ETL tools that load your warehouse at a flat price, not per row
ETL tools extract data from your apps and databases, transform it, and load it into a warehouse. Adapters is a lightweight ETL tool with incremental loads, type casting, and automatic retries, priced as a flat monthly tier instead of per synced row.
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Incremental loads · retries and alerts · no MAR meter, ever · Last updated August 2026
What is an ETL tool?
An ETL tool extracts data from applications and databases, transforms it into the shape and types your destination expects, and loads it into a warehouse or another system on a schedule. Modern ETL tools also own the unglamorous parts: incremental reads so you are not re-pulling whole tables, type casting, deduplication on load, retries with backoff, and an alert when a run fails. Adapters is an ETL tool of that size, priced as a flat monthly tier from $49 rather than per row synced.
The category has three shapes and the labels get used loosely. This is the honest split, and it is the fastest way to work out which kind of product your project actually needs.
| Category | Where transformation happens | Typical latency | Destination | Who buys it |
|---|---|---|---|---|
| ETL | In flight, before the load | Minutes to nightly | Warehouse, database, or an application | Teams that want typed, clean tables on arrival |
| ELT | In the warehouse, after the load, usually with dbt | Minutes to hourly | Warehouse only | Analytics teams that already model in SQL |
| Change data capture | None; it emits raw change events | Seconds | A stream, then anything downstream | Engineering teams that need deletes and low latency |
| iPaaS | Field level mapping between business objects | Minutes | Another business application, often two way | Ops and finance teams keeping systems in agreement |
If the destination is Snowflake or BigQuery, you are in the top two rows, and ETL versus ELT settles which. Snowflake buyers can go straight to the Snowflake ETL tools shortlist, which includes Snowflake's own Openflow service alongside the third-party platforms, and there is a matching shortlist of BigQuery ETL tools covering the four separate first-party products Google ships for this job. On AWS the equivalent shortlist is Redshift ETL tools, where the first question is whether zero-ETL removes the need for a pipeline entirely. If you need hard deletes propagated within seconds, you are in the third row and the change data capture tools comparison is the right shortlist. If the destination is another application rather than a warehouse, you want an iPaaS platform instead, and what iPaaS means explains where that line falls. Where the warehouse is the source and a business application is the destination, the pipeline is running backwards on purpose, and the shortlist for that is reverse ETL tools.
The three ways warehouse pipelines hurt
Most teams arrive here from one of two places: a usage-billed ELT vendor whose invoice doubled after a backfill, or a folder of cron scripts nobody wants to touch. Both fail the same three ways.
Failure 01
The surprise invoice
MAR pricing bills every row that changes. One historical backfill, one schema resync, one busy month, and the bill you budgeted at $1,000 lands at $4,800. Finance asks, and you spend a day explaining a meter you do not control.
Failure 02
The silent script
A cron job that exports Postgres to the warehouse fails on a Tuesday and nobody notices until the Monday revenue dashboard is flat. Hand-rolled pipelines fail silently because alerting was always the ticket that slipped to next sprint.
Failure 03
Schema drift
A product engineer renames a column, an API adds a nested field, and your load job starts writing nulls. Drift does not throw errors; it quietly corrupts weeks of tables before an analyst catches the gap.
What Adapters does between app and warehouse
Adapters is a data integration platform that treats a warehouse like any other destination: pick a source, map fields on two port lists, and the pipeline runs on a schedule down to every minute. A typical postgres to snowflake sync is live in under ten minutes.
Incremental loads by default
After the first full load, Adapters tracks a cursor per table and moves only new and changed rows. A 40M-row Postgres table syncs in seconds per run, not hours, and never re-bills you for history it already moved.
Type casting that matches your warehouse
Stripe sends cents as integers and dates as Unix timestamps. Adapters casts them to NUMBER(10,2) and TIMESTAMP_TZ on the way in, with a JSON in, JSON out preview on real sample records before anything lands.
Schema mapping you can version
Source and destination render as two port lists joined by cables. When a source column is renamed or added, the run flags the drift and proposes the mapping change; nothing writes nulls silently. Mappings are versioned on Scale, so you can diff and roll back.
Retries, alerts, and a per-record log
Failed batches retry with exponential backoff and idempotency keys. If a run still fails, you get an alert within a minute, plus a record-level trace showing exactly what came in, what transformed, and what landed.
Flat tier vs MAR meter: one pipeline, one year
The worked example: one team syncing about 500,000 rows a month from Postgres and Stripe into Snowflake. Usage-billed ELT meters monthly active rows at roughly $2 per 1,000; Adapters Scale is $399 a month for up to 1M records, flat.
| Line item | MAR pricing (~$2 / 1k rows) | Adapters Scale, flat |
|---|---|---|
| Steady month, 500k rows (×11) | $1,000 / mo | $399 / mo |
| Backfill month, 2.4M rows resynced | $4,800 that month | $399, same as always |
| New column added, table re-cursored | Every touched row re-billed | Included |
| Year one total | $15,800 | $4,788 |
Same pipeline, $11,000 kept. And the number Finance actually cares about: next month's bill is known today. Full tiers on the data integration pricing page.
Built for data and analytics engineers
Adapters fits the team that owns the warehouse but not a platform budget: the analytics engineer feeding dbt models, the data engineer replacing a dozen export scripts, the one-person data team at a 50-person company. If you know the etl vs elt debate and just want typed, monitored tables by 9am, this is your lane.
It is deliberately lightweight. If you need streaming CDC at 100M rows an hour or a Spark cluster, use a heavy platform. For app and database sources landing in Snowflake, BigQuery, Redshift, or Postgres on an every-minute schedule, this is the right size of tool: the pipelines are mappings you can read, and a failure alerts instead of going silent.
Questions teams ask about ETL tools
- What is an ETL tool?
- An ETL tool extracts data from applications and databases, transforms it into the types and shape the destination expects, and loads it on a schedule. Beyond the three steps it owns incremental reads, deduplication, retries with backoff, and alerting, which is the part teams underestimate when they write the pipeline themselves.
- What are ETL tools examples?
- The category spans several shapes. Fivetran, Airbyte, Matillion and Stitch are warehouse-focused. Informatica, Talend and SSIS are the traditional enterprise tools. Boomi, Workato and Celigo sit on the application integration side. Adapters is a flat-price tool for app and database sources landing in Snowflake, BigQuery, Redshift or Postgres.
- What is the difference between ETL and ELT?
- ETL transforms data before it lands, so the warehouse receives clean typed tables. ELT loads raw data first and transforms it inside the warehouse, usually with dbt. ELT wins when you have warehouse compute and a modeling team. ETL wins when the destination is not a warehouse, or when you want the data usable the moment it arrives.
- How do I choose an ETL tool?
- Check five things in this order: whether it reads your specific sources, whether loads are incremental or full refresh, what happens to a failed record, how the bill moves when volume triples, and how long the first working pipeline takes. Usage-billed pricing is the item that surprises finance six months in.
- Are ETL tools still relevant?
- Yes. ELT changed where transformation happens, not whether data has to move. Something still has to authenticate to the source, read only what changed, handle rate limits and schema drift, and retry cleanly. That work is exactly what an ETL tool is, and it did not disappear when dbt arrived.
- What are the best open source ETL tools?
- Airbyte, Meltano and Apache NiFi cover most open source ETL needs, with Airflow and Dagster orchestrating them. For log-based change data capture, Debezium is the standard and is licensed Apache 2.0. All of them trade licensing cost for infrastructure and engineering time you have to supply yourself.
- How much do ETL tools cost?
- Three models dominate. Usage-based vendors bill by monthly active rows or credits, so the invoice tracks volume and a backfill can multiply it. Enterprise platforms sell annual quote-based contracts. Flat-tier tools such as Adapters charge a fixed monthly price, $49 to $399, so a busy month costs the same as a quiet one.
The full vendor lineup is on best data integration tools, and the log-based end of the category is broken down on change data capture tools. The database lanes teams load most often are Postgres to Snowflake, MySQL to BigQuery and SQL Server to Snowflake, and the rest are in the connector library.
Your warehouse, loaded by tonight
Incremental loads, type casting, and retries from $49 a month, flat. The first pipeline takes about ten minutes.
No credit card required.