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AWS observability: AWS monitoring best practices for resiliency

Dynatrace

These challenges make AWS observability a key practice for building and monitoring cloud-native applications. Let’s take a closer look at what observability in dynamic AWS environments means, why it’s so important, and some AWS monitoring best practices. Serverless technologies can reduce management complexity.

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MongoDB Database Backup: Best Practices & Expert Tips

Percona

That’s why it’s essential to implement the best practices and strategies for MongoDB database backups. The speed of backup also depends on allocated IOPS and type of storage since lots of read/writes would be happening during this process. Best practice tip : Use PBM to time huge backup sets.

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Best Practices for Efficient Log Management and Monitoring

DZone

This is especially true because of the distributed and dynamic nature of cloud-native apps, which are often deployed using ephemeral technologies like containers and serverless functions. This post discusses what we consider to be some of the best practices and standards to follow when logging and monitoring cloud-native applications.

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Automate CI/CD pipelines with Dynatrace: Part 2, Deploy stage

Dynatrace

Leverage OneAgent functionality Ingesting configuration changes into Dynatrace through the events API call and utilizing OneAgent to detect configuration changes for supported technologies help maintain close alignment between your staging and production environments. This approach effectively combats configuration drift.

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Enterprise Cloud Security Strategy For 2024

Scalegrid

The Rise of Cloud Adoption in Enterprises The adoption of cloud technology is leading to a shift in the business landscape. The selection process for a suitable cloud provider is crucial as it involves aligning business objectives, regulatory requirements, and current technological infrastructure with strong cloud security measures.

Strategy 130
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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

This data overload also prevents customer-centric pricing models as users consider cost-effective technology platforms. Dynatrace has developed the purpose-built data lakehouse, Grail , eliminating the need for separate management of indexes and storage. The majority of costs are associated with data querying.

Retail 230
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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 216