A collection of 51 posts tagged with "AIStor"
As a MinIO Curriculum Engineer, I spend a lot of time creating demos and learning environments for customers. These environments need to be realistic, comprehensive, and ready to showcase MinIO AIStor's capabilities in real-world scenarios. But here's the thing - setting up a proper demo environment manually can take hours of low-value, repetitive work. Let me
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1. Executive Summary Our customer, a global telecommunications leader, established a Data Platform team to transform how data improved customer experiences and business operations. Faced with ballooning data growth and legacy storage constraints, they replaced aging legacy data storage systems with a high-performance, cloud-native data lakehouse, built on MinIO’s AIStor. The result: a scalable, cost-efficient foundation ready for AI,
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Government agencies must embrace AI responsibly, ensuring data sovereignty and security are top priority. To obtain sovereign AI, the right foundation must be in place to ensure infrastructure, data, and models remain fully controlled across agencies.
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AIStor Tables brings Iceberg catalogs natively into on-prem object storage. It simplifies data organization, enforces table-aware security, and lets AI teams catalog unstructured assets in structured tables, thereby enabling discovery.
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The format war is over, and Iceberg won. Every major engine now supports it, from Snowflake to Spark. Built for object stores, Iceberg delivers scale, consistency, and simplicity. It is the unified foundation for enterprise AI and analytics. Future-proof your data with Iceberg.
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Liberating data enables government agencies to modernize storage, cut hidden costs, and unlock mission-driven insights. By strategically curating, archiving, and aligning data with AI-driven priorities, agencies improve efficiency, compliance, and decision-making while preparing for the future.
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AIStor S3 Express is a high-performance object storage API designed for demanding data lakehouse workloads. Benchmarks show it outperforming AWS S3 Express on LIST operations and large object GETs.
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Built a portable Java data pipeline using MinIO's AIStor and Kafka that scales from Mac to Kubernetes. Containerized stack (Kafka, AIStor, Prometheus, Grafana) processes millions of events, preserving raw data for analysis while delivering real-time dashboard summaries.
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In data engineering, open standards are foundational for building interoperable, evolvable, and non-proprietary systems. Apache Iceberg, an open table format, is a prime example. Along with compute, Iceberg brings structure and reliability to data lakes. When coupled with high-performance object storage like MinIO AIStor, Iceberg unlocks new avenues for creating next-generation, high-performance, cost-effective, and scalable architectures. However, this powerful table
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In the previous blog posts of this series, we discussed the user-level and admin-level functions of the Model Context Protocol (MCP) server for MinIO AIStor. In the first blog, we learned how to review the bucket’s contents, analyze objects, and tag them for future processing. In the second blog, we also learned how to use admin commands and get
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Cloud lakehouses break the bank at scale and compromise control. On-prem Iceberg lakehouses deliver speed, savings, and sovereignty. From cancer research to finance, real-world deployments prove it: petabyte-scale performance, full control, and lower TCO are within reach.
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In the previous blog of this series, we discussed the basic user-level functions of the Model Context Protocol (MCP) server for MinIO AIStor. We learned how to review a bucket’s contents, analyze objects, and tag them for future processing using human-language commands and simply chatting with the cluster via an LLM such as Anthropic Claude. In this blog, we’
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GenAI is entering the agentic phase, with software agents collaborating with humans and other agents to reason and achieve complex goals. Agents are already demonstrating incredible intelligence and are very helpful with question answering, but as with humans, they need the ability to discover and access software applications and other services to actually perform useful work. The creators of such
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Tl;dr: We train a small LLM to become good at reasoning with reinforcement learning (similar to the process that led to Deepseek R1) all against AIStor AIHub, an on-premises model repository. Based on the great GRPO demo by will brown. Motivation: A growing requirement for teams is the need for an organized, secure, "single source of truth"
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Building upon AIStor's robust AI capabilities, MinIO's PromptObject has been enabling users to interact with their data through natural language queries as described here. PromptObject transforms how users interact with stored objects by allowing them to ask questions about their data's content and extract information using natural language—eliminating the need to write complex
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Modern enterprises seeking to leverage AI capabilities often face a significant hurdle: the complex deployment and management of GPU infrastructure in their Kubernetes environments. MinIO's AIStor addresses this challenge head-on by integrating the NVIDIA GPU Operator, revolutionizing how organizations deploy and manage GPU resources for AI workloads. Through automated GPU setup, driver management, and resource optimization, this integration
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MinLZ is a compression algorithm developed by MinIO. The main goal is to provide a format that offers the best-in-class compression while providing very fast decompression even with modest hardware.
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A cybersecurity firm faced soaring cloud costs and performance bottlenecks with AWS S3 as their log data grew to a multi-exabyte scale. They adopted MinIO AIStor for high-performance, S3-compatible object storage, cutting costs and boosting efficiency.
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In the rapidly-evolving world of artificial intelligence (AI) and machine learning (ML), speed and scalability are paramount. The ability to process massive amounts of data in real-time is a critical requirement for organizations looking to leverage AI/ML for competitive advantage. Whether it's training large machine learning models, running complex inference tasks, or scaling data pipelines, the performance
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In this post we look at how search, and specifically OpenSearch can help us identify patterns or see trends in our ever growing data.
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