Build a dependable route from source to warehouse.

We build and repair the ingestion and orchestration behind your data. The loading strategy follows the source, the volume, and how fresh the result needs to be. Recovery is designed alongside the successful run.

Operational recordsReady

Instrument exportReady

API extractWaiting for source

Reconcile one load

997 accepted + 3 held = 1,000 source records

Held records stay visible; dependent work waits for its required source.

Choose the right
loading pattern.

Connect the sources your teams need, with a loading strategy that fits how they work. We use Fivetran for supported sources and Python for custom extraction, choosing between batch loads, APIs, and change data capture around source capabilities, volume, and freshness. Instrument exports, operational databases, and application events each bring their own identifiers and update behavior. We design those differences into the route to the warehouse, including reconciliation and a tested recovery path.

load    101  102  103
replay       102  103  104

101102103104

Four unique destination keys

Make reruns
safe.

Recover an interrupted load without turning a retry into duplicate records. We define stable keys, checkpoints, and deduplication rules, then test how the load resumes. Updates, deletions, and backfills need a route to the target too. Source versions and extraction boundaries stay connected to the run history, so the team can trace a correction and understand what needs to run again. Update keys and late-arrival windows determine which target records can change.

Run work in
dependency order.

Give each run the inputs it needs and each alert a useful next step. Apache Airflow can coordinate schedules and dependencies; we build the readiness checks, retry safeguards, and ownership around that workflow. A late source should keep dependent work waiting. A failed transformation needs investigation. The run history makes that distinction clear, with a recovery path the maintaining team can follow. Backfills follow the same dependency graph, with the affected outputs identified.

Worksheets

select count(*) from raw.load_014;

Source boundary · run LD-014
Result	Records
Received from source	1,000

Source IDs and extraction boundary retained.

Worksheets

Validation LD-014 · showing 1 of 3 held records
Record	Issue
`SRC-084`	Missing source key

Data operations Review assigned

Held records stay outside the accepted model.

Worksheets

LD-014 · 1,000 source records
Accepted	997
Held	3

997 + 3 = 1,000

3 records remain held, with their run and source references.

Account for what arrived.

See what loaded, what was held, and what still needs attention. We reconcile source and target counts or control totals, preserve invalid records under an agreed policy, and connect exceptions to an owner. For laboratory measurements, that record can include sample and run identifiers, units, method versions, and source quality flags. The team can follow a result back to the inputs behind it and resolve the difference. Corrections retain their source and run references.

Governed model

account_idA-037

eligibletrue

ID + rule checked

Destination updateSalesforce / HubSpot

Deliver governed data
to the workflow.

Put curated data to work in the systems your teams use. Reverse ETL through Hightouch connects models to operational destinations, with stable identifiers, eligibility rules, exclusions, and ownership agreed before an update runs. Salesforce, HubSpot, and other systems retain their own access and workflow requirements. We design the integration around those constraints, so the destination receives the records and fields that belong in its workflow. Reviewed field mappings explain each change before it reaches the destination.

What does your data need to do next?

Tell us which sources need to connect, how fresh the result needs to be, and what currently happens when a load fails.

Discuss your project

Build a dependable route from source to warehouse.

Choose the right loading pattern.

Make reruns safe.

Run work in dependency order.

Account for what arrived.

Deliver governed data to the workflow.

What does your data need to do next?

Choose the right
loading pattern.

Make reruns
safe.

Run work in
dependency order.

Deliver governed data
to the workflow.