Databricks, Snowflake, and the centralized lakehouse all ask the same thing first: move everything you have into one place, then start. Didati doesn't. Huckleberry virtualizes your data exactly where it already lives – so the work starts in days, your data never leaves its domain, and governance stops being someone's bottleneck.
Comparison
Bring the platform to your data.
Not your data to the platform.
Two opposing bets
on your data.
A centralized platform bets that the fastest path to value is to
consolidate – pull every source into one warehouse or lakehouse,
then build on top of the copy. It's a powerful model, and for a
greenfield team starting from nothing it can be the right one.
But most enterprises aren't greenfield. Their data is too fractured,
too sensitive, too large, or too distributed to migrate. For them,
"centralize first" means a multi-quarter program, a growing
governance liability, and a single team standing between the
business and its own answers. Didati makes a different bet:
leave the data where it is, and bring intelligence to it.
Your data never moves
No ETL, no copies, no replication lag. Huckleberry virtualizes data in place – your source systems stay untouched.
Days, not months
The first governed, agent-ready insight lands in under a day – not at the end of a centralization project.
Governance without a bottleneck
Element-level, zero-trust controls enforced at the source and owned by the domains that actually know the data.
Side by side
The same problem. Two architectures.
Where centralized data management and Didati diverge – row by row, from the very first move you have to make.
The Centralized Platform
Databricks · Snowflake · central lakehouse
Didati Huckleberry
Decentralized enablement in place
The first move
Centralize first. Copy every source into one lakehouse before a
single question gets answered.
Enable in place. Point Huckleberry at
sources where they already live – the computation travels to the
data.
Data movement
ETL/ELT pipelines, replication jobs, and constant sync. Copies
multiply, and the lake is stale the moment it lands.
Zero ETL. No copying, no replication
lag. Source systems are never touched – you query live data.
Time to value
Months of pipeline engineering and modeling before the business
sees a usable answer.
Days. Under a day to agentic insight;
under five to governed data products.
Governance
Centralized – one platform team owns access and becomes everyone's
bottleneck.
Decentralized, element-level.
Zero-trust controls enforced before any model touches data.
Where data lives
It has to land in the vendor's cloud. Sovereignty and air-gap
requirements fight the architecture.
It never leaves its domain. Run
managed, self-hosted, or fully air-gapped.
AI & agents
Models query a central copy that's already drifting away from the
real source of record.
Governed data products, queryable
live by agents and humans via MCP, GraphQL, and SQL.
Lock-in
Data and compute consolidate inside one vendor – the more you
load, the harder it is to leave.
A tamper-evident interchange network.
Your data stays yours, where it is, on an audit ledger.
You don't have to rip anything out.
Already invested in Databricks or Snowflake? Keep them. Huckleberry connects to a warehouse or lakehouse as just another source and virtualizes it into the mesh alongside everything else – turning today's central platform into one governed data product among many, instead of the place all your data is forced to live.
Connects to what you already run
Snowflake
Delta Lake
Iceberg
DuckLake
PostgreSQL
MySQL
AWS S3
Azure Blob Storage
Parquet
…and 100+ more
Centralizing isn't free.
It just moves the bill.
The copy is stale on arrival
By the time data is ingested, transformed, and modeled, the source has already moved on. You govern and analyze a snapshot, not reality.
The lake team is the bottleneck
Every new question becomes a pipeline ticket. The central team turns into a queue that the entire business has to wait behind.
Sensitive data never makes the trip
Regulated, sovereign, or air-gapped data often can't be centralized at all – so it stays dark, and your AI keeps guessing without it.
Platform with usage (to a point… )
That's it.
A flat platform fee covers your deployment and base usage. Additional usage is metered on top – AI queries, data product scans, and interchange transactions. Need more room? Add usage anytime, no contract changes required.
Standard
Core platform access for teams getting started with data products, agent enablement, and agentic analytics.
- Data Products (Headwaters)
- AI Agent (Doc)
- Chat & Projects
- Interchange Network (Wyatt)
- Document Intelligence
- Data Apps
- Zero-trust security
- Advanced security & SSO
- Custom usage
Included usage
Standard
Premium
Full platform access with the interchange network, document intelligence, and Data Apps unlocked.
- Data Products (Headwaters)
- AI Agent (Doc)
- Chat & Projects
- Interchange Network (Wyatt)
- Document Intelligence
- Data Apps (10 view-only users)
- Zero-trust security
- Advanced security & SSO
- Custom usage
Included usage
3× Standard
Enterprise
Full capabilities, advanced security, SSO, whitelabel, and air-gap deployment for highly regulated environments.
- Data Products (Headwaters)
- AI Agent (Doc)
- Chat & Projects
- Interchange Network (Wyatt)
- Document Intelligence
- Data Apps (unlimited view users)
- Zero-trust security
- Advanced security & SSO
- Custom usage
Included usage
Custom