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RabbitMQ vs. Kafka: Key Differences

Scalegrid

This decoupling simplifies system architecture and supports scalability in distributed environments. Kafka stores and distributes data through a partitioned log system, which spans multiple brokers to provide fault tolerance and scalability. What is RabbitMQ? This allows Kafka clusters to handle high-throughput workloads efficiently.

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PostgreSQL Benchmark: ScaleGrid vs. Amazon RDS

Scalegrid

Performance Benchmarking of PostgreSQL on ScaleGrid vs. AWS RDS Using Sysbench This article evaluates PostgreSQL’s performance on ScaleGrid and AWS RDS, focusing on versions 13, 14, and 15. This study benchmarks PostgreSQL performance across two leading managed database platforms—ScaleGrid and AWS RDS—using versions 13, 14, and 15.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. ShuffleBench i s a benchmarking tool for evaluating the performance of modern stream processing frameworks. Recovery time of the latency p90. However, we noticed that GPT 3.5

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PostgreSQL vs. Oracle: Difference in Costs, Ease of Use & Functionality

Scalegrid

Compare ease of use across compatibility, extensions, tuning, operating systems, languages and support providers. Scalability. PostgreSQL offers free scalability, and can scale up to millions of transactions per seconds. Oracle Enterprise is recommended for high workloads which are highly scalable, but costly. PostgreSQL.

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10 tips for migrating from monolith to microservices

Dynatrace

Because they’re separate, they allow for faster release cycles, greater scalability, and the flexibility to test new methodologies and technologies. Use SLAs, SLOs, and SLIs as performance benchmarks for newly migrated microservices. Keeping track of the migration stages, phases, and environments is not always easy.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

An additional implication of a lenient sampling policy is the need for scalable stream processing and storage infrastructure fleets to handle increased data volume. Our engineering teams tuned their services for performance after factoring in increased resource utilization due to tracing. Storage: don’t break the bank!

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Building a Media Understanding Platform for ML Innovations

The Netflix TechBlog

In addition, we were able to perform a handful of A/B tests to validate or negate our hypotheses for tuning the search experience. The primary searcher used in the current implementation is called Marken  — scalable annotation service built at Netflix. We will continue to share our work in this space, so stay tuned.

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