Ingestion and connectors
180 maintained connectors for warehouses, event streams, billing and CRM. Schema drift is absorbed on the next sync instead of breaking the load.
Browse the directoryEducation Strategic Advisory Practice
Northlane runs on top of the warehouse you already own. Model the transforms, watch every table and prove where a number came from — without a second scheduler, an agent on a host or a copy of your data.
Running in production at
One control plane
Most teams arrive with an orchestrator, a testing framework and a catalogue that all disagree about what exists. Northlane reads the warehouse query history once and builds a single graph the three of them share — so a schema change, a failed run and a stale dashboard become one event with one owner instead of three tickets.
Platform pillars
Every pillar reads from the same lineage graph, so a change in one is visible in the other five before anyone opens a ticket.
180 maintained connectors for warehouses, event streams, billing and CRM. Schema drift is absorbed on the next sync instead of breaking the load.
Browse the directoryBuild transforms on the canvas or drop into SQL on any node. Every version is diffable and promoted between environments with one command.
See the pipeline builderTrace any figure on a dashboard back through every join and source column. Lineage is parsed from query history, so it stays correct without annotation.
How lineage is builtFreshness, volume and distribution baselines learned from 30 days of history, with the owner of the failing node paged first.
See the monitor typesRow and column rules written once and pushed down to Snowflake, BigQuery and Redshift, with a signed approval trail behind every grant.
Read the security briefEvery query, model and dashboard carries the compute it consumed, so the warehouse bill arrives with names attached to it.
See cost reportingEvery pillar ships on every plan. Volume, retention and support differ.
Inside the product
Pick a surface. Each panel shows the view your team would actually work in, and names the thing it replaces.
Every transform is a node carrying its inputs, its owner and its last run. Drop into SQL on any node without leaving the graph, and diff the change against last night's snapshot before it merges.
Freshness, volume and distribution checks run on every table Northlane can see. Baselines come from 30 days of history rather than a threshold somebody guessed at in a sprint two years ago.
Open any figure and walk it upstream: the model, the join, the source column, the job that wrote it. The same graph answers the auditor's question and the analyst's, so nobody maintains a second diagram.
How work moves
The same four stages run for every model, whether it was edited on the canvas or opened as a pull request.
A change opens as a diff against the live graph. Northlane names the columns it touches and lists the models, dashboards and reverse-ETL syncs sitting downstream of them.
Minutes
The new logic runs against last night's snapshot on your own compute. Row counts, null rates and distributions are compared with the current version before anything merges.
Automatic
Merging moves dev to staging to production without rewriting a connection string. Each promotion records the approver, the diff and the run that cleared it.
One command
Freshness and volume contracts attach to the new version immediately. A breach pages the table's owner with the failing column and the blast radius already resolved.
Continuous
Median time from first connection to a governed pipeline across 2025 deployments: 11 days.
Walk through it with an engineerPlatform impact
Each figure is measured on production workloads after cutover and reported by the customer's own platform team.
71 %
Fewer pipeline incidents
Failed production runs in the first quarter after cutover, against the 90 days before it.
48 accounts · platform telemetry
11
Days to the first governed pipeline
From read-only credential to a table running under an enforced freshness contract.
Median, 2025 deployments
$ 3.8 M
Warehouse spend recovered
Idle compute and duplicate models retired inside the first year on the platform.
Median enterprise account, FY25
99.98 %
Freshness SLA held
Across 12,400 monitored production tables in customer accounts.
Rolling 12-month average
Sample sizes and calculations published in full.
Connected systems
Eight of the 180 maintained connectors. Each is versioned with the platform and covered by the same uptime commitment.
Push-down SQL, automatic schema-drift handling and column-level lineage on every model run.
Open table format on object storage, with incremental merges and partition pruning read from the catalogue.
Distributed MPP reads with per-query cost attribution written back to the workspace ledger.
Two-way object sync for accounts, contacts and opportunities, including every custom field you have defined.
Deal, activity and quota objects mapped to your revenue model and refreshed on a five-minute cadence.
Schema-registry-aware consumers with exactly-once delivery into any warehouse table you nominate.
Governed metric definitions pushed downstream so every dashboard inherits the same logic and grain.
Model-level freshness and upstream lineage surfaced beside every published chart and saved view.
No connectors match that filter
Clear the search box or pick another category. If the system you need is not listed, our team can scope a custom connector.
Not on this list?
The REST and webhook APIs are open, and custom connectors ship on the standard release train rather than as a services project.
In production
Each quote comes from a named engineer running Northlane on production workloads, with the figure they reported beside it.
We retired the scheduler, the test runner and the catalogue in one quarter. Northlane did not add a tool to the stack, it removed two of them.
3 to 1 tools consolidated into one console
Priya Raghavan
VP Data Platform · Halden Systems
Every schema change now arrives with the list of dashboards it will break. Nobody guesses, and nobody ships a rename on a Friday any more.
1,400 models under lineage in nine days
Marcus Feld
Director of Analytics Engineering · Ardent Labs
Analysts used to raise a ticket to ask whether a dashboard was fresh. That question has not been asked here since March.
6 hrs of triage returned to the team weekly
Dana Okonkwo
Head of Revenue Operations · Corvane
Where teams start
Most teams adopt one surface first and pull the rest in once the graph is populated. These are the four entry points we see most often.
Replace a dbt-plus-scheduler-plus-spreadsheet stack with one versioned graph, and get column lineage without annotating a single model.
See the solutionGive every table an owner, a contract and a cost line, then hand the on-call rota alerts it can act on without opening the warehouse.
See the solutionFreshness guarantees on the tables the forecast reads from, so a pipeline review never opens with an argument about whether the numbers are current.
See the solutionColumn-level evidence retained for seven years and exported for SOC 2 and GDPR review without a single screenshot.
See the solutionEach route runs the same platform. Only the first surface differs.
Next step
A solutions engineer connects a read-only role during the call and maps lineage across your real tables. You keep the map whether or not you buy.
Read-only access, revocable by you. No agents, no copies of your data.