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
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.
Dynatrace Managed is intrinsically highly available as it stores three copies of all events, user sessions, and metrics across its cluster nodes. Our Premium High Availability comes with the following features: Active-active deployment model for optimum hardware utilization. Minimized cross-data center network traffic.
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.
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. The certification results are now publicly available.
However, your responsibilities might change or expand, and you need to work with unfamiliar data sets. Activate Davis AI to analyze charts within seconds Davis AI can help you expand your dashboards and dive deeper into your availabledata to extract additional information.
With all the data collected and powered by our Davis AI-driven causation engine, Dynatrace automatically identifies slowdowns in your applications and services and points you to their root cause. Ensure high quality network traffic by tracking DNS requests out-of-the-box. Network services visibility (DNS, NTP, ActiveDirectory).
For retail organizations, peak traffic can be a mixed blessing. While high-volume traffic often boosts sales, it can also compromise uptimes. The nirvana state of system uptime at peak loads is known as “five-nines availability.” But is five nines availability attainable? Downtime per year. 90% (one nine).
Accurately Reflecting Production Behavior A key part of our solution is insights into production behavior, which necessitates our requests to the endpoint result in traffic to the real service functions that mimics the same pathways the traffic would take if it came from the usualcallers. Time Travel to validate ahead oftime.
Managing High Availability (HA) in your PostgreSQL hosting is very important to ensuring your database deployment clusters maintain exceptional uptime and strong operational performance so your data is always available to your application. It reduces downtime and supports business continuity.
In the final post of this series, we will review the last solution, Patroni by Zalando, and compare all three at the end so you can determine which high availability framework is best for your PostgreSQL hosting deployment. Managing High Availability in PostgreSQL – Part I: PostgreSQL Automatic Failover. Patroni for PostgreSQL.
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 decoupling is crucial in modern architectures where scalability and fault tolerance are paramount.
The subject line said: “Success Story: Major Issue in single AWS Frankfurt Availability Zone!” The problem started at 1:24PM PDT, with the services starting to become available again about 3 hours later. This number was so low because the automatic traffic redirect was so fast it kept the impact so low.
From the moment a Netflix film or series is pitched and long before it becomes available on Netflix, it goes through many phases. Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain.
Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical. As cloud complexity grows, it brings more volume, velocity, and variety of log data. When trying to address this challenge, your cloud architects will likely choose Amazon Data Firehose.
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.
In today’s data-driven world, businesses across various industry verticals increasingly leverage the Internet of Things (IoT) to drive efficiency and innovation. IoT is transforming how industries operate and make decisions, from agriculture to mining, energy utilities, and traffic management.
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.
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.
Dynatrace and the Dynatrace Intelligent Observability Platform have added support for the newly introduced Amazon VPC Flow Logs to Amazon Kinesis Data Firehose. This support enables customers to define specific endpoint delivery of real-time streaming data to platforms such as Dynatrace. What is VPC Flow Logs? Why Dynatrace?
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.
Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. No locks on tables are ever acquired, which prevent impacting write traffic on the source database. Writing events to any output.
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.
Having released this functionality in an Early Adopter Release with OneAgent version 1.173 and Dynatrace version 1.174 back in August 2019, we’re now happy to announce the General Availability of OneAgent full-stack monitoring for Linux on the IBM Z platform, sometimes informally referred to as Z/Linux. Release details.
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.
Central to this infrastructure is our use of multiple online distributed databases such as Apache Cassandra , a NoSQL database known for its high availability and scalability. Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns.
Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. No locks on tables are ever acquired, which prevent impacting write traffic on the source database. Writing events to any output.
We recently announced Dynatrace Live Debugger , which gives developers unprecedented access to real-time data and runtime behavior insights. How can you tell if an algorithm or data source changed or a new feature flag worked? Test data collection Accurate test data can mean life or death.
In this three-part blog series, we introduced a High Availability (HA) Framework for MySQL hosting in Part I, and discussed the details of MySQL semisynchronous replication in Part II. Now in Part III, we review how the framework handles some of the important MySQL failure scenarios and recovers to ensure high availability.
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.
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. Duration: 163.41
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.
Welcome back to our power dashboarding blog series , data enthusiasts! Query your data with natural language Davis CoPilot is an excellent virtual assistant that helps you create queries using natural language. exploring your data when you know your desired outcome but are unfamiliar with the availabledata.
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.
OpenTelemetry , the open source observability tool, has become the go-to standard for instrumenting custom applications to collect observability telemetry data. For this third and final part of our series, we saved the best for last: How you can enhance telemetry data even more and with less effort on your end with Dynatrace OneAgent.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Both serve distinct purposes, from managing message queues to ingesting large data volumes.
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. .
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.
by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.
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.
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. Business-critical applications typically rely on F5 for availability and success. It serves as a crucial component between applications and users.
Incremental Backups: Speeds up recovery and makes data management more efficient for active databases. Faster Write Operations: Enhancements to the write-ahead log (WAL) processing double PostgreSQLs ability to handle concurrent transactions, improving uptime and data accessibility. Start your free trial today!
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.
In this three-part blog series, we introduced a High Availability (HA) Framework for MySQL hosting in Part I, and discussed the details of MySQL semisynchronous replication in Part II. Now in Part III, we review how the framework handles some of the important MySQL failure scenarios and recovers to ensure high availability.
Even when the staging environment closely mirrors the production environment, achieving a complete replication of all potential scenarios, such as simulating extremely high traffic volumes to assess software performance, remains challenging. This can lead to a lack of insight into how the code will behave when exposed to heavy traffic.
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