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New integrations announced at AWS re:Invent enhance cloud performance, security, and automation

Dynatrace

This year’s AWS re:Invent will showcase a suite of new AWS and Dynatrace integrations designed to enhance cloud performance, security, and automation. This is particularly valuable for enterprises deeply invested in VMware infrastructure, as it enables them to fully harness the advantages of cloud computing.

AWS 298
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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. FUN FACT : In this talk , Rodrigo Schmidt, director of engineering at Instagram talks about the different challenges they have faced in scaling the data infrastructure at Instagram.

Design 334
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Dynatrace elevates data security with separated storage and unique encryption keys for each tenant

Dynatrace

Protect data in multi-tenant architectures To bring you the most value by unifying observability and security in one analytics and automation platform powered by AI, Dynatrace SaaS leverages a multitenancy architecture, enabling efficient and scalable data ingestion, querying, and processing on shared infrastructure.

Storage 246
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What is hyperconverged infrastructure? Realizing the benefits of HCI

Dynatrace

Therefore, they need an environment that offers scalable computing, storage, and networking. That’s where hyperconverged infrastructure, or HCI, comes in. What is hyperconverged infrastructure? For organizations managing a hybrid cloud infrastructure , HCI has become a go-to strategy. Realizing the benefits of HCI.

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RabbitMQ vs. Kafka: Key Differences

Scalegrid

RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. This decoupling simplifies system architecture and supports scalability in distributed environments. Choosing between RabbitMQ and Kafka depends on your specific messaging needs.

Latency 147
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage.

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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251