Brenna Buuck

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.

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.

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.

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.

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.

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.

Object storage is the primary storage solution for OLAP databases. This survey highlights major database players that have embraced this movement.

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.

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.

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.

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.

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.

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.

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.

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.

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.

How does Exness handle massive data volumes and demanding AI/ML workloads? By moving to an on-prem infrastructure powered by MinIO. From scaling their data lake to managing traffic peaks of 200 Gbps, MinIO supports their AI workflows, disaster recovery, and more.

As AI workloads drive cloud costs through the roof, many companies are rethinking their approach. Moving select AI tasks back on-prem offers a path to predictable costs, improved performance, and stronger data control.

Enhance your AI workflows by combining MinIO’s scalable AIStor with Polars, a lightning-fast DataFrame library. Learn how this powerful duo accelerates data pipelines, handles massive datasets, and offers powerful performance and scale.

MinIO introduced its conditional write feature long before AWS S3’s recent announcement. This powerful tool offers greater control in high-concurrency environments, ensuring data consistency and reliability, especially in AI and ML workflows.

The rise of lakehouse functionality is reshaping data management. ParadeDB's pg_lakehouse extension lets PostgreSQL integrate with object storage, enabling scalable, secure analytics. This makes the modernization of data infrastructure possible without extensive overhauls. Welcome to the future!

Explore modern data architecture with Iceberg, Tabular, and MinIO. Learn to seamlessly integrate structured and unstructured data, optimize AI/ML workloads, and build a high-performance, cloud-native data lake.

Explore the future of AI in an open-source landscape, challenging Big Tech's masked efforts. Learn how embracing extreme open innovation fosters collaboration, drives market growth, and sets the stage for an open-source AI data stack.

Joust against data complexity with LanceDB, a lightning-fast vector database optimized for AI/ML on the open-source Lance format. Teaming up with MinIO, it scales seamlessly, offering high-performance, cloud-native storage. Dive into the tutorial for a swift deployment.