Data Lakes & Analytics

Until recently, data that stayed on-premises was data that Databricks couldn't reach. If your analytics and AI workloads ran in Databricks, and your most valuable data lived on-prem, you had two options: build and maintain a replication pipeline to copy data into the cloud, or accept that certain datasets simply wouldn't participate in your cloud analytics.Both options carry real costs. But a third option now exists: Databricks querying on-premises data directly, with no copies and no pipelines, through the open Delta Sharing protocol embedded natively in MinIO AIStor.

NA Automotive manufacturer drives the future of autonomous vehicles with AIStor

Orange Group subsidiary cut compliance query times from hours to minutes with AIStor, boosting productivity by 300%

Databricks needs your on-prem data. AIStor Table Sharing delivers it live — no replication, no pipelines.

Storage, not models or compute, is now AI's biggest bottleneck. The fix is an architectural one, not a bigger GPU.

Delta Sharing + AIStor enables zero-copy access to on-prem data from Databricks without duplication

Table Sharing connects Databricks directly to on-prem AIStor data via Delta Sharing without copying/syncing

Technical vs platform vs business catalogs—enterprises need all three layers working together for AI/analytics

Embedding Delta Sharing protocol into object storage for secure data sharing

Government AI readiness starts with unified data infrastructure—not algorithms—practical modernization guide

Global telco replaced legacy storage with AIStor lakehouse—scalable, cost-efficient foundation ready for AI insights

Making data discoverable with Databricks—Delta Sharing + AIStor enables direct access to on-prem data without duplication

MinIO is Hadoop HDFS's logical successor—faster, cloud-native, S3-compatible with better economics & simplicity

Databases optimized for object storage using S3 API for disaggregated compute-storage architecture

How Iceberg applies 40 years of database ACID principles to data lake architectures

Security best practices for data lakehouses supporting enterprise analytics and AI workloads

PuppyGraph engine queries Iceberg tables as graphs using Cypher and Gremlin without ETL

AI/ML datalake reference architecture—design high-performance infrastructure for training, inference & analytics at scale

Inline metadata and erasure coding solve small file performance challenges at scale

Delaying modernization costs more than migrating—60% of finance CTOs admit outdated tech blocks AI innovation

Real-time data pipeline with AIStor—stream processing architecture for low-latency analytics & operational intelligence

Iceberg Catalog API explained—atomic pointer manager coordinates metadata, snapshots & multi-engine table access

Comprehensive guide to lakehouse architecture combining Iceberg table format with AIStor for analytics

High-performance data preprocessing with Polars DataFrame library and MinIO integration