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From Proprietary to Open Source: The Complete Guide to Database Migration

Percona

Migrating a proprietary database to open source is a major decision that can significantly affect your organization. Advantages of migrating to open source For many reasons mentioned earlier, organizations are increasingly shifting towards open source databases for their data management needs.

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Prevent potential problems quickly and efficiently with Davis exploratory analysis

Dynatrace

This is done without the need to create custom dashboards and is complemented by efficient analysis capabilities that automatically guide SREs to potential root causes of anomalies, enabling more efficient work and freeing up time for essential workflows. Henrik Rexed, Open-Source Advocate.

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Kubernetes vs Docker: What’s the difference?

Dynatrace

This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. Docker Engine is built on top containerd , the leading open-source container runtime, a project of the Cloud Native Computing Foundation (DNCF). What is Docker? What is Kubernetes?

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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

We started seeing signs of scale issues, like: Slowness during peak traffic moments like 12 AM UTC, leading to increased operational burden. At Netflix, the peak traffic load can be a few orders of magnitude higher than the average load. Hence, the system has to withstand bursts in traffic while still maintaining the SLO requirements.

Java 202
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Dynatrace Application Security protects your applications in complex cloud environments

Dynatrace

Open source has also become a fundamental building block of the entire cloud-native stack. While leveraging cloud-native platforms, open-source and third-party libraries accelerate time to value significantly, it also creates new challenges for application security.

Cloud 296
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Exploring MySQL 8 Priority-Based Error Log Filtering

Percona

MySQL 8 introduces Error Log Filtering as a mechanism to fine-tune the error log, allowing administrators to focus on the most critical issues. This is particularly beneficial in high-traffic environments where minimizing log noise is crucial for efficient log analysis.

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

Dynatrace

For example, these include verifying app deployments, isolating faults coming from a single IP address, identifying root causes of traffic spikes, or investigating malicious user activity. Open source solutions are also making tracing harder.

Analytics 195