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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Adi Polak discusses patterns for effective data stream processing, highlighting common pitfalls and the complexities of balancing data infrastructure. Learn about exactly-once semantics, the challenges of join operations in streaming (including the "Puppies shelter" concept), and crucial error handling strategies.
By Adi PolakAccording to Lizzie Matusov, technical drivers like velocity offer an incomplete view of team performance. Social drivers—trust, autonomy, purpose, and psychological safety—provide a fuller picture and reveal important areas of opportunity for improvement. She spoke about the social drivers behind high-performing engineering teams at QCon San Francisco.
By Ben LindersMicrosoft's five digital commitments aim to bolster Europe's tech landscape and sovereignty through a 40% cloud and AI infrastructure expansion, enhanced cybersecurity, and a robust data privacy framework. By establishing a "European cloud for Europe," Microsoft reinforces its dedication to digital resilience while fostering economic competitiveness and supporting the open-source community.
By Steef-Jan WiggersUber has detailed a recent transition to running Ray-based machine learning workloads on Kubernetes. This marks an evolution in its infrastructure, with the aim of enhancing scalability, efficiency, and developer experience. The company recently published a two-part series from Uber Engineering delving into the motivations, challenges, and solutions encountered during this migration.
By Craig RisiThe Model Context Protocol (MCP), an open standard for LLM tool integration, is gaining adoption in Java (Quarkus, Spring AI, etc.). Developers can now run MCP servers more easily using tools like JBang and the MCP Java catalog.
By Shaaf Syed
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