AIStor Tables is the first on-prem and hybrid data store to support Iceberg REST Catalog views. Iceberg views are catalog-native, engine-agnostic virtual tables that simplify access, enable versioning, and work across Spark, Trino, and more without an external catalog.
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Apache Iceberg V3 boosts performance with deletion vectors that move delete costs to write time, row-level lineage for accurate incremental processing, variant types for efficient JSON queries, and native geospatial types with storage-level pruning. AIStor is the first to support V3.
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Technical catalogs track table state at the storage layer. Platform catalogs federate technical catalogs within an ecosystem. Business catalogs span the entire organization. Most organizations need all three layers working together.
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On-premises data should participate in cloud AI/ML workflows without copying it anywhere. Delta Sharing should be embedded directly into object storage, eliminating separate servers and infrastructure. Data stays put. Queries travel.
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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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Delta Sharing + MinIO AIStor enables direct access to on-prem data from Databricks without data duplication. Organizations can now query their hybrid infrastructure through a single interface, eliminating storage costs and synchronization complexity.
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Apache Iceberg 1.9.0 closes the gap with Delta Lake, adding row-level ops with lineage, JSON-friendly variant type, geospatial support, REST catalog upgrades, and easier Delta migration. Dropping Hadoop 2 and Spark 3.3 signals a modern focus, driving open table format convergence.
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Object storage is the primary storage solution for OLAP databases. This survey highlights major database players that have embraced this movement.
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Enterprises are moving AI and analytics workloads off public clouds to cut costs and regain control without sacrificing performance. Cloud repatriation brings cloud-native design on-prem.
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To support AI and analytics, a data lakehouse must be secure by design. This blog covers best practices for securing storage, metadata, and catalog layers including encryption, fine-grained IAM, audit logging, object locking, and multi-site replication without sacrificing performance.
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Relationships matter, especially in your data. Explore graph analytics without moving data using PuppyGraph, Apache Iceberg, and MinIO AIStor. Quickly set up a cloud-native graph analytics stack that uncovers hidden patterns directly from your data lakehouse.
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Legacy systems drain budgets, slow innovation, and block AI progress. This article shows how phased modernization cuts costs, boosts performance, and builds a future-ready data foundation without disruption. Inaction is the real risk.
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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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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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Want real-time analytics and blazing-fast performance? Learn how to build a high-speed, on-prem pipeline with Materialize and MinIO AIStor—faster than S3, high thoughput, and built for AI. Includes a full tutorial to get you up and running locally.
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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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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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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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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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