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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

Today, we’re excited to present the Distributed Counter Abstraction. In this context, they refer to a count very close to accurate, presented with minimal delays. After selecting a mode, users can interact with APIs without needing to worry about the underlying storage mechanisms and counting methods.

Latency 247
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Microsoft Ignite 2024 guide: Cloud observability for AI transformation

Dynatrace

The Grail™ data lakehouse provides fast, auto-indexed, schema-on-read storage with massively parallel processing (MPP) to deliver immediate, contextualized answers from all data at scale. However, data overload and skills shortages present challenges that companies need to address to maximize the benefits of cloud and AI technologies.

Cloud 263
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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. On the other hand, these optimizations themselves need to be sufficiently inexpensive to justify their own processing cost over the gains they bring.

Storage 208
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Introducing Impressions at Netflix

The Netflix TechBlog

It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure. In this multi-part blog series, we take you behind the scenes of our system that processes billions of impressions daily.

Tuning 165
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

As Netflix expanded globally and the volume of title launches skyrocketed, the operational challenges of maintaining this manual process became undeniable. Metadata and assets must be correctly configured, data must flow seamlessly, microservices must process titles without error, and algorithms must function as intended.

Traffic 168
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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. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ?

Latency 147
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Top PostgreSQL 17 New Features

Scalegrid

Heres what stands out: Key Takeaways Better Performance: Faster write operations and improved vacuum processes help handle high-concurrency workloads more smoothly. Improved Vacuuming: A redesigned memory structure lowers resource use and speeds up the vacuum process. JSON_QUERY extracts JSON fragments based on query conditions.

Speed 130