and Cloud-Agnostic:
Bare-Metal Servers: The computational power needed depends on the chosen hardware and the anticipated peak loads. You'll need to carefully size the servers to handle real-time ad serving, data processing with Airflow, and data warehousing with Presto/Spark. Monitoring tools will be crucial to identify potential bottlenecks and scale resources as needed.
GCP: GCP offers various virtual machine configurations (Compute Engine) that can be scaled up or down based on real-time needs. This provides more flexibility than bare-metal servers, but careful configuration is still required to optimize costs.
Factors Affecting Computational Requirements:
Real-time Ad Serving: This requires low latency and sufficient processing power to handle ad selection, delivery decisions, and user interaction logging.
Data Processing Pipelines: Airflow or Cloud Workflows orchestrate data pipelines, but the actual processing power depends on the complexity of the transformations being performed on the data.
Data Warehousing: Presto/Spark or BigQuery need sufficient resources to handle queries from analysts and applications. The size and complexity of the data warehouse also play a role.
Additional Considerations:
Cost Optimization: Both strategies require careful resource allocation to avoid overspending. Techniques like autoscaling (GCP) and right-sizing servers (bare-metal) can help optimize costs.
Monitoring and Alerting: Continuously monitor system performance metrics (CPU, memory, network) to identify potential bottlenecks and ensure smooth operation during peak loads.
By considering these computational requirements and tailoring the chosen strategy accordingly, you can design a system that scales effectively to handle the demands of your CTV/OTT ad delivery platform.