Object Storage Optimized Databases: Trends & Industry Leaders
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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Cloud Repatriation: Why Enterprises Are Moving Workloads Off Hyperscalers
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.
Read moreData Lakehouse Security: Supporting Scalable Analytics and AI Workloads
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.
Read moreFrom Tables to Relationships: Visualizing Iceberg Data as a Graph
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.
Read moreInertia Is the Problem: Why Waiting to Modernize Costs More Than Migrating
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.
Read moreAIStor Adds Support for the S3 Express API
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.
Read moreThe Case for On-Prem Iceberg: Cost, Control, and Performance
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.
Read moreBuilding a High-Performance, On-Prem Data Pipeline with Materialize and MinIO AIStor
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.
Read moreThe True Cost of Cloud: How One Cybersecurity Company Found a Better Alternative with MinIO AIStor
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.
Read moreApache XTable: Advancing Data Interoperability in Data Lakehouses
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.
Read moreArchitect’s Guide to Open Table Formats and Object Storage
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.
Read moreWhy an Open Lakehouse Approach Matters: Lessons from dbt’s Acquisition of SDF Labs
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.
Read moreAre We All DataOps Engineers Now? If So, How Can We Become Great at It?
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?
Read moreThe Definitive Guide to Lakehouse Architecture with Iceberg and AIStor
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.
Read moreDemystifying Amazon S3 Tables: Why AIStor Makes Special Buckets Unnecessary
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.
Read moreTalk to Your Data: Transforming Healthcare with AI-Powered Object Storage
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
Read moreExness: Managing petabytes of trading data with MinIO
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.
Read moreHow an Educational Services Organization Modernized Its Infrastructure with MinIO
An educational services leader transformed its IT with MinIO, achieving 40% faster performance and 30% lower costs. Powered by Kubernetes, MinIO supports AI, ML, and scalable data lakes with active-active replication. Discover how they modernized for innovation and resilience.
Read moreRepatriating AI Workloads: An On-Prem Answer to Soaring Cloud Costs
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.
Read moreThe Architect’s Guide to Interoperability in the AI Data Stack
Interoperability is the key to building a flexible, future-ready AI data stack. As proprietary systems lock down innovation and drive up costs, open tools like S3-compatible storage and multi-format table systems offer the freedom to scale and adapt.
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