Data pipeline migration
Your Fivetran bill keeps growing. Move your syncs to Rig
Rig does not charge by the row, so backfills and re-syncs stop showing up on the invoice. Ingest syncs your tools into your warehouse on a schedule, and our engineers move each connection in checked waves.
Row-based pricing bills you for every row that changes
Routine work shows up on the invoice: a backfill, a re-sync after a schema change, an API that updates the same records all day. One merged group’s Fivetran bill went from $2,548 a month to $18,901 in a year, and 46 of its sources fed nothing anyone used.
- A backfill
Row-based pricing bills you for
Every historical row, loaded again
With RigA backfill is planned work and adds nothing to the invoice.
- A schema change
Row-based pricing bills you for
A full re-sync of the table
With RigThe re-sync does not change what you pay, and the map shows which models and reports the change touches.
- A new tool
Row-based pricing bills you for
Another connector, priced on its rows
With RigWe build the connector during the migration if Ingest does not have it yet.
- The renewal
The vendor asks you to commit to
Another year of row-based pricing
With RigThe coverage review shows which connections Ingest can take over, so you renew only what has to stay.
What happens to each pipeline
We switch nothing off until its data matches.
Connections nobody checks
A coverage review
We match every connection to the tables, models and reports that read it, and switch off the unused ones first.
Billing by the row
A price agreed up front
We agree the price for your sources before the cutover, and it does not change with row counts. Syncs run on a set schedule, with on-demand refresh.
One big switch
Checked waves
Sources move in planned waves, a few at a time.
Raw tables
Described tables
Synced tables arrive ready for Rig Map, which adds descriptions, joins and certified metrics for reports and agents.
Rewired models
The same names
Ingest matches the existing table and column names, so the dbt models and dashboards on top keep working.
Connectors you cannot see
Connectors you own
Ingest is built on dlt, so each connector is a Python module in a repository you own.
How the work runs
Review the connections
We list every connection, what it costs, and which tables, models and reports read it, then mark the ones nobody uses.
Agree coverage and waves
Our engineers confirm which sources Ingest covers, agree refresh schedules, and plan the cutover in waves.
Run side by side
Each source syncs through Ingest beside the old connection, and the two are compared row by row, deleted records included.
Switch off as each matches
Once a source matches, its old connection goes. Databases that need real-time replication can stay where they are.
Which pipeline tool are you leaving?
Where to start
Common questions
Do we have to move every connector at once?
No. Most teams move the expensive SaaS connectors first and leave databases where they are until they are comfortable, and running both side by side for a while is normal.
What happens to the data already in our warehouse?
It stays where it is, and Ingest writes into that same warehouse.
Does Ingest do log-based CDC?
No, Ingest does not replicate at sub-minute latency. Batch and incremental syncs cover most sources, and if one production database needs a recovery point measured in seconds, keep it on its current pipeline and move the rest.
What if you do not have a connector we need?
We build it during the migration as a Python module, which you can read and run yourself.
Can we keep our own warehouse?
Yes. Ingest writes to Snowflake, BigQuery or Postgres, or to a warehouse we host that you own.
How is Ingest priced?
The price is set after the sync cost review, before any cutover, and nothing is billed per row.
How do we know what we would save?
Start with a sync cost review, which shows what each connection costs before you commit to anything.
Talk to Rig
What work do you want to improve?
Tell us where your team spends time, loses money or needs better information.
In a 30-minute call, we’ll discuss the data and systems work that could help.