Apache XTable enables interoperability between Iceberg, Hudi, and Delta Lake by translating metadata, eliminating the need for data migration. This makes it easier to adopt new query engines, optimize performance, and build a more flexible data architecture.
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In several previous posts on MLOps tooling, I showed how many popular MLOps tools track metrics associated with model training experiments. I also showed how they use MinIO to store the unstructured data that is a part of the model training pipeline. However, a good MLOps tool should do more than manage your experiments, datasets, and models. It should be
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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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Choosing the right open table format—Apache Iceberg, Delta Lake, or Apache Hudi—can make or break your data lakehouse. This guide breaks down their strengths, how they integrate with object storage, and which one is best for AI, analytics, and real-time workloads.
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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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This post first appeared on The New Stack on January 16th, 2025. Often, while accessing the legitimacy of a new technology receiving a lot of hype, studying existing core capabilities and history is helpful. If the new technology in question is not based on existing or imminent capabilities, we can label it as “hype” and move on. Another litmus test
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2025 has inherited a slew of geopolitical concerns that started years ago. U.S. Foreign policy, U.S. - China Relations, China’s geopolitical maneuvers, Conflicts in the Middle East, Russian Ukraine war, and cybersecurity threats. Additionally, new leadership in the United States adds to the uncertainty created by these concerns. And, as if all this were not enough, the
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dbt’s acquisition of SDF Labs reinforces a powerful trend: the modern data stack is open. Learn why this matters for performance, interoperability, and future-proofing your data strategy.
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Running AIStor on OpenShift enables enterprises to achieve cloud-native elasticity on their hardware or cloud instance of choice, balance cost, capacity and performance.
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The evolution of data roles never stops—first, we were all data scientists, then data engineers, and now, DataOps engineers. But is DataOps really new, or just a fresh take on the same mission: delivering business value through data?
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Discover the power of Apache Iceberg and AIStor in transforming data lakehouses! From multi-engine compatibility to time travel, schema evolution, and blazing-fast performance, this guide dives deep into how Iceberg unlocks the full potential of modern AI and analytics workloads.
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In November of 2023, Amazon announced the S3 Connector for PyTorch. The Amazon S3 Connector for PyTorch provides implementations of PyTorch's dataset primitives (Datasets and DataLoaders) that are purpose-built for S3 object storage. It supports map-style datasets for random data access patterns and iterable-style datasets for streaming sequential data access patterns. The S3 Connector for PyTorch also includes
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Lately, there has been a trend in the industry to bring data “closer” to home. The result is that organizations now want to keep their data on servers that they own, in their own datacenter or at a colocation provider.
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Earlier this month, Amazon held their re:Invent conference in Las Vegas, Nevada, from December 1st to 5th - a 5-day event. If you have never been to a re:Invent conference, then the word that describes it best is “huge” - not just in terms of the number of attendees (60,000) but also the breadth of topics covered.
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2024 was a HUGE year for MinIO. Not only did we release AIStor, the most powerful version of MinIO to date, but we also attended 54 events, wrote 159 blogs, won over 10 awards and so much more. And all of it wouldn’t be possible without the support of our amazing MinIO community. So, as a shoutout to you
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In November of 2023 Amazon announced the S3 Connector for PyTorch. The Amazon S3 Connector for PyTorch provides implementations of PyTorch's dataset primitives (Datasets and DataLoaders) that are purpose-built for S3 object storage. It supports map-style datasets for random data access patterns and iterable-style datasets for streaming sequential data access patterns. In a previous post, I introduced the
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AWS recently unveiled Amazon S3 Tables, claiming to optimize Iceberg analytics on S3. Yet, these "special buckets" mainly fix AWS's own limits—like request caps—not universal object storage issues. With AIStor, you get unmatched performance, no vendor lock-in, and no extra costs for table maintance.
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Deep dive into AIStor mindset on how we do and recommend updates and restarts with AIStor.
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AIStor's Prompt API transforms healthcare data—analyze MRI scans, uncover trends in medical records, and accelerate research with natural language prompts. From automating image analysis to streamlining patient care, it empowers better outcomes for providers, researchers, and patients
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We’ll dive deep into the network configurations with which you can configure AIStor to the different replication strategies and network topologies that can be used to ensure your data is stored and accessed efficiently across multiple AIStor deployments.
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