Remove Availability Remove Storage Remove Tuning
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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability.

Tuning 165
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

Both categories share common requirements, such as high throughput and high availability. After selecting a mode, users can interact with APIs without needing to worry about the underlying storage mechanisms and counting methods. The table below provides a detailed overview of the diverse requirements across these two categories.

Latency 248
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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 209
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. Its design prioritizes high availability and efficient data transfer with minimal overhead, making it a practical choice for handling real-time data pipelines and distributed event processing. What is RabbitMQ?

Latency 147
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

Data storage and distribution through HollowFeeds Netflix Hollow is an Open Source java library and toolset for disseminating in-memory datasets from a single producer to many consumers for high performance read-only access. The results are returned in a standardized format, ensuring easy support for futureUIs.

Traffic 172
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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. Open the available tabs to explore and easily tailor your data privacy settings.

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Storage Strategies for PostgreSQL on Kubernetes

Percona

There are a wealth of options on how you can approach storage configuration in Percona Operator for PostgreSQL , and in this blog post, we review various storage strategies — from basics to more sophisticated use cases. For example, you can choose the public cloud storage type – gp3, io2, etc, or set file system.

Storage 122