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Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience.
Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. This is where large-scale system migrations come into play.
Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. However, your responsibilities might change or expand, and you need to work with unfamiliar data sets. Your trained eye can interpret them at a glance, a skill that sets you apart.
Cloud service providers (CSPs) share carbon footprint data with their customers, but the focus of these tools is on reporting and trending, effectively targeting sustainability officers and business leaders. We implemented a wasted energy metric in the app to enhance practitioner actionability.
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Over the last year, Dynatrace extended its AI-powered log monitoring capabilities by providing support for all log data sources. We added monitoring and analytics for log streams from Kubernetes and multicloud platforms like AWS, GCP, and Azure, as well as the most widely used open-source log data frameworks.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. This has resulted in visibility gaps, siloed data, and negative effects on cross-team collaboration. At the same time, the number of individual observability and security tools has grown.
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? This allows us to focus on data analysis and problem-solving rather than managing complex systemchanges.
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Testing Strategies: A Summary Two key factors determined our testing strategies: Functional vs. non-functional requirements Idempotency If we were testing functional requirements like data accuracy, and if the request was idempotent , we relied on Replay Testing. In such cases, we were not testing for response data but overall behavior.
Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Youll also learn strategies for maintaining data safety and managing node failures so your RabbitMQ setup is always up to the task. This setup prioritizes data safety, with most replicas online at any given time.
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That is, relying on metrics, logs, and traces to understand what software is doing and where it’s running into snags. OpenTelemetry, the open source observability tool, has emerged as an industry-standard solution for instrumenting application telemetry data to make it observable.
This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case.
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In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices.
Welcome back to our power dashboarding blog series , data enthusiasts! You can either continue with the custom infrastructure metrics dashboard you created in Part I or use the dashboard we prepared here (Dynatrace login required). exploring your data when you know your desired outcome but are unfamiliar with the available data.
Every image you hover over isnt just a visual placeholder; its a critical data point that fuels our sophisticated personalization engine. This nuanced integration of data and technology empowers us to offer bespoke content recommendations. This queue ensures we are consistently capturing raw events from our global userbase.
How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.
To do this, we devised a novel way to simulate the projected traffic weeks ahead of launch by building upon the traffic migration framework described here. New content or national events may drive brief spikes, but, by and large, traffic is usually smoothly increasing or decreasing.
Dynatrace Managed is intrinsically highly available as it stores three copies of all events, user sessions, and metrics across its cluster nodes. Near-zero RPO and RTO—monitoring continues seamlessly and without data loss in failover scenarios. Minimized cross-data center network traffic. Dynatrace news.
This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.
Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.
The data locked in your log files can be a goldmine for your application developers, operations teams, and your enterprise as a whole. However, it can be complicated , expensive , or even impossible to set up robust observability that makes use of this data. Log format inconsistency makes it a challenge to access critical data.
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Making applications observable—relying on metrics, logs, and traces to understand what software is doing and how it’s performing—has become increasingly important as workloads are shifting to multicloud environments. We also introduced our demo app and explained how to define the metrics and traces it uses.
Redis , short for Remote Dictionary Server, is a BSD-licensed, open-source in-memory key-value data structure store written in C language by Salvatore Sanfillipo and was first released on May 10, 2009. Instead, Redis stores data in data structures which makes it very flexible to use. Data Structures in Redis.
The massive volumes of log data associated with a breach have made cybersecurity forensics a complicated, costly problem to solve. As organizations adopt more cloud-native technologies, observability data—telemetry from applications and infrastructure, including logs, metrics, and traces—and security data are converging.
In my last blog , I’ve provided an example of this happening, whereby the traffic spiked and quadrupled the usual incoming traffic. These are all interesting metrics from marketing point of view, and also highly interesting to you as they allow you to engage with the teams that are driving the traffic against your IT-system.
Over the last two month s, w e’ve monito red key sites and applications across industries that have been receiving surges in traffic , including government, health insurance, retail, banking, and media. Readers who share our privacy concerns, please note, all the data we monitor is publicly available. . Monitoring with ?the
The F5 BIG-IP Local Traffic Manager (LTM) is an application delivery controller (ADC) that ensures the availability, security, and optimal performance of network traffic flows. Detect and respond to security threats like DDoS attacks or web application attacks by monitoring application traffic and logs.
Large enterprise environments are often distributed across multiple data centers around the world. Unnecessary traffic between such data centers can result in wasted resources, unpredictable downtimes, and lost business. optimizing traffic routing. preventing unrelated traffic between data centers and regions.
When the SLO status converges to an optimal value of 100%, and there’s substantial traffic (calls/min), BurnRate becomes more relevant for anomaly detection. SLOs must be evaluated at 100%, even when there is currently no traffic. Data Explorer “test your Metric Expression” for info result coming from the above metric.
Dynatrace is fully committed to the OpenTelemetry community and to the seamless integration of OpenTelemetry data , including ingestion of custom metrics , into the Dynatrace open analytics platform. Announcing seamless integration of OpenTracing data into Dynatrace PurePath 4.
This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. Containers can be replicated or deleted on the fly to meet varying end-user traffic. Application teams and Kubernetes/Swarm platform operators alike depend on detailed monitoring data.
Dynatrace automatically detects and connects monitored services and web applications, considers synthetic data in AI-powered answers by Davis, and even lets you drill down from a synthetic transaction to code-level if necessary. Easily integrate Amazon CloudWatch Synthetics data into Dynatrace. This blog post explains how to do this.
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This becomes even more challenging when the application receives heavy traffic, because a single microservice might become overwhelmed if it receives too many requests too quickly. It controls the delivery of service requests to other services, performs load balancing, encrypts data, and discovers other services.
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Automating quality gates is ideal, as it minimizes manually checking and validating key metrics throughout the SDLC. By actively monitoring metrics such as error rate, success rate, and CPU load, quality gates instill confidence in teams during software releases. Several tools can be used to collect metrics in load/performance testing.
VPC Flow Logs is a feature that gives you the capability to capture more robust IP trafficdata that traverses your VPCs. Flow log data can be published to Amazon CloudWatch Logs or Amazon S3 after which you can retrieve and view its data in the Dynatrace Intelligent Observability Platform. Log Metrics. Log Events.
In February 2021, Dynatrace announced full support for Google’s Core Web Vitals metrics , which will help site owners as they start optimizing Core Web Vitals performance for SEO. To do this effectively, you need a big data processing approach. How do you clearly communicate this data to your marketing and business stakeholders?
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