AI-Ready Storage
Object storage and open table formats organized so analytics, retrieval, and training read from the same governed substrate.
AI workloads break when storage is optimized for one consumer (BI, archives, app database) and copied for everyone else. AI-ready storage is a lakehouse with Parquet on object storage under Iceberg, Delta, or Hudi — one substrate, many readers.
What it solves
Removes the per-consumer data copy: retrieval pipelines, BI dashboards, ML training, and operational agents all read the same tables. Numbers stop disagreeing because there is only one set of numbers.
How we build it
Choose the table format that fits the existing stack (Iceberg for broadest engine support, Delta on Databricks, Hudi for heavy streaming upserts). Migrate analytical tables first, then onboard retrieval indexes on top. Lifecycle policies, partition strategy, and compaction are sized to the workload, not copied from a template.
- Parquet on S3, GCS, or Azure Blob
- Iceberg, Delta, or Hudi table format
- Partition and compaction strategy per table
- Lifecycle and retention policies in code
What changes when it is in place
New AI use cases do not require new pipelines. Retrieval, evals, and training pull from governed tables instead of ad-hoc dumps. The platform owner can answer 'where does this number come from' with a table path and a lineage trail.