Remove Servers Remove Storage Remove Tuning
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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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Tuning EMQX To Scale to One Million Concurrent Connection on Kubernetes

DZone

When building an IoT-based service, we need to implement a messaging mechanism that transmits data collected by the IoT devices to a hub or a server. When dealing with IoT, one of the first things that come to mind is the limited processing, networking, and storage capabilities these devices operate with.

IoT 305
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Introducing Impressions at Netflix

The Netflix TechBlog

These events are promptly relayed from the client side to our servers, entering a centralized event processing queue. The enriched data is seamlessly accessible for both real-time applications via Kafka and historical analysis through storage in an Apache Iceberg table.

Tuning 166
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The Challenges of Ajax CDN

DZone

For the longest time, hosting static files on CDNs was the de facto standard for performance tuning website pages. The host offered browser caching advantages, better stability, and storage on fast edge servers across strategic geolocations. Not only did it have performance benefits, but it was also convenient for developers.

Cache 305
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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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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Our goal was to build a versatile and efficient data storage solution that could handle a wide variety of use cases, ranging from the simplest hashmaps to more complex data structures, all while ensuring high availability, tunable consistency, and low latency. Developers just provide their data problem rather than a database solution!

Latency 260
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Privacy spotlight: Control compliance in Dynatrace with multiple layers of sensitive data masking

Dynatrace

Masking at storage: Data is persistently masked upon ingestion into Dynatrace. Leverage three masking layers Masking at capture and masking at storage operations exclude targeted sensitive data points. To fine-tune your masking settings, select the entity you want to adjust and leverage the entity-specific settings.