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Dynatrace + Metis: Helping developers & SREs solve Database issues with AI

Dynatrace

And with cloud-native databases like PostgreSQL and MySQL, the complexity only grows. A shared vision At Dynatrace, weve built a comprehensive observability platform that already includes deep database visibility, the Top Database Statements view, and Grail for unified data storage and analysis.

Database 306
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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

As an example, cloud-based post-production editing and collaboration pipelines demand a complex set of functionalities, including the generation and hosting of high quality proxy content. It is worth pointing out that cloud processing is always subject to variable network conditions.

Cloud 242
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Part 1: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

Training: We created easy-to-provide feedback using and with a fully integrated fine-tuning loop to allow end-users to teach new domains and questions around it effectively. LORE also provides a confidence score to our end users based on its grounding in the domainspace.

Analytics 212
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. Message Broker vs. Distributed Event Streaming Platform RabbitMQ functions as a message broker, managing message confirmation, routing, storage, and delivery within a queue. What is RabbitMQ?

Latency 147
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Accelerate your cloud journey with Dynatrace observability for AWS S3 logs

Dynatrace

Logs complement metrics and enable automation Cloud practitioners agree that observability, security, and automation go hand in hand. The increasing complexity of cloud service architectures requires a rock-solid understanding of the activity, health status, and security of cloud services.

AWS 246
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

After selecting a mode, users can interact with APIs without needing to worry about the underlying storage mechanisms and counting methods. Let’s examine some of the drawbacks of this approach: Lack of Idempotency : There is no idempotency key baked into the storage data-model preventing users from safely retrying requests.

Latency 251
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No need to compromise visibility in public clouds with new Azure services supported by Dynatrace (Part 2)

Dynatrace

Fully automated observability into your Azure multi-cloud environment. You can integrate Dynatrace with Azure for intelligent monitoring of services running in Azure Cloud. Azure Data Lake Storage Gen1. Simplify cloud operations with full visibility into your Azure Automation accounts. Azure Logic Apps. Azure Event Grid.

Azure 235