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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. Flink is the best candidate when EOS guarantees are necessary.

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What is IT automation?

Dynatrace

Automating IT practices without integrated AIOps presents several challenges. Expect to spend time fine-tuning automation scripts as you find the right balance between automated and manual processing. By tuning workflows, you can increase their efficiency and effectiveness. The challenges of automating IT and how to combat them.

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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

Observability is essential to ensure the reliability, security and quality of any software system. Data visualization : how to present, explore and interpret observability data from serverless functions intuitively, clearly, and holistically? Such anomalies can be caused by function cold-starts.

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New Prometheus-based extensions enable intelligent observability for more than 200 additional technologies

Dynatrace

Building on its advanced analytics capabilities for Prometheus data , Dynatrace now enables you to create extensions based on Prometheus metrics. Without any coding skills required, you can declare extensions in a human-readable YAML format and activate them in your environment via the Dynatrace Software Intelligence Hub.

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Observability throughout the software development lifecycle increases delivery performance

Dynatrace

This leads to frustrating bottlenecks for developers attempting to build and deliver software. A central element of platform engineering teams is a robust Internal Developer Platform (IDP), which encompasses a set of tools, services, and infrastructure that enables developers to build, test, and deploy software applications.

Software 242
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Mastering MongoDB® Timeout Settings

Scalegrid

For example, your payment history might be on one database cluster and your analytics records on another cluster. If your analytics server is down, then each operation will wait for a default of 30 seconds before failing (which may or may not be what you want). Careful consideration must be given before making changes. </p>

Java 130
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Best Practices for a Seamless MongoDB Upgrade

Percona

Introduction of clustered collections for optimized analytical queries. Improved performance : MongoDB continually fine-tunes its database engine, resulting in faster query execution and reduced latency. Protection against data loss : Older MongoDB versions pose a risk of data loss due to unsupported software. In MongoDB 6.x: