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How Netflix Improves its Visibility with Real-Time Data?
This is the second post in our Data Visualization Spotlight series where we showcase how different organizations are using data visualization and analytics to solve their day to day problems.
In mid-March 2013, Netflix reported a global streaming subscriber list of 33 million. It increased to 36.3 million (29.2 million in U.S.) in April 2013. It had 40.4 million subscribers (31.2 million in U.S.) in September 2013. By Q4 2013, it reported 33.1 million subscribers in U.S alone.
With its subscriber list growing by leaps and bounds, Netflix faces the daunting task of supporting millions of connected devices spread across 40+ countries. At such a big a scale, it is impossible to manually monitor all that data. Imagine having to detect a system fault in an environment that is not only large and complex but also highly distributed!
To thwart such operational nightmares from occurring, the Netflix team is working on a greenfield project that focuses on building the next generation tools for operational visibility that can proactively detect and communicate system faults and identify areas of improvement.
Fig: An excerpt from the current Netflix dashboard along with explanations of what all the data represents. Source: Techblog.netflix
The tools that are currently in Netflix’s arsenal include dashboards that display the status of their systems in near real time, and alerting mechanisms that notify them of major problems. While these tools are very helpful, the team feels there is a huge scope of improvement in them.
“With our next generation of insight tools, we have the opportunity to create new and transformative ways to effectively deliver insights and extend our existing insight capabilities. We plan to build a new set of tools and systems for operational visibility that provide the insight features and capabilities that we need.”
Fig: The mockup (with dummy data) for one of the views shows several components within the design: a top level navigation bar to switch between different views in the system, a breadcrumbs component highlighting the selected facets, a main view module (a map in this instance), a key metrics component, a timeline and an incident view, on the right side of the screen. The main view communicates data based on the selected facets. Source: Techblog.netflix
Fig: Another mockup (with dummy data) represents another view in the system and displays routes in their edge tier with request rates, error rates and other key metrics for each route updated in near real-time. Source: Techblog.netflix
As per the team, all views in the new system will be dynamic and will reflect the current operational state based on selected facets. A user can modify the facets and immediately see changes to the user interface.