Aliaksei Belablotski

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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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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The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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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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Build an entire retrieval pipeline piece by piece. Ida Silfverskiöld's newest article includes a detailed walkthrough, covering query optimization, result fusion, re-ranking, and context building.

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Data engineers can use Agent Bricks AI functions to modernize their data engineering practice by automating data extraction, summarization, and consolidation.

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Palantir Technologies recently released a new Foundry update that lets you build directly on your source tables, removing the need for manual syncing. As Ontologize’s Ben Thomas explains, most teams used to have to move data into Foundry before they could start modeling. With this update, you can create ontology object types backed by virtual or iceberg tables, so your data stays where it already lives, whether that’s in Snowflake, S3, or another external system. It’s a small change that removes hours of setup and makes it easier to model directly on your source systems. Try it out, and let us know what you think!  

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As Databricks adoption accelerates, platform leaders face mounting pressure to deliver reliable performance, predictable costs, and scalable data operations. But with a fragmented vendor landscape and no one-size-fits-all approach, selecting the right observability solution can feel overwhelming. That's why we created the Databricks Data Observability Buyer's Guide - a clear, actionable framework to help you cut through the noise. 📥 Download your free copy today: https://lnkd.in/gayN9ui9 #Databricks #DataObservability #FinOps #AIOptimization #CloudCosts

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