Sneak Peek: Intelligent Thumbnails

Date:: May 4, 2017
Author:: Joel Korpi

An example of extracting topic metadata and isolating the best thumbnail image based on a neural network

Analytics Segmentation: Viewer Engagement and Ad Optimization

Date:: April 7, 2017
Author:: Ryan Coughlin

We continue to iterate on our powerful Analytics tool: Segmentation! This month, we rolled out several new metrics to help publishers measure and optimize their video and ads strategy:

Redefining “Batch” in our Analytics Pipeline

Date:: March 28, 2017
Author:: Sahil Shah

Much like the legendary Gordian Knot, sometimes when a problem becomes too thorny to solve, you just need to approach it from a different perspective. Especially in the fast-paced world of data, it’s tempting to devise complex systems when a simple one works just as well. A few months ago, the analytics team at JW Player started working on breaking apart the usage computation from our larger daily pipeline. This usage data needed to be more timely to help our customers avoid overage charges or unexpected loss of service. We called this new pipeline Usage-Mini because it would run more frequently on smaller batches of data as they arrive. As part of splitting apart the usage aggregation, we redesigned the pipeline, making one key decision that has led to huge improvements in performance, monitoring, and stability. This change was to reformulate our definition of batch from fixed time to fixed size. What’s the big deal about that? Keep reading to understand the impact of this simple choice.

Analytics Segmentation: Ad Impressions

Date:: January 25, 2017
Author:: Ryan Coughlin

Last August, our data exploration tool Segmentation launched and has helped Advanced Analytics users take control of their data and dig deeper into video performance trends.

This month, we rolled out an important new metric: Ad Impressions!

Users can now…

Agile Incident Response for Big Data Systems

Date:: December 14, 2016
Author:: Donato Borrello

How We Increased Uptime and Decreased Engineering Stress

Working in the big data world can be a little chaotic. For us that means a stable flow of 20k events per second can spike to 50k over the course of an hour. It takes only seconds to look through our logs and find a perfect example: chart

Things move fast and we need to be able to adapt and respond immediately when our systems are under stress. When a system goes “red” most people’s first instinct is to call the engineer who built it. Over time this can create silos of knowledge and an uneven load on the engineers. Another side-effect of this engineer-first thinking is that the priority is focused on fixing the problem, missing the importance of communication.

JW Recommendations – Powerful Content Discovery

Date:: October 20, 2016
Author:: Franklin Dement

It’s been a year since JW Recommendations launched and since then the product has been delivering millions of extra plays a day to publishers. This week’s release of Video Search Playlists and an enhanced web interface greatly improves our flexible & powerful content discovery solution.

Big Gains in 2016

Recommendations are a crucial part of many video publishers’ offerings and that has put a premium on simple and integrated solutions that can be delivered cost effectively at scale. JW Recommendations integrates deeply with each part of JW Player to provide a powerful, real-time and automated content discovery solution built on our vast network of viewer and content data streams. recs-pic-1

My Internship at JW Player

Date:: September 20, 2016
Author:: Dan Meng

This summer I was very lucky to join JW Player as an engineering intern on the Data Team. It has been a fantastic experience. Aside from sailing on the Hudson River, enjoying Ping Pong games, and cycling on the Governors Island, I learned about their state-of-the-art data pipeline, followed Agile practices, and worked with an amazing group of people. I was part of the Discovery squad of the Data Team, worked on evaluating recommendation systems, and was responsible for developing an evaluation tool for our data-driven recommendations. With data-driven recommendations, we want to show our users relevant videos to increase video plays and user engagement. The question is how to evaluate if the recommended content is relevant, and which metrics to use as the measure. Generally there are three methods for evaluating recommendation systems: offline experiments, online trials, and user studies. In this project, we are using the user study approach, by directly asking the opinions of the viewers whether the recommended video is relevant or not.

Architecture of the evaluation tool danmeng1

Introducing our newest JW Player Analytics dashboard feature: “Segmentation”!

Date:: September 14, 2016
Author:: Ryan Coughlin

JW Player’s Analytics dashboard is focused on helping online video publishers track key, top-level video metrics in real-time. Our goal is to empower users to make quick decisions to drive audience growth and viewer engagement. In order to empower our publishers, we needed a way to give them more control over analysis. Instead of only pre-defined dashboard widgets, users can define their own queries against historical data and see instant results. Our easy-to-use interface gives non-technical users the answer to key questions about their audience and content.

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An Easier Way to Run Spark Jobs on AWS EMR

Date:: June 16, 2016
Author:: Kamil Sindi

At JW Player, we use Spark to explore new data features and run reports that help drive product decisions and improve algorithms. But doing data analysis at the terabyte level is time consuming, especially when having to manually set up AWS Elastic Mapreduce (EMR) clusters. Our code often depends on custom libraries or Spark settings that require bootstrapping. Moreover, iterating on changes is cumbersome and adds extra steps to our workflow.

JW Platform’s Data-Driven Recommendations & Feeds

Date:: May 13, 2016
Author:: Franklin Dement

Franklin Dement, Product Manager for JW Platform’s Data-Driven Recommendations, explains new features as well as best practices for implementation.

Why use Data-Driven Recommendations?

Launched in November 2015 to a select group of publishers, JW Platform Data-Driven Recommendations:

Increase content plays and monetization opportunities where ads are present.
Use existing content connections and viewer activity to simplify your publishing workflow and increase revenue.
Provide your viewers with automatically-curated video suggestions that increase audience engagement and are continually updating based on interaction data and content traits.

JW Player In-Player Overlay — Available in Nov 2015 Release