InfoQ Homepage Architecture Content on InfoQ
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Why AWS Lambda Pricing Has to Change for the Enterprise
AWS Lambda users pay only when their code is run. This can result in massive cost savings over long-running workloads. The advantages start to disappear quickly when AWS Lambda is employed for batch processing. Enterprises can gain significantly from the scalability of FaaS - yet a price comparison between EC2, Lambda, and Fargate, the AWS-managed container service, reveals an ugly truth.
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DevOps is Not Enough for Scaling and Evolving Tech-Driven Organizations: a Q&A with Eduardo da Silva
Eduardo Silva from bol.com on the need for sociotechnical systems thinking. DevOps is a good starting point but a wider view of the organization as a sociotechnical system is key for sustained growth.
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Strategies to Modernize, Maintain, and Future-Proof Systems
The book Kill it with Fire by Marianne Bellotti provides strategies that organizations can use to modernize, maintain, and future-proof their systems. She suggests choosing strategies based on the organizational context, and defining what value you’re hoping to see from modernization.
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The Evolution of Distributed Systems on Kubernetes
At QCon in March, Bilgin Ibryam, product manager at Red Hat, gave a talk on the evolution of distributed systems with Kubernetes. You might have an answer to that, and Ibryam has one too. At the end of the article, you will find out what he thinks the answer will be.
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GraphQL Reference Guide: Building Flexible and Understandable APIs
This online guide aims to answer pertinent questions for software architects and tech leaders, such as: Why would you use GraphQL? Why should you pay attention to GraphQL now? How can GraphQL help with data modelling in the Enterprise?
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Testing Quarkus Web Applications: Reactive Messaging, Kafka, and Testcontainers
Quarkus is a full-stack, Kubernetes-native Java framework that supports many coding styles, including reactive programming. Writing clean unit/component/integration tests for Quarkus applications when a reactive approach is used is vitally important. Here we demonstrate testing reactive code, reactive messaging, and full integration testing.
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Cut Your Design Sprints in Half with These Key Tips
Cut your next design sprint in half with these tips at your side. With this approach, you’ll be able to turn 2.5 days into ~4 hours and the whole sprint to 2.5 days. Make collaborative design thinking easier, more fun, and exciting. With up-front preparation, a clear challenge to tackle, and attention to the clock, you can get to the essentials and turn innovative ideas into testable prototypes.
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Culture & Methods Trends Report March 2021
The most significant impact on culture and methods in 2021 is the disruption caused by COVID-19. We look at what's needed for good remote and the impact of bad remote, how management practices are evolving, and the importance of people skills for technologists. Paying attention to ethical issues, diversity and inclusion, tech for good, employee experience and psychological safety are important.
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Serverless Functions for Microservices? Probably Yes, But Stay Flexible to Change
When designing cloud-native systems, it is important to accommodate freedom to change deployment strategy, from FaaS to containers or VMs, for potentially significant savings on cloud bills.
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Blockchain Node Providers and How They Work
In this article, we will review the concept of a blockchain node, the problems a developer might face while deploying a node, and the working principle of Blockchain-as-a-Service providers, which simplify the integration of the blockchain into products, maintaining wallets, or keeping the blockchain in sync.
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Saga Orchestration for Microservices Using the Outbox Pattern
The outbox pattern, implemented via change data capture, is a proven approach for addressing the concern of data exchange between microservices. The saga pattern, as demonstrated in this article, is useful for data updates that span multiple microservices.
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The Future of Data Engineering
Chris Riccomini examines the current and future states of the art in data pipelines, data streaming, and data warehousing. He presents a six-stage evolution that data ecosystems follow, from a simple monolith to a complex data-microwarehouse architecture as the data engineers who manage them solve problems and clarify their roles as infrastructure engineers, rather than data stewards.
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