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This year’s AWS re:Invent will showcase a suite of new AWS and Dynatrace integrations designed to enhance cloud performance, security, and automation. This blog post will explore these exciting developments and what they mean for organizations.
Developers are key stakeholders in modern observability. 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!
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 facilitate easier access to incrementality results, we have developed an interactive tool powered by this framework.
When I founded Dynatrace, I aimed to bridge the gap between IT performance and user experience. Using causal AI, we identified and resolved performance issues automatically. Key insights for executives: Optimize customer experiences through end-to-end contextual analytics from observability, user behavior, and business data.
Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. UMELT are kept cost-effectively in a massive parallel processing data lakehouse, enabling contextual analytics at petabyte scale, fast.
Today, development teams suffer from a lack of automation for time-consuming tasks, the absence of standardization due to an overabundance of tool options, and insufficiently mature DevSecOps processes. This leads to frustrating bottlenecks for developers attempting to build and deliver software.
Dynatrace enables various teams, such as developers, threat hunters, business analysts, and DevOps, to effortlessly consume advanced log insights within a single platform. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
When it comes to mobile monitoring, everyone has their own point of view… Mobile is not a single technology: it involves different development teams handling Android and iOS apps, performance engineering teams, cloud operations, and marketing. How do I connect the dots between mobile analytics and performance monitoring?
Its AI-driven exploratory analytics help organizations navigate modern software deployment complexities, quickly identify issues before they arise, shorten remediation journeys, and enable preventive operations. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act.
Perform is our company’s event once a year in Las Vegas, where our customers and partners visit us to learn more about our product and industry. However, it was my first time at Perform, and although I knew I would learn a thing or two in the next week, I was unaware of how beneficial taking part in this event would be.
By following key log analytics and log management best practices, teams can get more business value from their data. Challenges driving the need for log analytics and log management best practices As organizations undergo digital transformation and adopt more cloud computing techniques, data volume is proliferating.
This results in site reliability engineers nudging development teams to add resource attributes, endpoints, and tokens to their source code. Thus, measuring application performance becomes an unnecessarily frustrating coordination effort between teams. Code changes are often required to refine observability data.
As user experiences become increasingly important to bottom-line growth, organizations are turning to behavior analytics tools to understand the user experience across their digital properties. Here’s what these analytics are, how they work, and the benefits your organization can realize from using them.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Log monitoring is a process by which developers and administrators continuously observe logs as they’re being recorded. What is log analytics?
Executives invest in Dynatrace to enable their IT operations, security, and development teams to maintain visibility into all their digital services and ensure flawless, secure digital interactions. Executives drive business growth through strategic decisions, relying on data analytics for crucial insights.
The Dynatrace platform automatically captures and maps metrics, logs, traces, events, user experience data, and security signals into a single datastore, performing contextual analytics through a “power of three AI”—combining causal, predictive, and generative AI. What’s behind it all? The result?
Mobile app monitoring and mobile analytics make this possible. By providing insight into how apps are operating and why they crash, mobile analytics lets you know what’s happening with your apps and what steps you can take to solve potential problems. What is mobile analytics? Why use mobile analytics and app monitoring?
Analytics at Netflix: Who We Are and What We Do An Introduction to Analytics and Visualization Engineering at Netflix by Molly Jackman & Meghana Reddy Explained: Season 1 (Photo Credit: Netflix) Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making.
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Traditionally, it’s critical for Dev and Ops teams to be able to quickly discover and remediate application performance and customer-facing issues. On the other side of the organization, application owners have hired teams of analysts to dig through web analytics tools to gain insights into the customer experience.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
We introduced Dynatrace’s Digital Business Analytics in part one , as a way for our customers to tie business metrics to application performance and user experience, delivering unified insights into how these metrics influence business milestones and KPIs. Only with Dynatrace Digital Busines Analytics.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
Much of the software developed today is cloud native. Organizations need to unify all this observability, business, and security data based on context and generate real-time insights to inform actions taken by automation systems, as well as business, development, operations, and security teams. Enter Grail-powered data and analytics.
In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. These are just some of the topics being showcased at Perform 2023 in Las Vegas. Perform 2023 news At Perform 2023 in Las Vegas, the headliner theme is IT automation. What is a data lakehouse?
Exploding volumes of business data promise great potential; real-time business insights and exploratory analytics can support agile investment decisions and automation driven by a shared view of measurable business goals. For additional technical insights, watch the Business Events Performance Clinic. What’s next?
Although most organizations invest in innovative mobile app development, not many allocate enough resources toward delivering and measuring the high-quality user experiences customers expect. Mobile analytics can help organizations optimize their mobile application performance, earning customer accolades and increasing revenue in the process.
With extended contextual analytics and AIOps for open observability, Dynatrace now provides you with deep insights into every entity in your IT landscape, enabling you to seamlessly integrate metrics, logs, and traces—the three pillars of observability. How can we optimize for performance and scalability?
But as with many other automation tools, it can be difficult to maintain the performance and visibility of these workflows. Improving collaboration across teams By surfacing actionable insights and centralized monitoring data, Dynatrace fosters collaboration between development, operations, security, and business teams.
While Dynatrace provides software intelligence to accelerate your company’s digital transformation, web analytics tools like Adobe Analytics help you deeply understand your user journeys, segmentation, behavior, and strategic business metrics such as revenue, orders, and conversion goals. Google Analytics.
This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. Apache Kafka uses a custom TCP/IP protocol for high throughput and low latency.
Echoing John Van Siclen’s sentiments from his Perform 2020 keynote, Steve cited Dynatrace customers as the inspiration and driving force for these innovations. “A Highlighting the company’s announcements from Perform 2020, Steve and a team of other Dynatrace product leaders introduced the audience to several of our latest innovations.
For this reason, end-to-end observability that offers a holistic understanding of problems and their impact on application performance is rising in prevalence across organizations of all sizes and industries. In such a fragmented landscape, having clear, real-time insights into granular data for every system is crucial.
Wouldn’t it be great if I had an industry-leading software intelligence platform to monitor these apps, pinpoint root causes of slow performance or errors, and gain insights about my users’ experience? This also, unfortunately, alerts our product developers instead of me if the app causes an error on the dashboard. Agentless RUM.
As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. The goal is to abstract away the underlying infrastructure’s complexities while providing a streamlined and standardized environment for development teams.
Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes. What Exactly is Greenplum? At a glance – TLDR.
Uber uses Presto, an open-source distributed SQL query engine, to provide analytics across several data sources, including Apache Hive, Apache Pinot, MySQL, and Apache Kafka. To improve its performance, Uber engineers explored the advantages of dealing with quick queries, a.k.a.
Sometimes overlooked is a fourth category we might call long-tail processes; these are the ad hoc or custom workflows that develop in response to gaps between systems, applications, departments, or workflows. These benefits come from robust process analytics, often augmented by AI.
Every software development team grappling with Generative AI (GenAI) and LLM-based applications knows the challenge: how to observe, monitor, and secure production-level workloads at scale. Developers deserve a frictionless troubleshooting experience and fast access to real-time datano more guesswork or costly redeployments.
This allows platform engineers to focus on high-value tasks like resolving issues and optimizing performance rather than spending time on data discovery and exploration. This app provides advanced analytics, such as highlighting related surrounding traces and pinpointing the root cause, as illustrated in the example below.
Dynatrace OTel Collector Understand your applications with ease Due to a lack of contextual insights and actionable intelligence, application teams often find themselves overwhelmed by data, unable to quickly identify the root causes of performance issues. This eliminates the need for swapping tools or manual log correlation.
At the 2024 Dynatrace Perform conference in Las Vegas, Michael Winkler, senior principal product management at Dynatrace, ran a technical session exploring just some of the many ways in which Dynatrace helps to automate the processes around development, releases, and operation. Real-time detection for fast remediation.
As an application owner, product manager, or marketer, however, you might use analytics tools like Adobe Analytics to understand user behavior, user segmentation, and strategic business metrics such as revenue, orders, and conversion goals. Establish BizDevOps collaboration by sharing business context with IT in real time.
Let me address that by combining my two favourite topics: CSS and performance. It’s really, really bad for Start Render performance. The introduction of the Preload Scanner improved web page performance by around 19%, all without developers having to lift a finger. What’s the Big Problem? This is great news for users!
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