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Cut costs and complexity: 5 strategies for reducing tool sprawl with Dynatrace

Dynatrace

As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Simplify data ingestion and up-level storage for better, faster querying : With Dynatrace, petabytes of data are always hot for real-time insights, at a cold cost.

Strategy 296
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ? What is Apache Kafka?

Latency 147
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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
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Efficient Multimodal Data Processing: A Technical Deep Dive

DZone

Handling multimodal data spanning text, images, videos, and sensor inputs requires resilient architecture to manage the diversity of formats and scale.

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Introducing Impressions at Netflix

The Netflix TechBlog

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. 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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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 212
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CDNs: Speed Up Performance by Reducing Latency

DZone

In the previous posts, we covered things we had to do to upload files on the front end, things we had to do on the back end, and optimizing costs by moving file uploads to object storage.

Latency 195