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DZone Survey 2023 Tracks Range of Container Experiences

d2iq

Only 22% of respondents said their company uses microservices, while in our 2022 Microservices survey, the rate was 93%, and in the 2021 Containers survey, it was 74%. Respondents at organizations using microservices had significantly more code and computational work running in containers.

Survey 52
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NiFi as a Function in DataFlow Service

Cloudera

NiFi as a Function in DataFlow Service provides an efficient, cost optimized, scalable way to run NiFi flows in a completely serverless fashion. It also effectively provides a serverless architecture and is very widely used when building microservices applications. However, for certain use cases, we want to go one step further.

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Serverless NiFi Flows with DataFlow Functions: The Next Step in the DataFlow Service Evolution

Cloudera

New use cases: event-driven, batch, and microservices. CDF-PC’s DataFlow Deployments provide a cloud-native runtime to run your Apache NiFi flows through auto-scaling Kubernetes clusters as well as centralized monitoring and alerting and improved SDLC for developers. near real-time offloading of files from a remote SFTP server).

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The DevOps Trends Forecast for 2020

RapidValue

Some of the notable technologies and tools boosting the cloud-native model are microservices, containerization, Agile methodology, CI/CD and the like. . With faster deployments, scalability and improved visibility across applications, cloud computing is a hit among DevOps-minded teams. The Switch to Assembly Lines. billion by 2023. .

DevOps 69
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Integrating Security Into Your CI/CD Pipelines

Modus Create

Automating Security In Your SDLC. The increased use of containers for scalable and portable deployments introduces an extra attack surface, and it is essential to ensure that containers are deployed as securely as possible. For example, a microservices architecture introduces new infrastructure components and unknown attack surfaces.

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A complete guide on DevSecOps!

Openxcell

To improve security at every stage of the software development lifecycle, engineering teams must build it in from the start (SDLC). Its primary goal is to deliver superior security while improving process speed, scalability, and accessibility. Challenges in infrastructure to microservices. Shortage of AppSec tool integration.

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Kubernetes and Artificial Intelligence/Machine Learning (AI/ML) — Four Things to Understand Today

Blue Sentry

Even if they do, many projects get stuck in the ever-so-fragile SDLC. Many go over budget, over time, and get trapped in the bottomless pit of scalability. Now you have to manage your avalanche of microservices to enable those machine learning workflows you’ve always dreamed about. It’s a nightmare.