By Michael Cucchi, CMO, Hydrolix
The challenge facing technology and delivery teams today isn’t just about handling large volumes of data. It’s about managing complexity at a scale that breaks conventional approaches. One area that peaks in scale is content delivery networks. Hundreds of thousands of servers and services are distributed around the globe. And trying to manage data across multiple CDNs simultaneously takes hours to days. It’s in environments like this that the limitations of traditional data warehouses become painfully apparent.
The Real Problem: Complexity at Scale
This isn’t a theoretical problem. During major live events like the Super Bowl, Black Friday, and major game release days, CDN traffic can surge to terabytes per minute. When Fox came to us about monitoring the Super Bowl, they thought getting real-time visibility was an impossibility. They pushed 1 terabyte per minute through our Hydrolix real-time data platform during the Super Bowl and charged us with finding a way to provide sub-second analytics over it. Any lag in processing or querying meant operators were flying blind during the exact moments when performance mattered most.
Traditional approaches to this problem typically involve sampling small amounts of data and making guesses. Or short retention windows and accepting delayed visibility. But in today’s environment—where security threats emerge in seconds, delivery issues impact millions of viewers simultaneously, and post-incident analysis requires complete data to find the root cause—these compromises are no longer acceptable.
Why Traditional Solutions Fall Short
The fundamental issue is that platforms like Snowflake, BigQuery, and Athena were designed with different assumptions. They rely heavily on caching to achieve acceptable query performance, which works well for repeated queries on relatively static datasets. But real-time observability at this scale demands something different: ad-hoc queries on constantly changing, high-cardinality data where every query asks a new question over massive and rapidly expanding data sets.
The economics of traditional platforms compound the problem. Most organizations face a brutal trade-off: either keep detailed data for a few days at enormous cost, or retain data longer but sacrifice the granularity needed for meaningful analysis. We’ve seen teams forced to choose between visibility and budget, often settling for throwing away the very details needed to diagnose complex issues.
Rethinking the Architecture
Solving this required fundamentally rethinking how data is ingested, indexed, compressed, and retrieved. The key insight was that SSD-like performance could be achieved for ad-hoc searches directly from object storage if we changed the underlying approach.
Hydrolix’s architecture was designed around several core principles:
Reliable Sub 5 Second Ingest Latency:
That includes complex data enrichment, transformation, indexing, and compression. When data arrives—whether on time or hours late—it becomes queryable almost immediately without compromising the integrity of existing data or requiring expensive reprocessing. We needed to build a linear and independently scalable ingestion pipeline and a proprietary partitioning approach to do it.
Sub-Second Query Performance:
Regardless of data age and without reliance on caching. Whether you’re querying data from five seconds ago or five years ago, performance remains consistent. This eliminates the common pattern where recent data queries fly, but historical analysis crawls. This is thanks to our ability to scale queries out seamlessly, along with a unique micro-indexing method that distributes processing across massive data sets.
Ultra Low Cost Storage and Next Generation Compression:
That enables limitless retention. Through proprietary high-density compression that consistently achieves 40x+ reduction, we can reduce 1TB of raw data to less than 55GB of object storage—without impacting performance. This transforms the economics entirely, allowing organizations to increase hot data retention from hours to years at the same cost.
Flexible Continuously Evolving Schemas:
Without reindexing, data formats change as vendors add new fields or customers deploy new monitoring capabilities. Supporting these changes without expensive reindexing or downtime is essential for operational continuity. Our solution provides self-optimization and aggregates and rebuilds indexes to ensure efficient data management and maintain performance across it.
Scaling for Reality
The architecture must handle both extremes: the sustained high volume of normal operations and the explosive bursts of live events or DDoS attacks. This requires a completely stateless, massively parallel architecture that takes full advantage of Kubernetes autoscaling in both directions.
During the Fox Super Bowl event, we sustained 1 TB per minute ingest rates while maintaining sub-5-second time-to-glass and 500 ms average query performance. Had that pace continued for a full day, it would have represented 1.5 petabytes—a scale we’ve proven capable of handling through similar events.
The Path Forward
As CDN traffic and digital exhaust in general continue to grow and security threats become more sophisticated, the complexity of managing real-time data will only increase. The organizations that thrive will be those that can maintain complete visibility into their infrastructure without compromising on retention, query performance, or budget.
The key is recognizing that this challenge requires purpose-built solutions rather than adapting platforms designed for different use cases. When your data arrives in terabytes per minute, when every query matters, and when historical context is essential for both operations and security, the traditional trade-offs are no longer acceptable.
The future of CDN observability belongs to platforms that can handle the full complexity of real-world data at scale because in today’s environment, anything less means operating blind at exactly the moments when visibility matters most.