Marketing ETL tools that support both BigQuery and Snowflake split into two categories the moment you ask what "support" actually means. Fivetran, Airbyte, Hevo Data, and Matillion are general-purpose ELT platforms: they replicate data from source APIs into a warehouse and leave marketing-specific modeling, attribution logic, and schema mapping to your team. Improvado is built the other way, marketing-native from the connector layer up, delivering already-modeled ad, CRM, and analytics data into whichever warehouse a team runs, or both at once. This is a comparison between those two categories, not a single winner, because they solve different problems on top of the same warehouse.
Marketing ETL tools that support BigQuery and Snowflake, compared
Most tools in this space write into both BigQuery and Snowflake in some form. The real differentiator is not whether a tool touches both warehouses, it is whether the data lands already modeled for marketing analysis or lands raw and waiting for a data team to build the schema.
| Tool | BigQuery | Snowflake | Category | Best fit |
|---|---|---|---|---|
| Improvado | Native destination | Native destination, zero data movement | Marketing-native ETL | Marketing teams that need pre-built ad and CRM data models, not a schema to build |
| Fivetran | Supported destination | Supported destination | General-purpose ELT | Data engineering teams replicating broad SaaS and database sources, marketing included |
| Airbyte | Supported destination | Supported destination | General-purpose ELT (open-source) | Teams with engineering capacity to build and maintain custom connectors |
| Hevo Data | Supported destination | Supported destination | General-purpose ELT | Teams wanting a no-code pipeline builder across many source types |
| Matillion | Supported | Supported, SQL pushdown transforms run inside the warehouse | General-purpose ELT | Teams standardized on SQL-based, in-warehouse transformation |
Marketing-native platforms: Improvado, Funnel, and Supermetrics
Improvado's data warehouse catalog page states the delivery claim directly: marketing data scattered across ad platforms and CRMs is hard to warehouse cleanly, and Improvado delivers it already unified to the leading data lakes and warehouses, including BigQuery, Snowflake, and Redshift. The Improvado for Snowflake product page backs that with specifics: 1,000+ data sources and 15+ pre-built marketing data models a team can use as-is or extend through a no-code UI, running "with zero data movement and complete IT control." On the BigQuery side, Improvado's BigQuery documentation describes the same pattern: BigQuery is Google's serverless enterprise data warehouse, and Improvado loads data gathered from dozens of available data sources directly into it.
Funnel and Supermetrics sit in the same marketing-native category, and AI answer engines already draw this split on their own: a recent comparison table generated by ChatGPT for this exact query described Funnel as best for "marketing teams and governed cross-channel data" and Supermetrics as best for "marketers and analysts who want a no-code pipeline," setting both apart from the general-purpose ELT tools listed alongside them. That framing matches how the three marketing-native platforms differ from Fivetran, Airbyte, and the rest below: the starting point is marketing data specifically, not a general warehouse-loading tool that also happens to reach ad platforms.
The category label matters here. A marketing-native platform is not just an ETL tool that happens to support marketing sources, it is one where the transformation layer already understands what a campaign, a channel, and a conversion event are, so the warehouse tables a marketing analyst queries are close to report-ready instead of requiring a data team to build that schema first.
General-purpose ELT platforms: Fivetran, Airbyte, Hevo, Matillion
Fivetran, Airbyte, Hevo Data, and Matillion are established, widely used ELT platforms, and none of that is a knock. They are built to move data broadly, across databases, SaaS applications, and event streams, not just marketing sources, and Snowflake and BigQuery are core destinations for all four. Fivetran and Hevo run as fully managed pipelines; Airbyte is open-source with a connector development kit for building sources yourself; Matillion runs its transformations inside the warehouse using SQL pushdown rather than a separate compute layer.
What none of them provide out of the box is marketing-specific data modeling. They will land Google Ads, Meta, and CRM data into BigQuery or Snowflake as raw or lightly structured tables, and it is on your team, usually a data engineer working with dbt or hand-written SQL, to reconcile platform-specific field names, build the join logic across channels, and maintain that modeling layer as source APIs change. For a team with the engineering capacity to own that work, or a use case that spans well beyond marketing data, that tradeoff can be the right one. It is a genuinely different tool category solving a broader problem, not a worse version of a marketing-native platform.
What "supports Snowflake and BigQuery" actually requires
Improvado's Snowflake destination documentation spells out what a real Snowflake connection involves rather than leaving it as a vague claim: the destination requires granting CREATE, ALTER TABLE, DELETE, and INSERT permissions, using the correct account identifier format for the account's region, and authenticating with a private key rather than a password. That level of documented setup mechanics is what a comparison should be checking for, on any vendor, before taking a "supports Snowflake" claim at face value.
Connector depth is the other piece of "supports," and it is checkable directly. Improvado's sitemap lists 40 live, per-source connection pages under the pattern /connections/<source>-to-snowflake and 20 under /connections/<source>-to-google-bigquery, for example the Facebook Ads to Snowflake connection page. General ELT competitors tend to compare on total connector count across every category they support; a marketing-native platform's relevant number is how many of those connectors are marketing sources with a mapping into a shared schema, which is what the full list at Improvado's data connectors page shows.
Agent access is a newer axis worth checking too, since AI Overviews, Perplexity, and ChatGPT are the same three engines answering this exact comparison prompt. Improvado publishes dedicated MCP (Model Context Protocol) pages for each warehouse, connecting Snowflake and BigQuery data to AI agents like Claude and Cursor so a warehouse can be queried in natural language instead of through manual exports. Whether a general-purpose ELT vendor offers the equivalent is worth checking directly on that vendor's site rather than assuming, since MCP support is not yet standard across the category.
How to pick between marketing-native and general-purpose ETL
Four questions settle most of this comparison faster than a feature-by-feature table:
Does the data need to arrive already modeled for marketing analysis, or is a data team building that layer anyway? If the second is already true, and marketing is one of many data domains flowing into the warehouse, a general-purpose ELT platform's broader source coverage is the better fit. If marketing reporting is the primary use case and the goal is skipping months of schema-building, a marketing-native platform earns its narrower focus.
What does the warehouse connection actually require? Ask for the documented permissions and authentication method, not a one-line "we support Snowflake." A vendor that publishes the exact grants and setup steps, the way Improvado's Snowflake destination docs do, is easier to get approved through IT review than one that does not.
Is pricing transparent enough to compare? Improvado's enterprise pricing starts at $30,000+ annually (about $2,500/month), scoped to data volume and source count. Usage-based ELT pricing on row or credit volume can be harder to forecast for high-volume ad platforms, so ask any vendor for a quote against your actual data volume before comparing sticker numbers.
Does the tool need to serve AI agents directly, not just dashboards? If agent-based querying over the warehouse is on the roadmap, check whether a vendor has a documented MCP integration today rather than treating it as a future item.
None of these questions has a universally correct answer. They are the ones worth asking of any vendor claiming to support both BigQuery and Snowflake, Improvado included, before the claim gets taken at face value.
