Xin Y.

Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv

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Redis 8 is GA and packed with new features, including new data types, faster commands, higher throughput, and faster replication. Learn more about our most performant and scalable version yet: https://lnkd.in/eFU5qPDs

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Tip: Scale large HNSW vector sets in Redis by splitting your dataset across multiple instances and querying them in parallel with multiplexing, enabling efficient parallel reads and writes that drastically improve performance and manage memory effectively. This approach leverages Redis's scalable vector support to handle hundreds of millions of vectors seamlessly. #Redis #HNSW #VectorSearch #ScalableAI #DataScience

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The most effective architectural changes are often subtractive - removing entire layers of cost and complexity. Behind the scenes, OpsHelm, Inc.'s streaming backbone was migrated from a costly MSK and unreliable NATS setup to a single diskless Kafka architecture. The results were immediate: - Annual spend dropped from >$50,000 to <$10,000. - Data loss incidents went from recurring to zero. - Manual scaling and cross-cloud networking fees were eliminated. Read the full story of their migration and 78% cost reduction: https://lnkd.in/dS6UK9-7 #disklesskafka #datastreaming #Aiven

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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They built. They shipped. They won. 🏆 After winning the MongoDB x Cerebral Valley Agentic Orchestration Hackathon, Oscar Hong and Javokhir Sh. of JustPrice talk about what’s next and why hackathons matter so much to their growth. 🔗 https://lnkd.in/eRgafZZK

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We’re continuing 30 𝐌𝐢𝐧𝐮𝐭𝐞𝐬 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 with Shih-min(Stan) Hsu in 2026! Three new blogs to deepen your understanding of stream processing: • Database Lookup Join for Stream Processing: Architecture, Implementation and Limitations: https://lnkd.in/gvHX_y2a • From Lambda to Kappa: The Evolution of Stream Processing Systems: https://lnkd.in/gwXkMBCT • Giving Stream Processing a “Memory”: The Power of Stateful Operations: https://lnkd.in/g5yfDBca Happy reading! #risingwave #streamprocessing #realtimedata

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“1 million rows per second.” In his opening keynote, our CEO Max Liu walked through what that really means in production: - TiDB processing 1M rows/sec - 10x higher throughput for cross-region replication - A data layer built to make multi-tenant architectures actually practical at scale If you’re thinking about SaaS, multi-region, or AI-heavy workloads, this is the kind of infrastructure ceiling you want. 🎥 Watch the full keynote from Max Liu at #TiDBSCaiLE to see what’s behind the numbers: https://ow.ly/BgKl50YetBq #TiDB #DistributedSQL #MultiTenancy #CloudNative #Scalability

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Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

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