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What is DataOps? Collaborative, cross-functional analytics

CIO

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. DataOps goals According to Dataversity , the goal of DataOps is to streamline the design, development, and maintenance of applications based on data and data analytics. What is DataOps?

Analytics 317
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Akuity raises $20M to simplify Kubernetes container management

TechCrunch

Apps are increasingly built using containers, or “microservices” packaged with all the necessary dependencies and configuration files. In software development, “continuous delivery” refers to the engineering approach where teams create software in short cycles to ensure that it can be reliably released at any time.)

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7 sins of digital transformation

CIO

“Few companies realize the role that organizational structure and culture play in driving transformation, and instead focus solely on the technology,” says Sunil Senan, SVP and global head of data, analytics, and AI at Infosys.

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DataOps: Adjusting DevOps for Analytics Product Development

Altexsoft

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

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Important Practices for DevOps in the Cloud

OTS Solutions

Microservices Microservices have emerged as a powerful approach in the field of DevOps, especially in the cloud environment. By breaking down complex applications into smaller, independent components, microservices allow for better scalability, flexibility, and fault tolerance.

DevOps 130
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Dev vs. Ops: 5 Problems That Make Dev Fight with Ops

OverOps

Everyone in tech is busy discussing Kubernetes, containers, and microservices as if the basics of DevOps and continuous delivery are all figured out. Each has multiple server instances, and those instances might have multiple microservices, distributed or not, containerized or not. The lay of the land gets quite complex.

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DevOps tools: Automation, Monitoring, CI/CD, and more

Altexsoft

In this article, we’ll discuss the categories of tools existing for DevOps and look at instruments for continuous delivery/integration, testing, monitoring, collaboration, code management, and more. Continuous delivery (CD). While in continuous deployment, pipelines deploy code automatically and constantly.

DevOps 116