Announcing General Availability of MinIO AIStor Tables
AIStor Tables: The first data store to build-in Apache Iceberg™ V3, unifying both tables and objects for analytics and AI at scale
Read moreData Engineering
A collection of 6 posts tagged with "Data Engineering"
Understanding the Difference Between Business Catalogs and Iceberg REST Catalogs
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.
Read moreA Global Telecommunications Leader and MinIO AIStor: Powering the Next Generation of Data Lakehouse for Analytics and AI
1. Executive Summary Our customer, a global telecommunications leader, established a Data Platform team to transform how data improved customer experiences and business operations. Faced with ballooning data growth and legacy storage constraints, they replaced aging legacy data storage systems with a high-performance, cloud-native data lakehouse, built on MinIO’s AIStor. The result: a scalable, cost-efficient foundation ready for AI,
Read moreFrom Data Swamps to Reliable Data Systems: How Iceberg Brought 40 Years of Database Wisdom to Data Lakes
The data lake was once heralded as the future, an infinitely scalable reservoir for all our raw data, promising to transform it into actionable insights. This was a logical progression from databases and data warehouses, each step driven by the increasing demand for scalability. Yet, in embracing the data lake's scale and flexibility, we overlooked a critical difference.
Read moreBuilding Real-time Data Pipelines with MinIO's AIStor
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.
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?
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