Databricks and MinIO AIStor

WEBINAR

From On-Prem to Insight: Secure, Zero-Copy Analytics with Databricks and MinIO AIStor

Date: Thursday, April 30th

‍Time: 10:00 AM PST / 1:00 PM EST

Join MinIO and Databricks for a live demo and technical walkthrough. This webinar is for you if:

You run Databricks but have critical data that can't move to the cloud
You're evaluating hybrid analytics strategies and need to avoid complex ETL
You want to increase the ROI of your Databricks investment by extending it to on-premises data without new pipelines or infrastructure

REGISTER NOW

AIStor Fuels the Databricks Data Intelligence Platform

Enterprises running Databricks in the cloud need their most valuable on-premises data to be instantly accessible, at the right performance, cost, and governance level. AIStor is the unified data foundation that makes this possible, accelerating analytics, enterprise RAG, and real-time AI inference across hybrid environments.

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Zero-Copy Hybrid Analytics

AIStor embeds Delta Sharing natively at the storage layer, giving Databricks direct, live query access to Delta and Apache Iceberg™ tables stored on-premises. No data pipelines, no synchronization, no stale snapshots.

Native Delta Sharing built into object storage with no standalone sharing servers

Live query access: only query results move, not the data

Works with both Delta Lake and Apache Iceberg™ V3 tables

Sovereign Data Infrastructure

Deploy AIStor in your data center with no external cloud dependencies. Your data stays where compliance demands it, while Databricks runs at full speed.

No egress to public cloud — full data residency and sovereignty on-premises

No cloud lock-in for data that must remain on-premises

Enforces read-only access at the storage layer

Simplified Hybrid Architecture

AIStor replaces fragmented multi-tier architectures and standalone sharing servers with a single, high-performance data store that feeds every stage of the AI and analytics pipeline.

No proprietary gateways or reverse proxies

No separate governance plane to manage

One source of truth across hybrid environments

Storage That Keeps Pace With Databricks Compute

Databricks workloads are only as fast as the storage behind them. AIStor delivers object data at wire speed — ensuring on-premises data is never the bottleneck in analytics, training, or inference pipelines.

Designed for the throughput demands of distributed Databricks compute

Scales linearly from terabytes to exabytes without re-architecting

Purpose-built Iceberg V3 tables eliminate the latency of external metadata services

How AIStor + Databricks Works

AIStor deploys as the unified storage layer in hybrid environments, replacing fragmented multi-tier architectures with a single high-performance data store that feeds every stage of the Databricks pipeline.

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Native Delta Sharing at the Storage Layer

AIStor embeds the Delta Sharing protocol directly into the object store — industry-first sharing of on-premises tables with cloud Databricks instances.

No standalone Delta Sharing server required

No reverse proxies or sidecar services

Shares update automatically as tables evolve

Storage at Wire Speed

AIStor delivers object data at the speed Databricks compute demands, ensuring queries are never bottlenecked by storage.

On-premises data is processed directly by Databricks compute

Eliminates bottlenecks in the data pathScales to match

Databricks workload throughput

Built on AIStor Tables — Full Iceberg V3

AIStor is the only data store with built-in, full Iceberg V3 tables, making it the ideal on-premises lakehouse foundation for Databricks.

Both tables and objects as first-class citizens

Guaranteed data correctness with atomic, multi-table transactions

Unified visibility across all AI data, structured and unstructured

Seamless ecosystem fit: works out-of-the-box with Databricks Unity Catalog

Architected for Enterprise On-Premises Deployment

AIStor's minimal footprint makes it purpose-built for on-premises and hybrid environments.

Deploys on industry-standard hardware

No external metadata databases or background services

Linear scaling from terabytes to exabytes

Built for Real-World Databricks Workloads

Analytics & AI Training

Large language model pre-training

Foundation model fine-tuningMulti-modal training pipelines

Distributed training data loading

AI Training & Inference, Without the Pipelines

Live access to on-premises training datasets from Databricks

No replication or staging before model training begins

Model checkpoint and artifact storage on-premises

Experiment tracking without data leaving the source

Data Preparation

Dataset curation and preprocessing

Feature engineering pipelines

Model checkpoint storage

Experiment tracking and artifacts

AIStor: On-Premises Data Foundation for Databricks