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Reliability engineering teams at Uber build the tools, libraries, and infrastructure that enable engineers to operate our thousands of microservices reliably at scale. At its essence, reliability engineering boils down to actively preventing outages that affect the mean time between … The post Using Machine Learning to Ensure the Capacity Safety of Individual Microservices appeared first on Uber Engineering Blog.
Cloud is one of the key drivers for innovation. Innovative companies experiment with data to come up with something useful. It usually starts with the opening of a firehose that continuously broadcasts tons of events before they start mining it to create music out of simply noise. Today, companies from all around the world are witnessing an explosion of event generation coming from everywhere, including their own internal systems.
MezzFS?—?Mounting object storage in Netflix’s media processing platform By Barak Alon (on behalf of Netflix’s Media Cloud Engineering team) MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. It’s used extensively in our media processing platform, which includes services like Archer and runs features like video encoding and title image generation on tens of thousands of Amazon EC2 instances.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
First introduced by Uber in November 2018, Peloton , a unified resource scheduler, manages resources across distinct workloads, combining separate compute clusters. Peloton is designed for web-scale companies like Uber with millions of containers and tens of thousands of nodes. … The post Open Sourcing Peloton, Uber’s Unified Resource Scheduler appeared first on Uber Engineering Blog.
It’s 7 o’clock. I tug my blanket tightly over my face, hoping to whisk the morning away. The sound of my favorite playlist soothes me to a gentle rise. I smile. The alarm worked. Months of hard work collaborating with Google™, including many days at each others’ offices, came to life in that moment. Just a week earlier, we announced the release of the Pandora integration with the Clock app from Google.
Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. This freedom allows teams and individuals to move fast to deliver on innovation and feel responsible for quality and robustness of their delivery.
Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. This freedom allows teams and individuals to move fast to deliver on innovation and feel responsible for quality and robustness of their delivery.
Today is my first day back in the office after attending SQLBits in Manchester last week. SQLBits is the UK's largest Microsoft Data Platform conference. What makes this event special is that it is organised for the community, by the community and is not for profit - All the proceeds go in to funding the event and in particular, the awesome Friday night party.
Find out how data scientists can contribute to solving real-life business problems, as well as how Teradata Vantage can help data scientists enable a better ROI for their company.
Design Principles for Mathematical Engineering in Experimentation Platform at Netflix Jeffrey Wong, Senior Modeling Architect, Experimentation Platform Colin McFarland, Director, Experimentation Platform At Netflix, we have data scientists coming from many backgrounds such as neuroscience, statistics and biostatistics, economics, and physics; each of these backgrounds has a meaningful contribution to how experiments should be analyzed.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Summary Customer analytics is a problem domain that has given rise to its own industry. In order to gain a full understanding of what your users are doing and how best to serve them you may need to send data to multiple services, each with their own tracking code or APIs. To simplify this process and allow your non-engineering employees to gain access to the information they need to do their jobs Segment provides a single interface for capturing data and routing it to all of the places that you
FlameScope sub-second heatmap visualization. Less than a year ago, FlameScope was released as a proof of concept for a new profile visualization. Since then, it helped us, and many other users, to easily find and fix performance issues, and allowed us to see patterns that we had never noticed before in our profiles. As a tool, FlameScope was limited.
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