Data Lakehouse Examples in Action

Every technology shift begins with real-world examples. For architects and engineers building or planning a data lakehouse, understanding what's working today and what isn't is essential. The idea of a lakehouse, combining the scale, flexibility, and affordability of data lakes with the performance and manageability of data warehouses, is compelling. But abstract concepts alone aren't enough to guide critical architecture decisions.

This page provides detailed, practical examples of organizations currently deploying open-source lakehouse stacks, including Apache Iceberg, Apache Hudi, StarRocks, Dremio, and more, to tackle specific, high-stakes data challenges. Many of these examples are stories of Hadoop migration, detailing extremely impactful performance, cost utilization benefits for a migration to a data lakehouse.

Whether your priority is achieving sub-second analytics at petabyte scale, enabling real-time ingestion without compromising governance, or significantly cutting storage costs, these case studies will show you what's possible and how to get there.

Data Lakehouse Examples

Data Lakehouse Examples

Building on Prem

Organizations are choosing on-premises data lakehouse architectures for their economics, performance at scale, and security. From a cost perspective, on-prem storage offers a predictable model: there are no egress charges or per-request API fees on prem. This means no surprise bills and a total cost of ownership that improves as capacity scales.

MinIO AIStor is the de facto on-prem object storage software; with over 2 billion Docker pulls worldwide and growing. If you’re looking for examples of where other enterprises have built their data lakehouses, AIStor has a proven track record of being the foundational storage layer for billions of users.

Performance-wise, AIStor has demonstrated extreme throughput: yielding faster time-to-first-byte and query responsiveness – a single-digit millisecond TTFB is achievable, compared to ~30 ms typical on AWS S3 Standard. Notably, AWS itself introduced S3 Express One Zone (a high-performance AWS S3 tier) claiming 10× lower latency and higher I/O than standard S3, but at 8× the cost per GB over their standard S3 API. AIStor provides that cloud-grade performance without punitive pricing or trade-offs, all while keeping data on infrastructure you control. Security and governance are stronger on-prem as well: AIStor gives enterprises data sovereignty, with full control over data placement and fine-grained access policies down to individual objects. In short, an AIStor-backed lakehouse offers cloud-like scalability and throughput with predictable economics and enhanced control. A compelling combination for large-scale analytics and AI workloads.

Data Lakehouse Examples

Building on AIStor

Compared to other on-prem object storage vendors, AIStor’s architecture is purpose-built to avoid common scale bottlenecks. Other S3-compatible systems often rely on separate metadata services or databases that become choke points in which every small-file operation incurs a metadata lookup, doubling latency and straining throughput. AIStor eliminates that entire layer: it does not use an external metadata database, instead storing metadata with the objects on disk via consistent hashing. This means no single metadata node to saturate or fail allowing AIStor to handle millions of object operations remains fast and strictly consistent even at petabyte scale.

The result is a simpler, horizontally scalable design with fewer moving parts (no dedicated index servers or name nodes). That simplicity translates to better operational stability and easier integration.

Data Lakehouse Examples

Data Lakehouse is Enterprise Architecture

Together, these real-world outcomes indicate that the open data lakehouse paradigm is on track to become the enterprise standard for modern data platforms. Forward-looking data teams increasingly favor this architecture for its high performance, lower costs, and freedom from vendor lock-in. A combination that traditional warehouses and proprietary stacks struggle to match. Please reach out to us at hello@min.io or on our Slack channel if you have any questions.