Every mobile growth team runs the same relay race. A user taps an ad, an MMP like AppsFlyer or Singular stitches that click to an install. The app fires in-app events back to the attribution provider. And, somewhere in finance a spreadsheet is trying to turn all of that into a ROAS number the CFO will believe. The problem isn’t any single tool in that chain – it’s what happens in the handoffs. Attribution data lives in one silo, ad spend lives in another, revenue lives in a third. Stitching them together by hand is how “quick weekly report” turns into a two-day fire drill every Monday.
ELT is the layer that fixes the handoffs. Extract sits between your measurement partners and your warehouse. It also sits between your warehouse and the tools your analysts actually open every day – dbt, Looker, Hex. It doesn’t replace any of them. It’s the connective tissue that makes the rest of the stack work the way it’s supposed to.
Here’s how the pieces fit together in practice.
The warehouse layer: Snowflake, BigQuery, Databricks, Redshift
Whichever warehouse your team has standardized on, the job is the same. You need to land raw data reliably, and handle schema drift without babysitting it. And finally, you have to do it without a data engineer riding shotgun on every sync. Extract’s destinations are built around that, and the setup reflects real production patterns rather than a lowest-common-denominator CSV drop:
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Snowflake: Extract authenticates as a dedicated service user with key-pair auth, stages records in S3, and loads through COPY INTO, with support for incremental upserts, full refreshes, or full history tracking depending on how you want a table to behave.
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BigQuery: connects via a service account (Extract’s own, or one you provision) scoped to BigQuery Data Editor and Job User roles, so your security team can see exactly what access was granted and why.
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Databricks: loads straight into Delta tables through a SQL warehouse, staging Parquet files in a Unity Catalog volume before the COPY INTO step, with access-token, OAuth user, or OAuth machine-to-machine auth depending on how locked-down your workspace is.
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Redshift: the same incremental-or-full-refresh choice, mapped to Redshift’s own load mechanics.
None of that is meant to be a sales pitch for any one warehouse – it’s the point. The warehouse choice stays yours. Extract’s job is to make the data show up there in a shape dbt can actually build on. All without you writing custom loaders for four different platforms.
The MMP layer: where attribution data actually comes from
This is the part worth being precise about, because “we integrate with your MMP” means different things depending on which MMP you mean.
Extract is built by Singular, so Singular data gets the deepest treatment. A Partner Config connector for campaign-level reporting, and a User Level connector that pulls attribution, in-app events, clicks, and impressions at the granularity most attribution and LTV modeling actually needs. If your stack already includes Singular, this is close to a native, first-party pipe rather than a bolted-on integration.
AppsFlyer support exists as a connector today, and it’s early – authentication and configuration are live, but data extraction is still being built out stream by stream. If there’s a specific AppsFlyer report or endpoint your team depends on, Extract’s team builds against real requests rather than guessing at a generic schema, so naming the endpoint is what moves it up the queue.
Adjust and Branch aren’t native connectors yet. In the meantime, both expose REST APIs and, in Branch’s case, warehouse-export options, and Extract’s generic API and webhook sources can be pointed at them so you’re not blocked while a dedicated connector is built. If your team runs on either, the fastest path to a native connector is the same one as AppsFlyer: tell Extract which reports you need.
The honest version of this story – a strong, first-party connector for Singular, an actively-developing one for AppsFlyer, and a build-your-own bridge for Adjust and Branch today – is more useful to a data team evaluating tools than a marketing page that implies four identical checkboxes. Connector maturity is a real input into build-vs-buy decisions, and it’s one Extract’s own docs are upfront about.
The downstream layer: dbt, Looker, Hex
Once attribution, spend, and revenue data are landing in the warehouse on a schedule, the interesting work starts – and none of it is Extract’s job to do.
dbt is where MMP data stops being raw event logs and starts being a model. A typical setup joins Singular or AppsFlyer install and event data with ad spend pulled from Meta, Google, TikTok, and Apple Search Ads, then rolls it up into a fct_attribution or fct_marketing_performance table that has one row per campaign per day with spend, installs, and downstream revenue already reconciled. Because Extract lands raw, untransformed data, dbt owns the business logic end to end. There is no transformation happening upstream that your analytics engineers can’t see or version-control.
Looker is usually where that dbt model becomes the number leadership actually looks at. A LookML layer built on top of the attribution model gives marketing, finance, and product a shared, governed definition of ROAS, CAC, and payback period. This means that “what’s our blended CAC this week” gets the same answer whether product or finance asks it.
Hex tends to sit next to Looker rather than behind it – the place where a data scientist pulls the same dbt-modeled tables into a notebook to test whether a new LTV model actually predicts payback better than the current one, or to build a one-off cohort analysis that doesn’t belong in a permanent dashboard. Same warehouse tables, same dbt models, different job: exploration instead of reporting.
The throughline across all three is that Extract only has to succeed once. It gets clean, reliable, schema-stable data into the warehouse: for dbt, Looker, and Hex. And they all work off the same foundation. Therefore, no individual team has to build its own version of “what counts as an install.”
What this looks like end to end
A growth team wants a daily blended ROAS view by campaign and creative:
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Extract pulls Singular user-level attribution data and ad spend from Meta, Google, and TikTok on independent schedules. It all lands in Snowflake with minimal schema mapping.
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dbt joins spend, installs, and downstream revenue events into a single attribution model. And it applies whatever attribution window and de-duplication logic the team has agreed on.
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Looker exposes that model as a governed explore. This way marketing can slice ROAS by campaign, creative, and geo without touching SQL.
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Hex picks up the same model when someone wants to test a new attribution methodology before it becomes the official one.
Nothing in that chain requires custom scripts glueing MMP exports to warehouse loads. Nothing requires dbt, Looker, or Hex to know where the data came from. That’s the actual value of an ELT layer in a mobile marketing stack. It is not a longer feature list, but one less thing anyone has to maintain by hand.
If your team is evaluating how attribution data should flow into your warehouse – or you’ve got a specific AppsFlyer, Adjust, or Branch report you need pulled – try Extract free or get in touch about what to build next.
