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DevOps monitoring tools: How to drive DevOps efficiency

Dynatrace

With the world’s increased reliance on digital services and the organizational pressure on IT teams to innovate faster, the need for DevOps monitoring tools has grown exponentially. But when and how does DevOps monitoring fit into the process? And how do DevOps monitoring tools help teams achieve DevOps efficiency?

DevOps 216
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DevOps observability: A guide for DevOps and DevSecOps teams

Dynatrace

As organizations accelerate innovation to keep pace with digital transformation, DevOps observability is becoming a critical key to success for DevOps and DevSecOps teams. However, getting reliable answers from observability data so teams can automate more processes to ensure speed, quality, and reliability can be challenging.

DevOps 194
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Is DevOps dead? Exploring the changing IT landscape and future of DevOps

Dynatrace

Just as organizations have increasingly shifted from on-premises environments to those in the cloud, development and operations teams now work together in a DevOps framework rather than in silos. But as digital transformation persists, new inefficiencies are emerging and changing the future of DevOps.

DevOps 160
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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

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.

Analytics 182
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Financial services IT leaders can boost customer lifetime value with DevSecOps-driven innovation

Dynatrace

Customer lifetime value (CLV) has long been established as the key metric financial services firms use to gauge their profitability and competitive position in the market. The growing demand for rapid innovation is worsened by the ongoing skills shortages. This puts sensitive customer and transactional information at risk.

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Introducing Dynatrace built-in data observability on Davis AI and Grail

Dynatrace

I have ingested important custom data into Dynatrace, critical to running my applications and making accurate business decisions… but can I trust the accuracy and reliability?” ” Welcome to the world of data observability. At its core, data observability is about ensuring the availability, reliability, and quality of data.

DevOps 192
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What is predictive AI? How this data-driven technique gives foresight to IT teams

Dynatrace

But when these teams work in largely manual ways, they don’t have time for innovation and strategic projects that might deliver greater value. Predictive AI uses machine learning, data analysis, statistical models, and AI methods to predict anomalies, identify patterns, and create forecasts. Proactive resource allocation.