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Why applying chaos engineering to data-intensive applications matters

Dynatrace

This high level of abstraction is provided by industry-grade, open source stream processing frameworks such as Kafka Streams , Apache Flink , and Spark Structured Streaming. Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume.

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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. End-to-end observability starts with tracking logs, metrics, and traces of all the components, providing a better understanding of service relationships and application dependencies.

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HammerDB for Managers

HammerDB

HammerDB is a software application for database benchmarking. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. The Transaction Processing Performance Council (TPC) was founded to bring standards to database benchmarking, and the history of the TPC can be found here.

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Plan Your Multi Cloud Strategy

Scalegrid

This process thoroughly assesses factors like cost-effectiveness, security measures, control levels, scalability options, customization possibilities, performance standards, and availability expectations. Choosing the Right Cloud Services Choosing the right cloud services is crucial in developing an efficient multi cloud strategy.

Strategy 130
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. In order to maintain performance, benchmarking is a vital part of our system’s lifecycle.

AWS 100
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. In order to maintain performance, benchmarking is a vital part of our system’s lifecycle.

AWS 100
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Troubleshooting a session in Edgar When we started building Edgar four years ago, there were very few open-source distributed tracing systems that satisfied our needs. Our tactical approach was to use Netflix-specific libraries for collecting traces from Java-based streaming services until open source tracer libraries matured.