Remove 2023 Remove Infrastructure Remove Scalability Remove Storage
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Kubernetes in the wild report 2023

Dynatrace

Findings provide insights into Kubernetes practitioners’ infrastructure preferences and how they use advanced Kubernetes platform technologies. Kubernetes infrastructure models differ between cloud and on-premises. Cloud-hosted Kubernetes clusters are on par to overtake on-premises deployments in 2023.

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AWS re:Invent 2023 guide: Generative AI takes a front seat

Dynatrace

At the AWS re:Invent 2023 conference, generative AI is a centerpiece. What’s more, in the McKinsey report “ The State of AI 2023 ,” 40% of respondents say their organizations will increase their overall AI investment because of advances in generative AI. Today’s IT organizations face unprecedented challenges.

AWS 211
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Perform 2023 Guide: Organizations mine efficiencies with automation, causal AI

Dynatrace

Moving to greater automation is a key goal for 2023, as organizations face economic uncertainty and need to stay innovative while remaining cost-efficient. In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. We’ll post news here as it happens!

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Web Development Trends in 2023

KeyCDN

Here is a roundup of frontend web development trends to keep an eye on in 2023. Cloud-based development and deployment One of the main advantages of cloud-based development and deployment is scalability. The main benefits of serverless architecture are cost savings and scalability.

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Conducting log analysis with an observability platform and full data context

Dynatrace

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. They enable IT teams to identify and address the precise cause of application and infrastructure issues.

Analytics 190
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Why growing AI adoption requires an AI observability strategy

Dynatrace

AI requires more compute and storage. Training AI data is resource-intensive and costly, again, because of increased computational and storage requirements. As a result, AI observability supports cloud FinOps efforts by identifying how AI adoption spikes costs because of increased usage of storage and compute resources.

Strategy 224
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Causal AI use cases for modern observability that can transform any business

Dynatrace

And while generative AI was much hyped in 2023, the deterministic quality of causal AI—which determines the precise root cause of an issue—is a key foundation for reliable recommendations that emerge from generative AI technologies. Data lakehouses combine a data lake’s flexible storage with a data warehouse’s fast performance.