Data Lakes & Analytics

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.

Government agencies must modernize data to achieve AI readiness. Unified, secure, and actionable data enables real-time decisioning and creates a foundation for mission-aligned AI.

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.

The "big data king" that enterprises spent billions on is dying. Not from lack of trying, but because cloud-native alternatives now beat it at its own game. AIStor is faster and cheaper than HDFS. The revolution is happening in quarters, not decades.

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

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.

Large numbers of small files present big challenges for application performance.

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.

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.

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.

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.

Pairing the Iceberg table format with AIStor creates a powerful, flexible and extensible lakehouse platform. The Iceberg Table Spec declares a table format that is designed to manage “a large, slow-changing collection” of files or objects stored in a distributed system.

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.

Take advantage of cloud native, Kubernetes-oriented, microservices-based architectures with object storage.

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!