Datazoom Improves End-to-End Observability
Understanding the interplay of services and being able to identify how the function of each component impacts the overall performance of the technology stack can improve both resource and financial efficiency and effectiveness. Linking data collected from across the technology stack can help diagnose the root cause of errors faster to reduce overall impact on the end-user Quality of Experience (QoE), as well as identify new patterns that can lead to sustained engagement. Being able to capture, standardize, and correlate these datasets from scratch requires serious analytical firepower, and is an insurmountable barrier to all but the most well-staffed companies.
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Datazoom's platform has been proven to improve a company's ability to develop end-to-end observability which can lead to improved operational efficiency by reducing MTTD and MTTR
Reveal Correlated Insights From Your Data
What is observability? Originally coined in the network management space, it was about understanding the internal operation of a system by analyzing external data output. But thanks to companies like New Relic, Splunk and Datadog, it also applies to software tools and practices for aggregating, correlating, and analyzing a steady stream of performance data from a distributed application.
Applying distributed tracing to observe the behavior of a video streaming workflow offers a comprehensive and insightful approach to understanding the intricacies of this complex process. In the realm of video streaming, where publishers manage multifaceted applications and workflows, distributed tracing becomes an invaluable tool. By instrumenting the various components of the streaming infrastructure, such as content delivery networks, encoding servers, and content databases, publishers can gain a holistic view of how data and requests flow through the system. When a viewer initiates a streaming session, distributed tracing can track the journey of the video content from its source to the user’s screen, pinpointing potential bottlenecks or latency issues at each step. This enables publishers to detect performance disparities, identify the root causes of any disruptions, and optimize the workflow accordingly. With the ability to visualize the end-to-end flow of data, pinpoint anomalies, and diagnose issues in real-time, distributed tracing empowers publishers to provide a seamless and high-quality streaming experience to their audience, making it an indispensable asset in the realm of video streaming operations.
The point of observability is to more effectively monitor, troubleshoot, and debug the application. But it can reveal much more than just issues and which system to address in solving them. It can reveal insightful information about how a system is being used, such as user requests traversing through different systems and accessing different resources. When reviewed in aggregate, this kind of observability can also impact company revenue by identifying new opportunities for optimizing aspects of the workflow that will positively impact the viewer experience.
Issue Diagnosis and Resolution
This is where traditional observability excels: identifying the components within a complex, distributed system that are inefficient, ineffective, malfunctioning (outside of tolerances), or even broken. But more than that, observability can show the predilection of a component to malfunction allowing operations engineers to address it even before it becomes a problem.
Scale and Resource Planning
One of the biggest issues with streaming is understanding the capacity that will be needed and spinning up resources to handle it. Observability that looks across all the systems can show network engineers where users are likely to need additional resources. Based on traffic patterns over time, heuristics can lead to improved predictability for scale and resiliency.
Content Engagement and Predicability
Unlike the observability of network planning, the new world of observability isn’t just about system state and the potential for error. It’s about looking across all of the data points collected from a myriad of workflow components, including the player. A variety of data points collected about viewer behavior, when combined with other system data, can show how errors impact content engagement and help predict how viewers respond to issues.
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There are many ways that Datazoom can improve business operation, effectiveness, and efficiency by improving operational and business observability.
How Datazoom Enables End-to-End Observability: Distributed Tracing
Distributed Media Tracing, a standard being developed by CTA-WAVE and SVTA, provides a way to link session data through the entire streaming workflow, enabling operations engineers to better understand root cause analysis for individual streams. Through the process detailed above, data collection, normalization, and analysis all happen through a DaaS platform, such as Datazoom, which can then be visualized through a variety of analytics tools.
