Data Engineering - MinIO Blog

A Global Telecommunications Leader and MinIO AIStor: Powering the Next Generation of Data Lakehouse for Analytics and AI

David Koppe David Koppe on Case Study | 31 October 2025

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,

From Data Swamps to Reliable Data Systems: How Iceberg Brought 40 Years of Database Wisdom to Data Lakes

Karthic Rao

Karthic Rao @hackintoshrao on Apache Iceberg | 25 July 2025

From 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.

Building Real-time Data Pipelines with MinIO's AIStor

Samson Koshy Samson Koshy on Data Engineering | 23 May 2025

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.

Are We All DataOps Engineers Now? If So, How Can We Become Great at It?

Brenna Buuck

Brenna Buuck on AI/ML | 24 January 2025

Are 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?