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Leveraging AI can revolutionize query optimization and predictive maintenance, ensuring the database remains efficient, secure, and responsive. In this article, we will explore how AI can assist in these areas, providing codeexamples to tackle complex queries.
When we are working with a database, optimization is crucial and key in terms of application performance and efficiency. Likewise, in Azure Cosmos DB, optimization is crucial for maximizing efficiency, minimizing costs, and ensuring that your application scales effectively.
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!
But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise. With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.
They now use modern observability to monitor expanding cloud environments in order to operate more efficiently, innovate faster and more securely, and to deliver consistently better business results. Further, automation has become a core strategy as organizations migrate to and operate in the cloud.
The VS Code extension Dynatrace Apps is here to streamline your development process and simplify app building. Now you can easily query live data directly within VS Code using the Dynatrace Query Language (DQL). Re)Using queries within your app Once you are happy with your query’s result, you can easily use it in your app code.
Next, a pragmatic approach involves examining the backend, focusing on Service type entities prominently exposed to the frontend (for example, Apache Tomcat in a Linux environment). In today’s landscape, we lack a clear understanding of properly creating frontend SLOs (for example, RUM application type entities) based on key user actions.
Fast and efficient log analysis is critical in todays data-driven IT environments. For enterprises managing complex systems and vast datasets using traditional log management tools, finding specific log entries quickly and efficiently can feel like searching for a needle in a haystack. What are Dynatrace Segments?
This growth was spurred by mobile ecosystems with Android and iOS operating systems, where ARM has a unique advantage in energy efficiency while offering high performance. Energy efficiency and carbon footprint outshine x86 architectures The first clear benefit of ARM in the enterprise IT landscape is energy efficiency.
Broken Apache Struts 2: Technical Deep Dive into CVE-2024-53677The vulnerability allows attackers to manipulate file upload parameters, possibly leading to remote code execution. This allows attackers to manipulate file upload parameters, leading to unauthorized file placement and potentially remote code execution (RCE).
These platforms provide developers with powerful tools to monitor, debug, and optimize AI agents, ensuring their reliability, efficiency, and scalability. Let's explore the key features of these platforms and examine some codeexamples to illustrate their practical applications.
This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently.
The IT world is rife with jargon — and “as code” is no exception. “As code” means simplifying complex and time-consuming tasks by automating some, or all, of their processes. Today, the composable nature of code enables skilled IT teams to create and customize automated solutions capable of improving efficiency.
Most of these leverage the unique capability of Dynatrace OneAgent® to extract business data from in-flight application payloads — without writing any code. Simplified and enhanced analytics efficiency. Since then, many of our customers have embraced the opportunity to explore and adopt new business analytics use cases.
Certain service-level objective examples can help organizations get started on measuring and delivering metrics that matter. Teams can build on these SLO examples to improve application performance and reliability. In this post, I’ll lay out five SLO examples that every DevOps and SRE team should consider. or 99.99% of the time.
When Davis detects deviations from this baseline (for example, a sudden dip in usage or a user action that lasts longer than expected), it generates a problem event , identifies the root cause of the problem, and sends notifications based on the configured alerting profile. OneAgent for mobile apps is specific to iOS and Android.
This release resulted from several months of hard work where we had to thoroughly review our data model and implement a very resource-efficient permissions calculation mechanism. Disclaimer: I’ll be using Django in my codeexamples, but the ideas can be generalized; however, a relational database is a stronger requirement.
Without an efficient data retention strategy, this approach may struggle to scale effectively. Reducing Code Complexity : We reduce a lot of code complexity in Counter Abstraction by delegating a major portion of the functionality to an existing service. This is where most of the complexity in Counter Abstraction comes in.
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. Get started monitoring Red Hat OpenShift on IBM Z and LinuxONE New to Dynatrace?
This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.
The automated extraction of ownership information, for example, from Kubernetes annotations, is therefore essential. How to efficiently introduce team ownerships Dynatrace provides different ways of associating team ownership with entities and adding desired team metadata, such as contact details, to your environments.
Theyre often categorized by their function; core processes directly create customer value, support processes increase departmental efficiency, and management processes drive strategic goals and compliance. Log files and APIs are the most common business data sources, and software agents may offer a simpler no-code option.
Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. Most approaches focus on improving Power Usage Effectiveness (PUE), a data center energy-efficiency measure. energy-efficient data centers—cloud providers—achieve values closer to 1.2. A PUE of 1.0
Broadcast variables can be used to efficiently distribute large read-only data structures, such as lookup tables, to worker nodes. For example, to broadcast a lookup table named lookup_table : Use Broadcast Variables Broadcast variables are read-only variables that can be shared across nodes in a Spark cluster.
Indeed, according to one survey, DevOps practices have led to 60% of developers releasing code twice as quickly. But increased speed creates a tradeoff: According to another study, nearly half of organizations consciously deploy vulnerable code because of time pressure. Increased adoption of Infrastructure as code (IaC).
For example: Exploitation awareness: Identify vulnerabilities based on whether they’re exposed to critical assets or have exploits circulating in the wild. Unified vulnerabilities view The new Dynatrace platform consolidates third-party and code-level vulnerabilities into a single, intuitive view.
Dynatrace Configuration as Code enables complete automation of the Dynatrace platform’s configuration, ensuring that software is secure and reliable. With Configuration as Code, developers can manage their observability and security tasks with config files that can be developed alongside source code conveniently and at scale.
This is one example of the many use cases we’re exploring. For example, it can help DevOps and platform engineering teams write code snippets by drawing on information from software libraries. It highlights the potential of GPT technology to drive “information democracy” even further.
Scale with confidence: Leverage AI for instant insights and preventive operations Using Dynatrace, Operations, SRE, and DevOps teams can scale efficiently while maintaining software quality and ensuring security and reliability. To experience the possibilities of AI-empowered dashboards , try our example dashboard on the Dynatrace Playground.
The complexity and numerous moving parts of Kubernetes multicloud clusters mean that when monitoring the health of these clusters—which is critical for ensuring reliable and efficient operation of the application—platform engineers often find themselves without an easy and efficient solution.
Together, these practices ensure better collaboration and greater efficiency for DevOps teams throughout the software development life cycle. When they check in their code, the build management system automatically creates a build and tests it. If the test fails, the system notifies the team to fix the code.
The DevOps playbook has proven its value for many organizations by improving software development agility, efficiency, and speed. This method known as GitOps would also boost the speed and efficiency of practicing DevOps organizations. Development teams use GitOps to specify their infrastructure requirements in code.
In this article, we’ll explore these challenges in detail and introduce Keptn, an open source project that addresses these issues, enhancing Kubernetes observability for smoother and more efficient deployments. Insufficient CPU and memory allocation to pods can lead to resource contention and stop Pods from being created.
The OpenTelemetry Protocol (OTLP) plays a critical role in this framework by standardizing how systems format and transport telemetry data, ensuring that data is interoperable and transmitted efficiently. OpenTelemetry provides [extensive documentation]([link] and examples to help you fine-tune your configuration for maximum effectiveness.
Business and technology leaders are increasing their investments in AI to achieve business goals and improve operational efficiency. From generating new code and boosting developer productivity to finding the root cause of performance issues with ease, the benefits of AI are numerous.
Onboarding teams using self-service Kubernetes selectors is one of the best examples of how Dynatrace embraces cloud native technologies to increase automation, reduce bureaucracy, and encourage agility. The following example drives the point home. Embracing cloud native best practices to increase automation. Putting it all together.
As global warming advances, growing IT carbon footprints are pushing energy-efficient computing to the top of many organizations’ priority lists. Energy efficiency is a key reason why organizations are migrating workloads from energy-intensive on-premises environments to more efficient cloud platforms.
As cloud environments become increasingly ubiquitous, so does the need for effective and efficient management. This helps organizations to achieve proactive prevention, efficient operation, faster remediation, strong protection, and business acceleration. Example workflow for event-driven vulnerability reporting and escalation.
Let’s dive into how these metrics and DevOps KPIs can help your team perform better and deliver better code. Deployment frequency measures both long-term and short-term efficiency. For example, by measuring deployment frequency daily or weekly, you can determine how efficiently your team is responding to process changes.
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. Application security has historically been addressed after development is completed.
For example, a CNBC report found that training just one LLM can cost millions of dollars, and then millions more to update. How cloud cost optimization mitigates the effects of tool sprawl For example, the Dynatrace team investigated its Amazon Elastic Block Store (EBS) usage. Generative AI and LLMs are compounding these figures.
The following is an example of a query using the Dynatrace Query Language (DQL) to find out when BSOD issues are being written to Windows System logs. The Dynatrace platform establishes context across all observability data sources – metrics, events, logs, traces, user sessions, synthetic probes, runtime security vulnerabilities, and more.
Yet, ensuring code quality and breaking down silos are some of the many challenges that come with DevOps methodologies. In a similar way that developers automate a single task to improve consistency, efficiency, and speed, orchestration tools can coordinate the automation of tasks across platforms. Automation versus orchestration.
link] Example: Netflix Studio Production Money Heist (La casa de papel) / Netflix Let’s assume there is a Production service that manages Studio Content Productions (in the film and TV industry, the term production refers to the process of making a movie, not the environment to run a software). Our protobuf message definition (.proto
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