Use Case

Video Observability for Root Cause Analysis

The Problem
Real-time video observability that can solve Quality of Experience (QoE) issues while live broadcast events are still playing require the simultaneous monitoring of millions of data points. Video sessions flow across multiple systems including origins, CDNs, manifest services, and players provided by multiple vendors. Relational database approaches to perform this complex log analysis at productions scale run into costs constraints that prohibit comprehensive real-time operations for all but the highest value broadcast events.
The Solution
Quine streaming graph ingests logs and events from clients, CDNs, origins, etc. in real-time and materializes the data into a graph. The graph data model natively connects chunk QoE metrics with unlimited categorical classifications and calculated metrics to identify "alerts that matter to your audience" and instantly associate them to ASN, Geo, client type, asset names, encoding formats, CDN cache server, origin server, etc. This real-time comprehensive view of the inter-relationships between services allows rapid assessment of root causes while live video streams as still playing.

Key Value Delivered

Identify the QoE impacting issues that matter, in real-time and at scale
Graph data modeling eliminates the complexity of deeply nested joins
NOC technicians can easily pivot data to understand issue impacts and root causes
Automatic handling of out-of-order data arrival
Entity resolution between log and event sources
Integrates with existing Apache Kafka, AWS Kinesis, data lake, and API event sources.

What people are saying

thatDot Platform’s advanced Novelty Scoring for categorical data feature is the future of anomaly detection. Its speed powers our new real-time services while also significantly reducing false-positive findings for our customers.

Gery Szlobodnyik

CEO

The security industry is headed towards leveraging knowledge graphs of all the relevant evidence provided by threat groups, network indicators and endpoint artifacts to quickly identify and mitigate threats. The security companies that can most confidently extract relevant context from these graph and then immediately action on findings to mitigate threats will be the winners. This is exactly the problem Quine aims to address.

Evan Wright

Staff Data Scientist

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If you think Streaming Graph or Novelty might be for you, contact us to see them in action.

Video Observability for Root Cause Analysis

The Problem

The Solution

Key Value Delivered

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