The intelligence layer for your entire lakehouse stack

Your lakehouse isn't one tool. Neither is your intelligence layer.

NaviLake correlates signals across every layer of your stack — storage format, warehouse, transformation, compute — to find root causes no single-engine tool can see, and quantify exactly what they're costing you. Live today: BigQuery + dbt. Next: Iceberg + Spark.

No migration. No agent required. Read-only access — we never touch your data files.

critical$847/month

analytics.events.user_sessions — unused 107 days

Warehouse×Transformation2 layers correlated

Root Cause

dbt model disabled Feb 12 → table never cleaned up
Confidence
95%

One graph. Every layer connected.

Each connector you add compounds the intelligence. Four layers, one graph — starting with where the pain is sharpest.

Open table formats

  • Apache Iceberg — coming soon

Warehouse intelligence

  • BigQuery

Correlation graph

Compounds with every connector added

Transformation

  • dbt

Compute

  • Spark — coming soon
Live today
Coming soon

Every tool in your stack only sees its own layer.

Your warehouse tool knows what's expensive. Your catalog knows what exists. None of them talk to each other — so the engineer becomes the integration layer, manually stitching root causes together over and over.

× Single-layer tools

  • See cost, but not why
  • See file health, but not who's writing bad files
  • See lineage, but not whether it's actually used
  • 2–4 hours of manual stitching per incident

NaviLake

  • Sees cost and the upstream cause
  • Traces file health back to the model that caused it
  • Cross-references lineage with real usage
  • Correlated and ranked automatically

Live today

Correlated together — findings from one layer enrich the other.

BigQuery
Usage, cost, and query history from your billing export.
dbt
Model-to-table mapping with confidence scoring, lineage, and run cost attribution.

Coming next

Each new connector compounds the intelligence of every connector before it.

Iceberg
Storage health — files, snapshots, partitions, delete ratios. Root cause attribution for file bloat.
Spark
Compute intelligence — job performance, shuffle analysis, and table-to-job correlation.

The insight isn't in any one layer. It's in between them.

dbt doesn't know about downstream query patterns. BigQuery doesn't know about upstream transformation logic. Each tool is confident about its own view — but the real problem lives at the boundary.

NaviLake correlates across those boundaries to catch what slips between the cracks.

critical$2,400/month

warehouse.sales.order_events

Unused for 107 days — dbt model disabled, table never cleaned up

transformation×warehouse2 layers correlated

Transformation layer

dbt model was disabled 3 months ago — table orphaned

Warehouse layer

BigQuery still running scheduled queries against the table — all returning stale data

Root Cause

dbt model disabled → table orphaned → scheduled queries still running against stale data. Two tools, neither flagged the waste.
Confidence
87%

Your team already knows the data platform is slow and expensive.
Now you can prove it — and prove it's fixed.

$10K–$50K

Typical monthly waste found in unused tables, orphaned pipelines, and storage bloat in a team's first session.

2–4 hours

Time saved per incident — root cause surfaced instantly instead of manually stitched together across four tools.

< 10 mins

Time from connecting your warehouse to your first dollar-quantified finding.

Every dollar NaviLake finds comes with a confidence score and the exact evidence behind it — so when your team brings you a recommendation, you're looking at the same data they are.

This isn't a tool that adds to your stack of dashboards nobody checks. It's the layer that explains why the other dashboards say what they say.

From connect to root cause in under 10 minutes.

1

Connect your warehouse

Start with BigQuery — read-only, one-click OAuth.

2

Add dbt

Upload manifest.json — unlocks model-to-table correlation.

3

(Optional) Add more layers

Connect Iceberg, Spark, and more — each new layer compounds the intelligence.

4

Get correlated insight

Recommendations ranked by savings, confidence, and cross-layer evidence.

We will never corrupt your tables. That's the whole point.

Metadata-only

We read schema and statistics — never your actual data files.

Dry run, always

See exactly what will happen before anything runs.

Reversible by design

Every safe action comes with a rollback path. No surprises.

Not a cost dashboard. Not a catalog.

We didn't start by picking tools. We started by picking a problem.

“Every data team we've been part of had the same failure mode: five excellent tools, each confident about its own layer, none of them agreeing on the full picture. We didn't want to build another excellent single-layer tool. We wanted to build the layer that sits above all of them — one that gets more valuable every time you connect something new to it, not less.

BigQuery, Iceberg, and dbt are where we started because that's where the pain is sharpest today. They won't be where we stop.”

Currently in private development.

Join the waitlist for early access.

We're onboarding design partners now. Early partners get hands-on support and help shape the product roadmap.

No credit card. No migration. No risk to your data.