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What is security analytics?

Dynatrace

As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.

Analytics 226
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Dynatrace extends contextual analytics and AIOps for open observability

Dynatrace

Today’s digital businesses run on heterogeneous and highly dynamic architectures with interconnected applications and microservices deployed via Kubernetes and other cloud-native platforms. Common questions include: Where do bottlenecks occur in our architecture? Dynatrace extends its unique topology-based analytics and AIOps approach.

Analytics 246
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Full Kubernetes logging in context: Easily stream logs from Fluent Bit to Dynatrace

Dynatrace

Log collection platforms, such as Fluent Bit, give organizations a much-needed solution for quickly gathering and processing log data to make it available in different backends for further analytics. Fluent Bit, an open source tool within the CNCF ecosystem, is a popular choice when choosing which log collection tool to use.

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

Dynatrace

In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. From data lakehouse to an analytics platform Traditionally, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.

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RSA Guide 2023: Cloud application security remains core challenge for organizations

Dynatrace

Cloud application security remains challenging because organizations lack end-to-end visibility into cloud architecture. As organizations migrate applications to the cloud, they must balance the agility that microservices architecture brings with the complexity and lack of transparency that can also come with it.

Cloud 182
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. We designed experimental scenarios inspired by chaos engineering.

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Why Use “Real-Time Digital Twins” for Streaming Analytics?

ScaleOut Software

And how are they different from streaming pipelines like Azure Stream Analytics and Apache Flink/Beam? What Problems Does Streaming Analytics Solve? To understand why we need real-time digital twins for streaming analytics, we first need to look at what problems are tackled by popular streaming platforms.