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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 228
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Mobile application monitoring with Dynatrace: How an end-to-end platform advances mobile DevOps

Dynatrace

To effectively and efficiently get mobile apps out the door, monitor their performance, and manage subsequent releases, mobile DevOps practitioners can play an integral role. DevOps tasks become significantly more manageable with an all-in-one platform that offers automated instrumentation and AI capabilities out of the box.

Mobile 130
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Kubernetes vs Docker: What’s the difference?

Dynatrace

The time and effort saved with testing and deployment are a game-changer for DevOps. This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. In production, containers are easy to replicate. What is Docker? Networking. Observability.

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The Ultimate Guide to Database High Availability

Percona

To make data count and to ensure cloud computing is unabated, companies and organizations must have highly available databases. A basic high availability database system provides failover (preferably automatic) from a primary database node to redundant nodes within a cluster. HA is sometimes confused with “fault tolerance.”

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Lessons learned from enterprise service-level objective management

Dynatrace

A service-level objective ( SLO ) is the new contract between business, DevOps, and site reliability engineers (SREs). This greatly reduced the number of metrics to manage and provided a more comprehensive picture of what was behind their primary reliability service-level objective. The metrics behind the four signals vary by row.

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AI techniques enhance and accelerate exploratory data analytics

Dynatrace

Exploratory data analytics is an analysis method that uses visualizations, including graphs and charts, to help IT teams investigate emerging data trends and circumvent issues, such as unexpected traffic spikes or performance degradations. Start by asking yourself what’s there, whether it’s logs, metrics, or traces.

Analytics 219
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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. This unified approach enables Grail to vault past the limitations of traditional databases. And without the encumbrances of traditional databases, Grail performs fast. “In

Analytics 195