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AI DATA PIPELINES
Rubrik Annapurna: The AI Data Pipeline for Your Dark Data
No copying the whole estate to use a sliver of it. Rubrik Annapurna indexes billions of files where they already live, publishes a connected catalog your Data Intelligence platform can query, and stages only the subset your pipeline actually pulls.
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WHAT IS AN AI DATA PIPELINE
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Every data architecture has asked you to copy everything to understand anything.
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An AI data pipeline moves data from its source system into a model, an agent, or a retrieval index: discover, prepare, index, deliver, monitor. Hadoop, the warehouse, the data lake, and the data lakehouse all asked you to copy everything in before you could see it. Rubrik Annapurna inverts that order: it scans and indexes files where they already live, publishes a connected catalog, and stages only what a query actually matches.
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Confidence gap
14%
of organizations are very confident their data is AI-ready.
Source:
Gartner, G00793937, p.2 (2025)
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Build-vs-buy cost
300%+
projected cost to build your own readiness capability.
Source:
Gartner, G00824467, p.2, a Strategic Planning Assumption
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REACHES AI
< 1%
of enterprise data reaches AI.
Source:
What’s Your Data Strategy? (HBR, 2017)
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WHY ANNAPURNA
Three pains. One broken operating model.
Your AI project can fail before the first run, or after the experiment. Annapurna is built around the three places it breaks.
Incomplete Corpus
"Did the right data make it in?"
You copy what's easiest to move, so most of the estate is never cataloged, and a partial corpus produces confident answers from incomplete evidence. Annapurna scans and catalogs the full estate in place, then stages only what's matched.
Time to Data
"How long before work can start?"
Request, approve, copy, rebuild access, then start work, repeated per pipeline and per provider. With Annapurna you ask for the data and receive the matched subset. No re-request, no rebuild.
Lost Provenance
"Is your dataset still current?"
The source keeps changing after you copy it, so the training set can't explain itself once the source moves on. Lineage, versioning, and a file-level fingerprint travel with the staged dataset.
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RUBRIK ANNAPURNA CAPABILITIES
One layer between your unstructured estate and your AI data pipeline.
DISCOVERY
See the whole estate before you move a file
Automated Data Discovery connects to each storage system through its native protocols and APIs, then reads each file's metadata and ACLs without moving it. Connected Catalog for AI publishes that extracted file metadata as a connected catalog your Data Intelligence platform can query directly. You get the map first and the decisions second.
- Automated Data Discovery
- Connected Catalog for AI
- Connects to Snowflake, Databricks, AWS Sagemaker, Microsoft OneLake, and any other catalog provider.
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STAGING
Stage only what AI pulls
Deterministic Data Staging writes only the matched subset as AI-ready files, directly into your lakehouse. Annapurna is delivered as a service, with lightweight data movers deployed near your unstructured data: no appliance, no agent-per-file footprint. Spend tracks consumption, not estate size.
- Deterministic Data Staging
- Software-only. No new hardware.
- 10PB indexed, 1PB staged is realistic
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PERMISSIONS AND PROOF
Keep permissions and proof attached
Source access controls are captured and surfaced in the catalog, so your platform can enforce the rules that already exist. Verifiable Chain of Custody carries lineage and versioning with each staged dataset from the source file forward, with a verifiable fingerprint of what a file looked like when scanned.
- Source Permissions Carried Forward
- Verifiable Chain of Custody
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How the Annapurna AI Data Pipeline Works
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One scan, three layers, an AI-ready dataset.
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Scan
Reads file metadata and ACLs in place through native protocols. No file content is copied at this step.
Catalog & Carry Permissions
Metadata lands in a connected catalog. Source permissions and lineage travel with it.
Stage & Prove
The matched subset is written as an AI-ready dataset in your lakehouse, with lineage and a fingerprint attached.
One platform, many consumers
Agentic AI (one governed source), Analytics & BI (one catalog), Research & Data Science (corpus in an afternoon), Fraud & Risk (fuller evidence), Data Ops / IT (track drift).
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RUBRIK ANNAPURNA CUSTOMER STORIES
A financial services CTO on mapping, governing, and indexing the estate for AI without another ETL stack
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“In financial services, managing petabytes of highly distributed, regulated, and siloed unstructured data across legacy and modern platforms was operationally limiting. Rubrik Annapurna provides an automated approach to map, govern, and index our estate for AI initiatives, reducing the friction of cross-functional configurations and data sovereignty requirements without needing another ETL stack or compromising our compliance posture."
Corey West
Chief Technology Officer, Piper Sandler
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Rubrik Annapurna Use Cases
What teams build on an AI-ready unstructured data pipeline.
Start from the question your AI project needs answered. Annapurna finds the files, stages the subset, and keeps the proof.
Feed a RAG or vector pipeline without a migration. Current files, no bulk copy first.
Re-run a training corpus you can defend. A point-in-time record, not a reconstruction.
Find the data you forgot you had. A map first, decisions second. Then ask the estate a plain-language question: answers from the estate, not a spreadsheet.
Keep permissions from disappearing in transit. Governance context preserved. Cut the cost of an AI initiative before it starts: spend tracks consumption, not estate size.
| Criteria | Legacy pipelines | |
| ETL pipelines | Legacy pipelines ask you to copy everything before you can see anything | Annapurna indexes in place and stages only what a query matches. |
| More copies vs. index in place | The legacy model adds storage, egress, and operations | Annapurna reads metadata where files live and stages only the matched subset. |
| More silos vs. one connected catalog | A different answer in every platform | One catalog your Data Intelligence platform can query |
| Less clarity vs. see the estate first | You can't see the estate until it is copied | Scan and catalog the full estate before anything moves. |
| Infrastructure required | Multiple file copies to multiple storage targets to stage and pipe data | A single vaulted system of record |
RESOURCES & EXPERTISE
Learn more from our experts
Solution Brief
2 pages
Rubrik Annapurna at a Glance
How demand-driven, scan-at-source delivery turns unstructured data into AI-ready datasets in days.
Datasheet
2 pages
Rubrik Annapurna
Learn more about Rubrik Annapurna, the AI-ready unstructured data layer for enterprise Data Intelligence platforms.
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Infographic
1 page
The Journey of a File
See how Annapurna takes a file from silo to AI-ready: mapped, cataloged, and staged only when your pipeline pulls it.
FAQ
Everything you need to know
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What is Rubrik Annapurna?
The AI-ready unstructured data layer for enterprise Data Intelligence platforms. It indexes data in place, publishes a connected catalog, and lets AI pipelines pull only the files they need, with verifiable lineage.
What is an AI data pipeline?
The path data takes from its source system into a model, agent, or retrieval index: discover, prepare, index, deliver, monitor. For unstructured data, the hard part is the first step.
Why doesn't my existing ETL pipeline already make my data AI-ready?
Legacy ETL breaks provenance by copying the whole estate before you know what matters and drops permissions in transit. Annapurna sits upstream, indexing in place and handing your pipeline only the AI-ready subset with source permissions intact.
How much data can Annapurna handle, and how fast?
Annapurna is designed to handle multi-petabyte, multi-billion files. A common pattern is scanning a 10PB estate and staging roughly 1PB. The first scan covers the full estate; every subsequent scan is incremental, meaning it only processes files that are new or changed since the last pass.
Does Annapurna require new hardware?
No. It's delivered as a service and runs as software: lightweight data movers near your existing storage. No appliance, no new infrastructure to buy.
Does my data actually move?
The estate doesn't. When a query matches files, Annapurna stages that subset and mounts it to your lakehouse. You move what AI pulls, not the estate.
Does Annapurna enforce access controls?
No. It captures source-system access controls and surfaces them in the catalog. Enforcement stays with your Data Intelligence platform or upstream applications.