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DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Find and prevent application performance risks A major challenge for DevOps and security teams is responding to outages or poor application performance fast enough to maintain normal service.
What is Azure Functions? Similar to AWS Lambda , Azure Functions is a serverless compute service by Microsoft that can run code in response to predetermined events or conditions (triggers), such as an order arriving on an IoT system, or a specific queue receiving a new message. The growth of Azure cloud computing.
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? A DevOps platform engineer is a more recent term.
But with many organizations relying on traditional, manual processes to ensure service reliability and code quality, software delivery speed suffers. As a result, organizations are investing in DevOps automation to meet the need for faster, more reliable innovation. Automation is a crucial aspect of achieving DevOps excellence.
Dynatrace Delivers Software Intelligence as Code. With this announcement, Dynatrace delivers software intelligence as code, including broad and deep observability, application security, and advanced AIOps (or AI for operations) capabilities. Dynatrace Delivers Most Complete Observability for Multicloud Serverless Architectures.
As organizations adopt microservices architecture with cloud-native technologies such as Microsoft Azure , many quickly notice an increase in operational complexity. To guide organizations through their cloud migrations, Microsoft developed the Azure Well-Architected Framework. What is the Azure Well-Architected Framework?
Hopefully, this blog will explain ‘why,’ and how Microsoft’s Azure Monitor is complementary to that of Dynatrace. Do I need more than Azure Monitor? Azure Monitor features. Application Insights – Collects performance metrics of the application code. Available as an agent installer). Hybrid and multi-cloud platform –.
Dynatrace’s OneAgent automatically captures PurePaths and analyzes transactions end-to-end across every tier of your application technology stack with no code changes, from the browser all the way down to the code and database level. Monitoring-as-code requirements at Dynatrace.
In addition to existing support for AWS Lambda , this support now covers Microsoft Azure Functions and Google Cloud Functions as well as managed Kubernetes environments, messaging queues, and cloud databases across all major cloud providers. This enables your DevOps teams to get a holistic overview of their multicloud serverless applications.
As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. As a result, teams can focus on writing code and building features rather than dealing with infrastructure nuances. “It makes them more productive.
The time and effort saved with testing and deployment are a game-changer for DevOps. These tools integrate tightly with code repositories (such as GitHub) and continuous integration and continuous delivery (CI/CD) pipeline tools (such as Jenkins). In production, containers are easy to replicate. Here are some examples.
The Dynatrace Software Intelligence Platform already comes with release analysis, version awareness , and Service Level Objective (SLO) support as part of the Dynatrace Cloud Automation solution , helping DevOps and SRE teams automate the delivery and operational decisions. GitOps: Cloud automation as code. Expand to more use cases.
Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes. DevOps teams often use a log monitoring solution to ingest application, service, and system logs so they can detect issues at any phase of the software delivery life cycle (SDLC).
Part 1 of this series starts will cover the key ingredients needed for successful DevOps use to deliver better software faster, followed by a short overview of GitHub Actions and example use cases related to deployment and release monitoring. Example #1 – Deploy application code to Kubernetes.
Digital workers are now demanding IT support to be more proactive,” is a quote from last year’s Gartner Survey Understandably, a higher number of log sources and exponentially more log lines would overwhelm any DevOps, SRE, or Software Developer working with traditional log monitoring solutions.
In recent years, function-as-a-service (FaaS) platforms such as Google Cloud Functions (GCF) have gained popularity as an easy way to run code in a highly available, fault-tolerant serverless environment. Although GCF adds needed flexibility to serverless application development, it can also pose observability challenges for DevOps teams.
Using a microservices approach, DevOps teams split services into functional APIs instead of shipping applications as one collective unit. One large team generally maintains the source code in a centralized repository visible to all engineers, who commit their code in a single large build. Focused on delivering business value.
Using a microservices approach, DevOps teams split services into functional APIs instead of shipping applications as one collective unit. One large team generally maintains the source code in a centralized repository visible to all engineers, who commit their code in a single large build. Focused on delivering business value.
Accordingly, these platforms provide a unified, consistent DevOps and IT experience. Five available hybrid cloud platforms from the top public cloud providers include the following: Azure Stack : Consumers can access different Azure cloud services from their own data center and build applications for Azure cloud.
Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Dynatrace news. Manually maintaining dependencies among components doesn’t scale.
The goal was to develop a custom solution that enables DevOps and engineering teams to analyze and improve pipeline performance issues and alert on health metrics across CI/CD platforms. Faced with these requirements, Omnilogy carefully evaluated the following two options for implementing a solution to the pipeline observability challenge.
Back in 2018, we taught those DevOps concepts and implemented unbreakable pipelines for cloud-native delivery projects. For easy access to all configuration files relevant for Dynatrace Cloud Automation, we start by setting an upstream git to our own GitHub, GitLab, Bitbucket, AzureDevOps, or any other git compliant version control system.
Software companies who have already been following and adopting DevOps and site reliability engineering (SRE) practices alongside their shared ancestry in agile concepts came out on top – especially if they adopted those practices across the whole organization and customer value stream.
Gone are the days for Christian manually looking at dashboards and metrics after a new build got deployed into a testing or acceptance environment: Integrating Keptn into your existing DevOps tools such as GitLab is just a matter of an API call. Automate Performance aka Performance as a Self-Service: Watch SRE-Driven Performance Engineering.
Dynatrace enables various teams, such as developers, threat hunters, business analysts, and DevOps, to effortlessly consume advanced log insights within a single platform. DevOps teams operating, maintaining, and troubleshooting Azure, AWS, GCP, or other cloud environments are provided with an app focused on their daily routines and tasks.
According to Forrester Research, the COVID-19 pandemic fueled investment in “hyperscaler public clouds”—Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure. This approach—known as DevSecOps —places greater emphasis on developing secure application code in the cloud. Data confirms Aggarwal’s conclusions.
Integration with CI/CD pipelines: Teams can integrate SRG into existing delivery pipelines including Jenkins, Github, GitLab, AWS, or Azure pipelines. Automated release validation: The platform supports automated release validation for security and quality gates to ensure that only high-quality code progresses through the delivery pipeline.
After moving to Microsoft Azure for many of its production-stage applications, Park ‘N Fly’s IT teams experienced blind spots. “We IT automation speeds code development. To do so, organizations often succumb to a “hamster wheel” of having to release code more quickly to innovate effectively.
A microservices approach enables DevOps teams to develop an application as a suite of small services. One large team generally maintains the source code in a centralized repository that’s visible to all engineers, who commit their code in a single build. But nothing is perfect — and microservices is no exception.
If you need help setting up high availability for your PostgreSQL clusters, check out our fully Managed PostgreSQL on AWS , PostgreSQL on Azure , and PostgreSQL Enterprise solutions to automate your database management in the cloud an on-premise. Embed This Image On Your Site (copy code below): Courtesy of: ScaleGrid.
While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Why: Data Makes It Different.
Its approach to serverless computing has transformed DevOps. With AIOps , practitioners can apply automation to IT operations processes to get to the heart of problems in their infrastructure, applications and code. DevOps/DevSecOps with AWS. Successful DevOps is as much about tactics as it is technology. Learn more here.
As a result, IT operations, DevOps , and SRE teams are all looking for greater observability into these increasingly diverse and complex computing environments. DevSecOps teams can tap observability to get more insights into the apps they develop, and automate testing and CI/CD processes so they can release better quality code faster.
Those tests get executed from two locations (Paris and London) hosted by different cloud vendors (Azure & AWS). Thanks to our Automation APIs and our open-source project Monaco (Monitoring as Code) the creation and updates of those synthetic tests are fully embedded into their GitOps automation.
VMware commercialized the idea of virtual machines, and cloud providers embraced the same concept with services like Amazon EC2, Google Compute, and Azure virtual machines. You’ll benefit from serverless computing when: Authenticating users (for example, Okta , Azure Active Directory ).
This guest blog is authored by Raphael Pionke , DevOps Engineer at T-Systems MMS. The error log of the JMeter load test contains a message about a missing element, which can be traced down to an exception in the application code with the help of the W3C Trace Context Id. Dynatrace news. The white box load testing project setup.
Each use case provides its own unique value and impact, and whoever sees value in the use cases can adopt it—whether they are a platform engineer, DevOps engineer, performance engineer, or a site reliability engineer (SRE). Configuration as code is easy to use, update, and understand.
All technologies and extensions provide or permit additional contexts, like user sessions and experience, interdependencies between components, or code-level information in addition to the three pillars of observability (traces, metrics, and logs). Easily tie data to the application topology for deep analysis and to fuel the Davis AI engine.
OpenTelemetry works by providing developers with APIs, SDKs, and tools to instrument their code and collect telemetry data such as logs, metrics, and traces. Using Dynatrace OneAgent adds automatic data collection and enables user behavior analytics and application security use cases, as well as code-level analytics and profiling.
And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks? Using an interactive no/low code editor, you can create workflows or configure them as code.
Cloud application security practices enable organizations to follow secure coding practices, monitor and log activities for detection and response, comply with regulations, and develop incident response plans. Rapid development and iteration Modern cloud apps are typically developed using modern methodologies such as Agile and DevOps.
Cloud Native DevOps with Kubernetes : . DevOps and Continuous delivery: R evolution in the process, the way people and organizations delivering software work . refers to cloud-based, containerized, distributed systems, made up of cooperating microservices, dynamically managed by automated infrastructure as code. . ?
In particular, achieving observability across all containers controlled by Kubernetes can be laborious for even the most experienced DevOps teams. DevOps and continuous delivery: A revolution in processes, and the way people and software delivery teams work. Examples include: Azure Kubernetes Service (AKS).
Cloud-native architecture is a structural approach to planning and implementing an environment for software development and deployment that uses resources and processes common with public clouds like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The principles of cloud-native architecture. What are cloud-native services?
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