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
This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. To better guide the design and budgeting of future campaigns, we are developing an Incremental Return on Investment model.
Until recently, improvements in data center power efficiency compensated almost entirely for the increasing demand for computing resources. Platform engineers can set defaults for development teams, such as the number of replicas a service should have or whether it scales automatically. However, this trend is now reversing.
Why manual audits and custom scripts fall short for Kubernetes security posture management In the dynamic and complex world of Kubernetes, relying on manual audits, custom scripts, and general-purpose security tools is no longer enough to achieve efficient security posture management. Here’s why: Misconfigurations are pervasive.
Behind every high-performing application whether its a search engine, an e-commerce platform, or a real-time messaging service lies a well-thought-out system design. Have you ever wondered how large-scale systems handle millions of requests seamlessly while ensuring speed, reliability, and scalability?
In fact, observability is essential for shaping how we design smarter, more resilient systems for the future. To get a better idea of OpenTelemetry trends in 2025 and how to get the most out of it in your observability strategy, some of our Dynatrace open-source engineers and advocates picked out the innovations they find most interesting.
Dynatrace Live Debugger makes troubleshooting efficient, seamless, and non-disruptive. At Dynatrace, we understand your challenges when dealing with external packageswhether you’re hustling with reverse engineering, automatically fetching open source code, or playing the guessing game.
In response to this shift, platform engineering is growing in popularity. Many consider it an effective solution for improving efficiency and overall satisfaction for developers across a variety of organizations and industries. The practice of platform engineering has evolved alongside the increasing complexity of cloud environments.
Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. Automation, automation, automation.
The Davis AI engine automatically and continuously delivers actionable insights based on an environment’s current state. By leveraging the combined strengths of Dynatrace and Microsoft Sentinel, enterprises can achieve a comprehensive security posture for enhanced operational efficiency.
AV1 is one of the most efficient codecs available today. We would like to extend our thanks to the following teams for their crucial roles in thislaunch: The various Client and Partner Engineering teams at Netflix that manage the Netflix experience across different device platforms.
From developers leveraging platform engineering tools to optimize application performance, to Site Reliability Engineers (SREs) ensuring resilience, and executives gaining critical business insights, observability increases the velocity of innovation across every level of an organization.
In this blog post, we will see how Dynatrace harnesses the power of observability and analytics to tailor a new experience to easily extend to the left, allowing developers to solve issues faster, build more efficient software, and ultimately improve developer experience!
As cloud-native, distributed architectures proliferate, the need for DevOps technologies and DevOps platform engineers has increased as well. DevOps engineer tools can help ease the pressure as environment complexity grows. ” What does a DevOps platform engineer do? .” What are DevOps engineer tools and platforms.
Stranger Things imagery showcasing the inspiration for the Hawkins Design System by Hawkins team member Joshua Godi ; with art contributions by Wiki Chaves Hawkins may be the name of a fictional town in Indiana, most widely known as the backdrop for one of Netflix’s most popular TV series “Stranger Things,” but the name is so much more.
Planned effort Site Reliability Engineering (SRE) effort and time allocation planning typically fall into two domains: Operations Management (50%) Operations Management includes on-call responsibilities, post-mortem assessments, addressing other interruptions, and buffer time. Streamlining the CI/CD process to ensure optimal efficiency.
This standardization enhances adoption within the personalization stack, simplifies the system, and improves understanding and debuggability for engineers. They must also provide enough information for partner engineers to identify the problem with the underlying service in cases of system-level issues.
Stream processing enables software engineers to model their applications’ business logic as high-level representations in a directed acyclic graph without explicitly defining a physical execution plan. We designed experimental scenarios inspired by chaos engineering. Recovery time of the latency p90.
But because of the complexity involved in executing and analyzing test results of dynamic systems, performance engineering is difficult to scale — especially with lean staff or resources. Grabner also introduced four ways organizations can turbocharge their performance engineering with automation.
We’re therefore excited to announce that Dynatrace has received the Amazon RDS Service Ready designation. Achieving this designation differentiates Dynatrace as an AWS Advanced Technology Partner with a product that is integrated with Amazon RDS and is generally available and fully supported. Next steps.
They offer a comprehensive end-to-end solution to these challenges, providing functionalities designed to enhance compliance and resilience in IT environments. This enables DevOps platform engineers to make the right release decisions for new versions and empowers SREs to apply Service-Level Objectives (SLOs) for their critical services.
How can we achieve a similar functionality when designing our gRPC APIs? The solution we use within the Netflix Studio Engineering is protobuf FieldMask. This (alongside some other techniques like ZigZag encoding for signed types) makes protobuf messages space-efficient. Our protobuf message definition (.proto
A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.
Site reliability engineering (SRE) has become increasingly important to organizations looking to keep up with the rapid pace of digital transformation. Effective site reliability engineering requires enterprise-wide transformation Without a unified understanding of SRE practices, organizational silos can quickly form between departments.
Ready-made dashboards and notebooks address this concern by offering pre-configured data visualizations and filters designed for common scenarios like troubleshooting and optimization. These ready-made dashboards offer your platform engineers, who oversee Kubernetes environments, immediate and comprehensive data visibility.
Details of the root cause The developer deems it appropriate to either exclude or designate this error as acceptable during the patch release to prevent being overwhelmed with false positive alerts. Contact Sales The post Efficient SLO event integration powers successful AIOps appeared first on Dynatrace news.
To create a CPU core that can execute a large number of instructions in parallel, it is necessary to improve both the architecturewhich includes the overall CPU design and the instruction set architecture (ISA) designand the microarchitecture, which refers to the hardware design that optimizes instruction execution.
By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. Our audits would detect this and alert the on-call data engineer (DE).
Business events: Delivering the best data It’s been two years since we introduced business events , a special class of events designed to support even the most demanding business use cases. Simplified and enhanced analytics efficiency. Our Business Analytics solution is a prominent beneficiary of this commitment.
By Karen Casella, Director of Engineering, Access & Identity Management Have you ever experienced one of the following scenarios while looking for your next role? Most backend engineering teams follow a process very similar to what is shown below. If so, we invite you to begin the interview process.
Our engineering team and Creative Technologies sound expert joined forces to quickly solve the issue, but a larger conversation about higher quality audio continued. We expect these bitrates to evolve over time as we get more efficient with our encoding techniques. channel stream, as well as audible degradation of high frequencies.
Whether youre a developer, DevOps engineer, or QA professional, this image is designed to make your performance testing faster, easier, and more efficient. Today, Im excited to share a new Dockerfile Ive crafted that delivers a lightweight Apache JMeter image without compromising on functionality.
Key insights from this shiftinclude: A Data-Centric Approach : Shifting focus from model-centric strategies, which heavily rely on feature engineering, to a data-centric one. At inference time, when multi-step decoding is needed, we can deploy KV caching to efficiently reuse past computations and maintain lowlatency.
When we talk about conversational AI, were referring to systems designed to have a conversation, orchestrate workflows, and make decisions in real time. By separating these concerns, structured automation ensures that AI-powered systems are reliable, efficient, and maintainable. What Does Structured Automation Look Like in Practice?
Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.
The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? How can we design systems that recognize these nuances and empower every title to shine and bring joy to ourmembers?
This guide will cover how to distribute workloads across multiple nodes, set up efficient clustering, and implement robust load-balancing techniques. The architecture of RabbitMQ is meticulously designed for complex message routing, enabling dynamic and flexible interactions between producers and consumers.
By leveraging Dynatrace observability on Red Hat OpenShift running on Linux, you can accelerate modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes. Dynatrace is designed to scale easily across the entire Kubernetes stack.
While conventional video codecs remain prevalent, NN-based video encoding tools are flourishing and closing the performance gap in terms of compression efficiency. We employed an adaptive network design that is applicable to the wide variety of resolutions we use for encoding. How do we apply neural networks at scale efficiently?
Site reliability engineering (SRE) continues to gain popularity as organizations embrace hybrid cloud strategies and IT automation at scale. By applying software engineering principles to operations and infrastructure practices, SRE enables organizations to streamline and automate IT processes. Dynatrace news.
Additionally, the tight coupling with multiple native database APIs — APIs that continually evolve and sometimes introduce backward-incompatible changes — resulted in org-wide engineering efforts to maintain and optimize our microservice’s data access.
It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers. Greenplum uses an MPP database design that can help you develop a scalable, high performance deployment. Greenplum Architectural Design.
Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.
In this article, we discuss the concepts of dependability and fault tolerance in detail and explain how the Ably platform is designed with fault tolerant approaches to uphold its dependability guarantees. Fault tolerant design approaches address these shortfalls to provide continuity both to business and to the user experience.
Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can
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