This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
These innovations promise to streamline operations, boost efficiency, and offer deeper insights for enterprises using AWS services. This seamless integration accelerates cloud adoption, allowing enterprises to maximize the value of their AWS infrastructure and focus on innovation rather than managing observability configurations.
Furthermore, it was difficult to transfer innovations from one model to another, given that most are independently trained despite using common data sources. This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models.
Specifically, we will dive into the architecture that powers search capabilities for studio applications at Netflix. In addition, we were able to perform a handful of A/B tests to validate or negate our hypotheses for tuning the search experience. Media Search Platform (MSP) is the initiative to address these requirements.
Part 3: System Strategies and Architecture By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques This blog post is a continuation of Part 2 , where we cleared the ambiguity around title launch observability at Netflix. The response schema for the observability endpoint.
Without observability, the benefits of ARM are lost Over the last decade and a half, a new wave of computer architecture has overtaken the world. ARM architecture, based on a processor type optimized for cloud and hyperscale computing, has become the most prevalent on the planet, with billions of ARM devices currently in use.
In this blog, I want to give you two examples of internal innovation projects at Dynatrace which leverage this new API, to truly show you the power – and the fun-ness of this new metric ingest ??. So stay tuned. The idea was inspired by an innovation day project of our lab in Klagenfurt. Goal: sending metrics to Dynatrace.
You’re getting all the architectural benefits of Grail—the petabytes, the cardinality—with this implementation,” including the three pillars of observability: logs, metrics, and traces in context. Now, that same full-spectrum value is available at the massive scale of the Dynatrace Grail data lakehouse.
Want to learn more about how zero trust architecture can improve government user experiences? Read the blog User experience and DevSecOps make a positive impact Sandia National Laboratories has made remarkable contributions to national security and technology innovation. Tune in to the full episode to hear more from Gross on UX Ops.
Motivation With the rapid growth in Netflix member base and the increasing complexity of our systems, our architecture has evolved into an asynchronous one that enables both online and offline computation. Architecture As shown in the diagram above, the RENO service can be broken down into the following components.
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Following the innovation of microservices, serverless computing is the next step in the evolution of how applications are built in the cloud. Understand and optimize your architecture. So stay tuned!
As organizations plan, migrate, transform, and operate their workloads on AWS, it’s vital that they follow a consistent approach to evaluating both the on-premises architecture and the upcoming design for cloud-based architecture. Fully conceptualizing capacity requirements. Common findings. How to get started.
We combine our entertainment knowledge and our technical expertise to provide innovative technical solutions from the initial pitch of an idea to the moment our members hit play. Stay tuned as we expand on each stage of the content lifecycle over the coming months! link] Why Does Studio Engineering Exist?
This includes custom, built-in-house apps designed for a single, specific purpose, API-driven connections that bridge the gap between legacy systems and new services, and innovative apps that leverage open-source code to streamline processes. Environmental forces. IT environments exist in a state of almost constant change.
Also, these modern, cloud-native architectures produce an immense volume, velocity, and variety of data. Manual troubleshooting is painful, hurts the business, and slows down innovation. Explore your logs in multicloud environments and analyze them in the context of your architecture. So please stay tuned for updates. .
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Following the innovation of microservices, serverless computing is the next step in the evolution of how applications are built in the cloud. Understand and optimize your architecture. So stay tuned!
As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. By tuning workflows, you can increase their efficiency and effectiveness.
This architecture shift greatly reduced the processing latency and increased system resiliency. We rolled out encoding innovations such as per-title and per-shot optimizations, which provided significant quality-of-experience (QoE) improvement to Netflix members. This introductory blog focuses on an overview of our journey.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Log analysis can reveal potential bottlenecks and inefficient configurations so teams can fine-tune system performance. Accelerated innovation.
The rapidly evolving digital landscape is one important factor in the acceleration of such transformations – microservices architectures, service mesh, Kubernetes, Functions as a Service (FaaS), and other technologies now enable teams to innovate much faster. So please stay tuned for updates.
In this post, we dive deep into how Netflix’s KV abstraction works, the architectural principles guiding its design, the challenges we faced in scaling diverse use cases, and the technical innovations that have allowed us to achieve the performance and reliability required by Netflix’s global operations.
Then I will describe various types of security products that can be used for web application security including some innovations that Dynatrace has recently introduced. Especially as software development continually evolves using microservices, containerized architecture, distributed multicloud platforms, and open-source code.
By Xiaomei Liu , Rosanna Lee , Cyril Concolato Introduction Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Improved Architecture In order to address the limitations of our initial architecture, we proceeded to make some optimizations.
Serverless can accelerate innovation (and introduce blind spots). Serverless architectures help developers innovate more efficiently and effectively by removing the burden of managing underlying infrastructure. stay tuned?for A single pane of glass to view trace information along with AWS CloudWatch metrics.
This article was co-authored by Eduardo da Silva and Nick Tune based on our individual and collective experiences. FThis article describes a pattern we have observed and applied in multi-team-scope architecture modernization initiatives, the Architecture Modernization Enabling Team (AMET).
As organizations adopt microservices-based architecture , service-level objectives (SLOs) have become a vital way for teams to set specific, measurable targets that ensure users are receiving agreed-upon service levels. Properly set and defined SLOs should have error budgets that give developers space to innovate without impacting operations.
The increasing complexity of cloud service architectures requires a rock-solid understanding of the activity, health status, and security of cloud services. If so, stay tuned for more news about direct AWS Kinesis Data Firehose configuration in AWS console. Or explore the recently introduced support for AWS Lambda logs.
As VMAF evolves and is integrated with more encoding and streaming workflows within Netflix, we need scalable ways of fostering video quality innovations. This article explains how we designed microservices and workflows on top of the Cosmos platform to bolster such video quality innovations. via bug fixes).
In the rest of this blog, we will a) touch on the complexity of Netflix cloud landscape, b) discuss lineage design goals, ingestion architecture and the corresponding data model, c) share the challenges we faced and the learnings we picked up along the way, and d) close it out with “what’s next” on this journey.
In previous blog posts, we introduced the Key-Value Data Abstraction Layer and the Data Gateway Platform , both of which are integral to Netflix’s data architecture. We sometimes raise this limit for backfilling historical data, but it is tuned back down for regular write operations.
In our increasingly digital world, the speed of innovation is key to business success. Cloud-native technologies, including Kubernetes and OpenShift, help organizations accelerate innovation. Stay tuned – this is only the start. Dynatrace news. Automatic “crown jewel” protection and comprehensive CISO reporting.
Utilizing the automatic dependency mapping functionality of the Dynatrace OneAgent, DevSecOps and SecOps teams gain real-time visibility into application and infrastructure architectures. In the future you will see even more innovation from Dynatrace in this space so please stay tuned.
We need to be constantly adapting and innovating as a result of this change. In particular, we’ll define plans and offers, review the legacy architecture and some of its shortcomings, and dig into our new architecture and some of its advantages. Let’s take a deeper look at the architecture, protocols, and systems involved.
Amazon Redshift uses a variety of innovations to enable customers to rapidly analyze datasets ranging in size from several hundred gigabytes to a petabyte and more. s architecture and underlying platform are also optimized to deliver high performance for data warehousing workloads. re excited to be able to deliver this to them.
Self-Healing also means setting up your architecture, application, code, and infrastructure to deal with situations that go past operational requirements and inevitable failure of components. Your application should not grind to a halt because of the recommendation engine not working, so design your application with that goal in mind.
While integrating AI and machine learning into cloud-native architectures, there’s an increasing demand from users for AI to be open and collaborative. This marks the end of an era of chaos, paving the way for efficiency gains, quicker innovation, and standardized practices.
Each algorithm needed a process of evaluation and tuning to get great results in AVA Discovery View. Technical Architecture Discovery View Plugin Architecture Discovery View Plugin Architecture We built Discovery View as a pluggable feature that could quickly be extended to support more algorithms and other types of suggestions.
As software development environments adopt more cloud-native technologies, microservices, and container-based architecture, delivering software manually becomes increasingly impractical. This rapid feedback enables developers to stay focused on innovation instead of managing infrastructure. What are the benefits of continuous delivery?
What used to be only available in physical formats now often has digital equivalents and this digitalization is driving great new innovations. A key part of the Cloud Drive architecture is a Metadata Service that allows customers to quickly search and organize their digital collections within Cloud Drive.
Key Takeaways Multi-cloud strategies have become increasingly popular due to the need for flexibility, innovation, and the avoidance of vendor lock-in. Yet it reveals a migration trajectory favoring multi-cloud models as companies wake up to advantages such as heightened innovation potential tied with these varied service structures.
Shazam needed to handle an enormous increase in traffic for the duration of the Super Bowl and used DynamoDB as part of their architecture. This rapid adoption has allowed us to benefit from the scale economies inherent in our architecture. How are we able to do this?
There’s no doubt PostgreSQL provides a solid foundation for innovative development. The challenges can slow down the pace of innovation. The licensing can be expensive, and innovation can be limited or squelched. Percona makes PostgreSQL work out of the box for enterprise infrastructure, empowering you to innovate freely.
If you are an experienced DevOps Engineer who is constantly looking for ways to innovate and improve, we might just be the place for you! No more hassles of benchmarking and tuning algorithms or building and maintaining infrastructure for vector search. Bridgecrew is the cloud security platform for developers. Stateful JavaScript Apps.
This capability is essential when performance tuning since query events include discrete CPU and IO metrics as well as runtime parameters, which are key for troubleshooting query performance problems such as parameter sniffing. For some reason this thing will not go away! Legacy Profiler "Standard" trace events. An Updated XE Session.
For this, the authors use the Recursive Model Index (RMI) architecture as first introduced in ‘ The case for learned index structures ‘ In brief, RMI uses layers of simple linear models arranged in a hierarchy a bit like a mixture of experts.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content