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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 195
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Overcoming the 6 barriers to IT modernization

CIO

It adopted a microservices architecture to decouple legacy components, allowing for incremental updates without disrupting the entire system. For instance, AT&T launched a comprehensive reskilling initiative called “Future Ready” to train employees in emerging technologies such as cloud computing, cybersecurity, and data analytics.

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Microservices, Apache Kafka, and Domain-Driven Design

Confluent

Microservices have a symbiotic relationship with domain-driven design (DDD)—a design approach where the business domain is carefully modeled in software and evolved over time, independently of the plumbing that makes the system work. In these projects, microservice architectures use Kafka as an event streaming platform. Microservices.

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Azure container Apps: The future of Microservices in Azure?

Xebia

Their focus was to build a solution that makes it easier for development teams to build Microservice architecture-based applications and deploy those to Azure. What are the features that development teams want when building and hosting microservices? Microservices using Dapr in Azure Container Apps.

Azure 130
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AI-Driven API and Microservice Architecture Design for Cloud

Dzone - DevOps

Incorporating AI into API and microservice architecture design for the Cloud can bring numerous benefits. Automated scaling : AI can monitor usage patterns and automatically scale microservices to meet varying demands, ensuring efficient resource utilization and cost-effectiveness.

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Rebuilding Netflix Video Processing Pipeline with Microservices

Netflix Tech

Moving away from the use of dedicated instances that were constrained in quantity, we tapped into Netflix’s internal trough created due to autoscaling microservices, leading to significant improvements in computation elasticity as well as resource utilization efficiency. This introductory blog focuses on an overview of our journey.

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This is the beginning of the unbundled database era

TechCrunch

Today, thanks to the cloud, microservices, distributed applications, global scale, real-time data and deep learning, new database architectures have emerged to solve for new performance requirements. We now have different systems for fast reads and fast writes.