Tanzir M.

If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

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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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AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

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If your RAG is underperforming, the model you’re working with might not be the problem. It might be your chunking strategy. Effective chunking keeps LLMs accurate, efficient, and relevant by segmenting data into semantically meaningful, retrieval-friendly pieces. But scaling RAG requires more than naive splitting. Learn more from Redis AI Product Marketing Manager, Rini Vasan, here: https://bit.ly/46oeSRB

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Explore Weaviate, the open-source, AI-native vector database powering next-gen multi-agent systems with hybrid search, multimodal support, and blazing-fast performance. Trusted by Snowflake and backed by 25K+ GitHub stars, Weaviate is built for scale, memory, and flexibility in enterprise AI use cases. Whether you're self-hosting or using Weaviate Cloud, it's a robust solution for intelligent data infrastructure. #Weaviate #VectorDatabase #AIInfrastructure #SemanticSearch #OpenSourceAI #MultiAgentSystems #EnterpriseAI #VectorSearch #ScalableAI #DataInfrastructure

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Tuning Spark, Ray, or Dask for ML isn’t one-size-fits-all. In a new Domino Blueprint, Principal Solution Architect Sameer Wadkar shows how API-driven scaling complements autoscaling to right-size clusters on demand—cutting idle spend and speeding bursts for GPU/I/O-heavy work. Practical steps, a lightweight Python client, and Helm install included. If you care about FinOps and MLOps, this is for you. Read the blueprint: https://lnkd.in/ga_qJkhC

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Multiple databases. One platform. MySQL. MongoDB. Redis. Elasticsearch. Your data stack isn’t the problem, managing consistency, migrations, and performance across all of them is. Every schema change feels risky. Every migration invites downtime. Every integration adds another layer of complexity. COCREATE brings order to your data chaos: - Standardized integration templates for MySQL, MongoDB, Redis & Elasticsearch - Built-in migration tools embedded directly in integration flows - Real-time monitoring for latency, throughput & performance Before you go live, run migrations safely in COCREATE’s sandbox environments. Validate schema consistency. Measure performance. Roll out with zero surprises. Your data deserves smarter integrations, not riskier ones. Build, migrate, and optimize your databases seamlessly with COCREATE. Explore more at https://lnkd.in/d4yXMwxh #DataEngineering #DevOps #MySQL #MongoDB #Redis #Elasticsearch #DataMigration #DatabaseManagement

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Reveal 1.8.0 introduces two major upgrades that make building embedded analytics faster, smarter, and more scalable: Conditional Formatting and the Redis Cache Provider. These updates help developers, data teams, and enterprise users deliver clearer insights and higher performance with less complexity. Whether you are enhancing dashboards with color logic or optimizing query speeds for high traffic applications, Reveal continues to set the standard for customizable embedded BI that simply works: https://lnkd.in/dh3SgXp8

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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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What happens when you pair LangGraph’s flexibility with Snowflake’s secure, scalable compute? You get a seamless path to production-grade LLM applications. Fabian Hernandez, part of our Infostrux team, walks you through how LangGraph agents can now be executed entirely inside Snowflake using its new LLM function framework. It’s an exciting step forward for anyone building: -LLM agents -Retrieval-augmented generation pipelines -Conversational workflows directly tied to enterprise data This article is a must-read for teams operationalizing AI where it matters most—in the cloud, with governance built in. Explore it here: 👉 https://hubs.ly/Q03ykwlj0 #Snowflake #LLMFunctions #LangGraph #AIWorkflows #GenAI #EnterpriseAI #Infostrux

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Engineering at Snowflake means solving problems no one has tackled before. For Principal Software Engineer Hyungtae Kim and the Cortex team, that meant designing GPU-powered infrastructure flexible enough to handle any AI workload, at scale. Get a behind-the-scenes look at how the foundation was built, what makes it different, and how it’s powering the next era of GenAI: https://bit.ly/4kKjGVI

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