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Microsoft Azure digital transformation: Embracing the power of cloud technology with next-gen observability

Dynatrace

To make this happen, enterprises are shifting an unprecedented volume of workloads onto cloud platforms such as Microsoft Azure. Digital transformation is only going to speed up, not slow down, and companies must remain on top of it. The speed of change is only going to accelerate, thus requiring more innovation.

Azure 164
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The top four log analytics and log management best practices

Dynatrace

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.

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Microsoft Ignite 2023 guide: AI transformation and Microsoft Azure

Dynatrace

To keep up, organizations are making significant investments to harness this technology and unlock new opportunities to thrive in the era of AI with Microsoft Azure and adjacent technologies. As a Microsoft strategic partner, Dynatrace delivers answers and intelligent automation for cloud-native technologies and Azure.

Azure 130
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Why log monitoring and log analytics matter in a hyperscale world

Dynatrace

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.

Analytics 198
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Support diverse analytics workloads. What is a data lakehouse? Data management.

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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. Observability is not only about measuring performance and speed, but also about capturing granular business analytics to support data-driven decision-making.

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Enhance data management with Grail: Ultimate guide to custom buckets and security policies

Dynatrace

This improves query speeds and reduces related costs for all other teams and apps. If your typical queries only target a specific use case, business unit, or production stage, ensuring they don’t include unrelated buckets helps maintain efficiency and relevance. Custom buckets unlock different retention periods.