ChatGPT answers, unedited

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist platforms based on how much control you want over schemas, raw fields, and pipeline ownership.

My recommendation by scenario

If you have a data engineering team and need maximum analytical flexibility

• Choose Fivetran or Airbyte.
• Land raw tables in Snowflake, BigQuery, Redshift, or Databricks.
• Build your own dbt models for campaign → ad set → ad → creative → conversion analysis.

If marketing analysts will own the pipeline

• Look at OWOX or Supermetrics.
• They are designed around getting paid media data into analysis workflows without building API integrations yourself. www.stitchdata.comwww.owox.com

If your priority is granular Meta/Facebook + Instagram data

• Verify that the connector exposes:
  • campaign, ad set, ad, creative IDs
  • daily insights
  • placement breakdowns
  • demographic breakdowns
  • attribution windows
  • conversion action arrays
  • video metrics
  • audience/targeting metadata

Some tools provide only reporting-level metrics; others preserve lower-level objects. For example, some Meta-specific warehouse connectors advertise campaign, ad set, ad, creative, audience, placement, demographic, and conversion tables. datawarehouse.io

A stack I’d typically recommend

Modern warehouse stack:

1. Connector: Airbyte / Fivetran / OWOX
2. Warehouse: Snowflake, BigQuery, Redshift, or Databricks
3. Modeling: dbt
4. BI: Looker, Tableau, Power BI, Mode, or Hex

A good target schema would look like:

raw_meta_ads
raw_tiktok_ads
raw_linkedin_ads

dim_campaign
dim_ad_set
dim_ad
dim_creative

fact_ad_daily_performance
fact_conversions
fact_spend

For most companies doing serious custom attribution, creative analysis, or LTV modeling, I would avoid tools that only export dashboards or CSVs and prioritize a warehouse-native ELT approach.

If you share your warehouse (Snowflake/BigQuery/Databricks/etc.), ad platforms (Meta, TikTok, LinkedIn, Reddit, X), and required granularity (ad-level vs impression-level/event-level), I can narrow this to 2–3 best fits.

If your priority is granular paid-social data → warehouse → custom SQL/analytics, I’d shortlist these:

My take

I'd start with Supermetrics if you're primarily trying to get reliable, granular ad data into an existing warehouse and then do the interesting work yourselves in SQL/dbt/Python. Its Facebook/Instagram connector explicitly supports campaign, ad-set and creative-level metrics alongside audience and placement dimensions, and it can deliver to BigQuery, Snowflake and Redshift. Supermetrics

I'd investigate Adverity instead if you have a much larger ecosystem—lots of ad networks, regions/accounts, custom transformations, data-quality monitoring, or enterprise governance. Its current offering advertises 600+ connectors and automated monitoring/harmonization before delivery to the warehouse. Adverity

One important diligence point: don't evaluate these only on the number of connectors. For custom analysis, I'd test the exact dimensions + metrics + historical lookback + breakdowns you need from Meta, TikTok, LinkedIn, etc. API availability can differ substantially by platform and reporting level.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and which ad platforms you need (Meta, TikTok, Google, LinkedIn, Reddit, etc.), I can compare the relevant options on granularity, historical data, refresh frequency, schema flexibility, and likely cost.

For granular social-ad data → data warehouse → custom SQL/analysis, I’d shortlist Supermetrics and Funnel.

My recommendation: Supermetrics

supermetrics.com is a strong fit if your priority is getting a broad set of ad-platform dimensions/metrics into your warehouse without building and maintaining API pipelines yourself.

• Broad source coverage: Meta/Facebook Ads, Instagram, TikTok, LinkedIn Ads, Pinterest, Reddit, Snapchat, X, etc. are supported. Supermetrics
• Warehouse-native delivery: supports BigQuery, Snowflake, Redshift, Azure Synapse, Databricks and other destinations. Supermetrics
• Granularity: Supermetrics provides field lists and warehouse-table documentation, so you can inspect what dimensions and metrics are actually available before designing your model. docs.supermetrics.com
• Operationally easier: it handles API connections, incremental loads, schema management and error recovery rather than making your team maintain those pieces. Supermetrics
• Backfills: it supports historical backfills and configurable refreshes. docs.supermetrics.com

The other one I'd evaluate: Funnel

funnel.io is particularly interesting if you want more control over the shape of the exported dataset. Funnel lets you define data sources with specific metrics, dimensions, filters and granularity, and then export those datasets to destinations such as BigQuery, Snowflake and Redshift. Funnel Knowledge Base Funnel Knowledge Base

Funnel also advertises custom dimensions and metrics, which can be useful if you're building a standardized paid-social model across multiple ad platforms. funnel.io

How I'd choose

One important caveat: don't choose based only on the connector count. For your use case, I'd make the decision based on whether each platform exposes the specific grain and fields you need—e.g. account → campaign → ad set/ad group → ad → creative, plus daily spend, impressions, clicks, conversions, placement, audience, geography, device, attribution window, and creative identifiers.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and the social platforms (Meta, TikTok, LinkedIn, Reddit, Pinterest, etc.), I can compare Supermetrics vs. Funnel vs. Fivetran specifically for your stack and data-granularity requirements.

If your priority is granular paid-social data → warehouse → custom SQL/modeling, I’d shortlist these:

• supermetrics.com — my default recommendation. It has strong coverage across Meta/Facebook & Instagram, TikTok, Google, LinkedIn, etc., lets you select metrics/dimensions for warehouse transfers, and supports destinations including BigQuery, Snowflake, Redshift, S3, and others. Supermetrics Supermetrics Supermetrics
• funnel.io — best if you want more data transformation/marketing-analytics workflow built in. It supports granular dimensions/metrics, warehouse exports, and creative-level exports, including creative assets and metadata to object storage. Funnel Help Funnel Help
• windsor.ai — worth considering for breadth and flexibility. It supports Meta, Google, LinkedIn, TikTok and many other sources, with destinations including BigQuery, Snowflake, Redshift, Databricks, PostgreSQL and S3. Windsor.ai windsor.ai

What I'd choose

For a data/analytics team doing custom analysis rather than just dashboards, I'd start with Supermetrics. Its warehouse product is explicitly designed around selecting the dimensions and metrics you want, maintaining historical data, and transferring it on a schedule. Supermetrics Supermetrics

If creative-level data and broader marketing-data transformation are particularly important, I'd evaluate Funnel alongside it.

One caveat: "granular" doesn't necessarily mean every field available in the underlying ad-platform API. Before committing, I'd test your required schema—e.g. account → campaign → ad set/ad group → ad → creative, placement, audience, geography, device, date, plus spend/impressions/clicks/conversions—against each connector.

If you tell me whether you're on Snowflake, BigQuery, Databricks, or Redshift and which ad platforms you use, I can give you a more specific recommendation and compare Supermetrics vs Funnel vs Windsor.ai on granularity, historical backfills, freshness, transformations, and likely cost.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist these platforms. The best choice depends on whether you prioritize engineering control, breadth of ad platforms, or ease of setup.

Fivetran Supermetrics## My recommendation by use case

1. You want the cleanest warehouse pipeline → Fivetran

Choose this if your stack is something like:

Meta Ads + TikTok Ads + LinkedIn Ads + Google Ads → Snowflake/BigQuery/Databricks → dbt → BI

Fivetran’s ad connectors are designed to replicate advertising data into warehouses, and it supports destinations including Snowflake, BigQuery, Databricks, and others. Fivetran It also provides ad reporting transformation models that standardize metrics like spend, clicks, impressions, campaigns, ad groups, and ads across platforms. Fivetran

Good fit if:

• Analysts need SQL access to raw-ish campaign data
• You already have data engineering practices
• Reliability matters more than lowest cost

2. You are a marketing analytics team → Supermetrics

Choose this if marketers need to own the workflow.

Supermetrics focuses heavily on marketing sources and can move paid media data into warehouses such as BigQuery, Snowflake, and others. Supermetrics It supports many ad and social sources, including platforms like Facebook Ads, LinkedIn Ads, Instagram Insights, Reddit Ads, and others. Supermetrics

Good fit if:

• You need many marketing connectors quickly
• You care about campaign/ad/creative reporting
• You want less engineering involvement

3. You need maximum customization → Airbyte or custom API ingestion

If you need:

• hourly pulls
• unusual breakdowns (creative attributes, audience segments, placements)
• custom attribution logic
• raw API objects

a more engineering-oriented approach may be better.

What I would evaluate before buying

Make sure the platform can extract the dimensions you actually need:

• Campaign → ad set/ad hierarchy
• Creative IDs and metadata
• Placement breakdowns
• Audience targeting dimensions
• Geography/device breakdowns
• Conversion events
• Attribution windows
• Daily historical backfill
• Change tracking for renamed/deleted campaigns

Also check whether it exports raw API-level data versus only pre-aggregated reports. Some connectors are report-based and require selecting metrics/dimensions rather than extracting every underlying object. Fivetran

Default recommendation

For most companies doing custom analysis:

1. Fivetran — if data engineering owns the warehouse
2. Supermetrics — if marketing analytics owns the workflow
3. Airbyte — if you have engineers and want control/cost flexibility

If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.), ad platforms (Meta, TikTok, LinkedIn, Reddit, X, Pinterest), and desired granularity (campaign vs ad vs impression-level), I can narrow this down.

If your main requirement is granular paid-social data → your warehouse → custom SQL/analysis, I’d shortlist Funnel and Supermetrics, with Funnel as my default recommendation.

1. funnel.io — best overall fit

Funnel is particularly strong when you want control over the dimensions and metrics that land in the warehouse. Its exports let you select the fields you need, and it supports destinations including BigQuery, Snowflake, Redshift, S3, and Azure. Funnel Funnel Help

Why I'd pick it:

• Granular field selection: choose the dimensions/metrics rather than being stuck with a fixed reporting schema. Funnel Help
• Broad paid-social coverage: Meta, TikTok, LinkedIn, Pinterest, Snapchat, Reddit, X, etc., alongside Google Ads and other marketing sources. Funnel Help
• Warehouse-oriented: it is designed to absorb source API changes and deliver warehouse-ready data, rather than making your engineers maintain dozens of ad-platform integrations. Funnel
• Creative-level data: Funnel can export creative assets and metadata, which is valuable if your analysis goes beyond campaign/ad-set performance. Funnel Help
• Pricing isn't row-based: Funnel says it doesn't charge based on data volume/row count. Funnel
• Custom destinations are available on Business/Enterprise if your warehouse setup is unusual. Funnel Help

2. supermetrics.com — strong alternative

I'd choose Supermetrics if you want a very mature ecosystem and particularly broad marketing-source coverage. Its warehouse product supports 170+ marketing platforms, incremental loading, and destinations such as BigQuery, Snowflake, Redshift, Databricks, and others. Supermetrics Supermetrics

It's also well suited to combining social ads with CRM, analytics, search, and other marketing data in the warehouse. Supermetrics

My recommendation

I'd start with Funnel if you're building a centralized warehouse and want analysts to have maximum flexibility over the raw-ish advertising data they receive.

One thing I'd verify in a proof of concept before buying: exactly which breakdowns are available for each network you care about (e.g. Meta placement × age × gender × creative, TikTok video/ad-level fields, LinkedIn audience dimensions). “Granular” varies substantially by ad-platform API, so the connector's theoretical coverage matters more than the vendor's overall feature list.

If your priority is granular social-ad data → warehouse → custom SQL/analysis, my first choice would be funnel.io.

My shortlist

• Funnel — best overall for a marketing-data warehouse

• Supports 640 connectors, including Meta/Facebook Ads, Instagram, TikTok, Google Ads, LinkedIn, etc. Funnel
• Lets you explicitly choose the dimensions and metrics you export, then send them to warehouses such as BigQuery, Snowflake, and Redshift. Funnel Funnel Knowledge Base
• Particularly attractive if you expect to combine paid social with search, analytics, CRM, and ecommerce data.
• It also supports custom dimensions and metrics, which is useful for building your own analytical layer. Funnel
• supermetrics.com — best if field-level granularity is the main concern

• Its Meta connector currently documents 537 metrics and 254 dimensions, with ad/campaign/ad-set, audience, placement, and other granular data. Supermetrics
• It supports BigQuery, Snowflake, and Redshift, and has similarly granular TikTok warehouse tables. Supermetrics Supermetrics
• I'd lean this way if your analysts frequently need obscure platform-specific fields rather than just standardized reporting metrics.
• windsor.ai — worth considering for a lighter-weight ELT/API approach

• Connects Meta, TikTok, LinkedIn, Google Ads and hundreds of other sources to BigQuery, Snowflake, Redshift, Databricks, etc. Windsor.ai
• Its model is attractive if you want more direct control over extracting/filtering fields and destinations.

What I'd pick

Funnel if you're building a durable marketing data foundation across many ad platforms.

Supermetrics if "granular" means I want as many native platform dimensions/metrics as possible and don't want the connector abstracting them away.

One important caveat: these platforms generally aren't exporting individual ad-click/impression events. The ad platforms themselves expose largely aggregated reporting data, often at daily/account/campaign/ad-set/ad/placement/geography levels, and those numbers can be revised after the initial reporting period. Funnel Knowledge Base Funnel Knowledge Base

If you tell me whether your warehouse is Snowflake, BigQuery, Databricks, Redshift, or something else, I can narrow this to the best 2–3 options and compare field coverage, historical backfill, sync frequency, API limits, and pricing.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist platforms based on how much control you want over schemas, raw fields, refresh frequency, and warehouse destinations.

Top recommendations

My pick by scenario

If you have a data engineering team

Choose Fivetran or Airbyte.

You’ll get:

• Raw-ish ad objects (campaigns, ad sets, ads, creatives)
• Incremental syncs
• Warehouse-native modeling
• More control over downstream transformations

Typical stack:

Meta Ads API / TikTok Ads API / LinkedIn Ads → ELT connector → Snowflake/BigQuery → dbt → BI

If marketing analysts own the pipeline

Choose Supermetrics or OWOX.

They are easier for teams that care more about:

• Campaign reporting
• Spend/ROAS analysis
• Creative performance
• Audience and placement breakdowns

OWOX specifically positions its connectors around loading ad data into warehouses and building reusable marketing data marts. OwoX

If you need the deepest Meta Ads granularity

Look for support for:

• Campaign → ad set → ad hierarchy
• Creative IDs and metadata
• Placement breakdowns (Feed, Stories, Reels, etc.)
• Age/gender/location breakdowns
• Conversion action breakdowns
• Hourly or daily insights
• Historical backfills
• Deleted/archived object handling

Some simpler reporting connectors only expose summarized metrics and can hide the raw insight tables.

What I would evaluate before buying

1. Warehouse destinations

• Snowflake
• BigQuery
• Databricks
• Redshift
• Postgres
• Granularity

Ask: “Can I query performance at the ad creative × audience × placement × date level?”

3. Schema stability

Ad APIs change frequently; automated schema management matters.

4. Historical backfill

Can you pull 1–2 years of historical ads data?

5. Attribution fields

Verify support for:

• purchases/revenue
• leads
• custom conversions
• attribution windows
• Cost model

Watch for pricing based on:

• rows synced
• API calls
• destinations
• accounts connected

A practical recommendation

For most companies building a warehouse for custom social ad analysis:

• Enterprise: Fivetran + Snowflake/BigQuery
• Marketing analytics team: OWOX or Supermetrics
• Engineering-heavy / cost-sensitive: Airbyte
• Agency reporting + multi-client dashboards: Supermetrics or Porter Metrics

If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.) and the ad platforms you need (Meta, TikTok, LinkedIn, Reddit, X, Pinterest), I can narrow this to 2–3 best fits.

If your priority is granular paid-social data → warehouse → custom SQL/analysis, my first choice would be Funnel.

My pick: Funnel

funnel.io

Why I’d choose it:

• Built for warehouse exports: supports destinations including BigQuery, Snowflake, Redshift and other warehouse/storage options. Funnel Funnel Help
• Granular ad data: connects to platforms such as Meta, TikTok and Google Ads, with dimensions/metrics you can select for export. TikTok, for example, exposes campaign-level fields and broader granular data through its connector. Funnel Funnel Help
• Transformation layer: you can normalize, filter, and create custom metrics/dimensions before the data lands in your warehouse, rather than dumping everything into raw tables. Funnel
• Good fit for analysts: you can control the fields and export schedule, then do the genuinely custom work in your warehouse. Funnel Help
• Custom destinations: if your warehouse or internal infrastructure isn't supported natively, Funnel offers custom destinations on Business/Enterprise. Funnel Help

Strong alternative: Supermetrics

supermetrics.com

I'd consider Supermetrics instead if you want a broader marketing-data ecosystem and particularly strong support for combining Meta + TikTok + Google + LinkedIn + other sources. Its Meta connector supports campaign, ad-set, creative, audience, and placement-level data, while its warehouse destinations include BigQuery, Snowflake and Redshift. Supermetrics Supermetrics

Quick decision

Bottom line: for a data/analytics team that wants reliable granular ad data in its warehouse and intends to build its own models, attribution logic, cohort analysis, and dashboards, I'd start with Funnel. The important thing to validate in a trial is the exact breakdowns and historical fields available from each ad API—you don't want to discover after implementation that a particular Meta/TikTok dimension isn't exposed at the granularity you need.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist platforms based on how much control you want over the raw data model, warehouse destination, and transformation layer.

Best overall options

My recommendations by scenario

1. You need raw, granular data for analysts/data scientists

Pick:

• Fivetran
• Airbyte
• OWOX

Look for:

• campaign → ad set → ad → creative-level extraction
• daily/hourly incremental syncs
• historical backfills
• preservation of platform IDs
• support for breakdowns (placement, geography, device, audience, age/gender)

2. Your warehouse is BigQuery/Snowflake and marketing owns the pipeline

I’d lean toward:

• OWOX — marketing-first warehouse workflows and prebuilt connectors. OwoX
• Supermetrics — easier for marketing teams that need less engineering.

3. You run many paid channels (Meta, TikTok, LinkedIn, Google Ads, etc.)

Consider:

• Adverity
• OWOX
• SpendMix, which focuses on normalizing multiple advertising sources into a shared schema and sending data to destinations such as warehouses and APIs. SpendMix

4. You want maximum control and lower platform lock-in

Consider:

• Airbyte
• Direct ingestion from APIs using your own pipelines

This gives you control over:

• raw tables
• attribution logic
• custom joins with CRM/revenue data
• machine-learning features

What I would evaluate before buying

For social ads specifically, don’t just compare connector count. Check whether the platform exports:

• Meta Ads

• campaign, ad set, ad, creative IDs
• spend/impressions/clicks
• conversion actions (purchase, lead, etc.)
• attribution windows
• placement/device/demographic breakdowns
• creative metadata
• TikTok Ads

• ad group/ad-level performance
• creative IDs
• video metrics
• LinkedIn Ads

• campaign performance
• lead-gen form data
• Warehouse support:

• BigQuery
• Snowflake
• Databricks
• Redshift
• Postgres/S3 staging

My default shortlist

For most companies building a custom marketing analytics warehouse, I’d start with:

1. Enterprise: Fivetran
2. Marketing data warehouse focus: OWOX OwoX
3. Cost/control focused: Airbyte
4. Agency/multi-client reporting: Supermetrics or SpendMix SpendMix

If you share your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.), ad platforms (Meta/TikTok/LinkedIn/etc.), and approximate spend/account count, I can narrow this to 2–3 best fits.

For your use case, I’d shortlist Funnel and Fivetran, with Funnel as my first choice if the primary goal is granular paid-social data for custom analysis.

My recommendation: Funnel

funnel.io is particularly strong for marketing/ad data because it is purpose-built around advertising platforms rather than being a generic ETL tool.

• Broad paid-social coverage: Meta/Facebook Ads, TikTok Ads, LinkedIn, Pinterest, Snapchat, Reddit, X, etc. Funnel currently advertises 600+ connectors. Funnel Funnel
• Granular dimensions and metrics: You can configure data sources around the dimensions and metrics you actually need, rather than being limited to a canned dashboard dataset. Funnel Help
• Warehouse exports: It supports destinations including BigQuery, Snowflake, Redshift, and other data-warehouse/storage options. Funnel Help
• Good for cross-platform analysis: This is useful if you want to build your own warehouse models comparing, say, Meta campaign/ad data against TikTok and Google.
• Marketing-specific normalization: Funnel handles a lot of the ugly API differences between ad platforms for you.

When I'd choose Fivetran instead

fivetran.com is my pick if you already have a mature data engineering stack and want the marketing data to behave like any other source in your warehouse.

Its Facebook Ads connector, for example, replicates a deep level of source data into your warehouse/data lake. Fivetran Fivetran also has connectors for TikTok, LinkedIn, Pinterest, Reddit, Snapchat, X and many other advertising platforms. Fivetran

A nice bonus is its dbt packages: Fivetran provides models that transform Facebook Ads and TikTok Ads data into analytics-ready tables, and its broader Ad Reporting model standardizes reporting across platforms. GitHub GitHub Fivetran

How I'd decide

Bottom line: If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.) and the ad platforms you care about (e.g. Meta + TikTok + Google + LinkedIn), I can narrow this down to the best 2–3 options and compare granularity, historical backfill, API limitations, pricing model, and data freshness.

For granular social-ad data → data warehouse → custom SQL/BI analysis, I’d shortlist three, with Supermetrics as my default recommendation.

My recommendation: supermetrics.com

It’s particularly strong if you want to centralize Meta, TikTok, LinkedIn, Pinterest, Reddit, Snapchat, X, Google Ads, etc. without building and maintaining API integrations yourself. Supermetrics currently supports 170+ marketing data sources and destinations including BigQuery, Snowflake, Redshift, and cloud storage. Supermetrics Supermetrics

For your specific requirement, the important part is granularity: its Meta connector supports campaign, ad-set, and creative-level data along with audience and placement dimensions, rather than just high-level spend/impressions reporting. Supermetrics

Why I'd pick it:

• Broad cross-channel coverage.
• Direct warehouse loading.
• Granular dimensions/metrics suitable for custom analysis.
• Incremental transfers and backfills.
• Less engineering/maintenance than managing each ad platform's API yourself. Supermetrics Supermetrics
• Custom connectors are also possible if you eventually need a source they don't natively support. Supermetrics

Strong alternative: fivetran.com

I'd choose Fivetran if your organization already has Fivetran as its standard ELT platform or if engineering/data-platform governance is more important than marketing-specific usability.

Its Facebook Ads connector exposes a fairly deep schema, and importantly, you can configure custom reports with specific fields, breakdowns, attribution windows, aggregation, and reporting level. Fivetran Fivetran

That makes it attractive for a data team that wants to treat ad platforms as another source in a broader ELT architecture.

Another option: funnel.io

Funnel is worth considering if the primary users are marketing/analytics teams rather than data engineers. It supports social/ad platforms and can load the resulting data into a warehouse, while also providing reporting/analysis functionality. Funnel

Quick decision

For a new stack, I'd start with Supermetrics and validate one representative use case before committing: e.g. Meta + TikTok → Snowflake/BigQuery, pulling ad/creative × placement × audience × day with spend, impressions, clicks, conversions, and attribution metrics. The key thing to test is not the headline connector count, but whether the exact breakdowns and historical/backfill behavior you need are available without unacceptable API limitations.

If your goal is exporting granular social ad data into a warehouse for custom SQL analysis, I’d look at a few categories of platforms rather than just dashboard tools. The best choice depends on whether you need raw ad-level data, multi-channel normalization, or managed attribution.

Strong options

1. fivetran.com — best enterprise ELT default

Best for: Data teams that already run Snowflake, BigQuery, Redshift, or Databricks.

Why consider it:

• Mature ELT pipelines and monitoring
• Broad connector ecosystem (Meta Ads, TikTok Ads, LinkedIn Ads, Google Ads, etc.)
• Handles schema changes, incremental syncs, and pipeline reliability
• Good fit when analysts want to model data themselves in dbt

Tradeoff:

• Can get expensive at high sync volumes
• Usually provides platform-level tables rather than true user-level attribution data

2. stitchdata.com — simpler warehouse replication

Best for: Smaller data teams wanting straightforward replication.

Stitch is designed to move marketing and other SaaS data into warehouses, including social advertising sources, with less engineering overhead. Stitch Data Stitch Data

Tradeoff:

• Less enterprise-heavy than Fivetran
• You may need more transformation work downstream

3. owox.com — best marketing analytics workflow

Best for: Marketing teams combining ad spend with GA4, CRM, ecommerce, and revenue data.

OWOX focuses heavily on marketing data pipelines, bringing sources like Meta Ads, TikTok Ads, LinkedIn Ads, and Google Ads into warehouses such as BigQuery, Snowflake, Databricks, Redshift, and Athena. Owox

Tradeoff:

• More opinionated toward marketing analytics use cases
• Less of a general-purpose ELT platform

4. cdata.com — best for broad destination flexibility

Best for: Enterprises with unusual databases, lakes, or hybrid environments.

CData Sync supports replicating ad data sources into many databases, data lakes, and warehouses. CData Software

Tradeoff:

• More infrastructure-oriented; less marketing-specific modeling

5. attributionapp.com — best if “granular” means user-level marketing journeys

Best for: Teams doing attribution modeling, incrementality analysis, or customer journey analysis.

Attribution offers warehouse exports focused on full-fidelity marketing data, including visit/event-level data rather than only aggregated campaign reporting. Attribution

Tradeoff:

• More specialized than a pure ad-platform connector

My shortlist by use case

For most companies building custom social ad analysis, I’d start with Fivetran or OWOX feeding Snowflake/BigQuery, then build your own models for:

• campaign → ad → creative hierarchy
• spend pacing
• CAC/ROAS
• cohort performance
• creative fatigue
• audience segment analysis
• incrementality tests

A key question: do you need ad-platform granularity (campaign/ad/ad set metrics), or user-level attribution data (impression → click → conversion → customer journey)? That distinction changes the best platform quite a bit.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist platforms based on how much control your analytics team needs, the number of ad networks you run, and whether you want managed ETL vs. building pipelines.

Recommended platforms

My top picks by scenario

1. You want a production-grade analytics pipeline

Choose Fivetran.

Good fit if you need:

• Meta Ads, TikTok Ads, LinkedIn Ads, Google Ads, etc. feeding Snowflake/BigQuery/Redshift
• automated schema management
• minimal maintenance
• analysts querying clean tables

2. You want maximum flexibility and lower platform lock-in

Choose Airbyte.

Good fit if you have engineers who want:

• custom connectors
• control over sync logic
• self-hosting options
• ownership of transformations

3. You are a marketing analytics team rather than a data engineering team

Choose Supermetrics, Adverity, or Dataslayer.

These are optimized for:

• campaign reporting
• spend/ROAS analysis
• creative performance
• cross-channel dashboards

4. You need very granular ad-level analysis

Look for platforms that expose:

• campaign → ad set → ad hierarchy
• creative metadata
• placement breakdowns
• audience/demographic breakdowns
• daily performance facts
• conversion events
• attribution windows
• raw API fields where possible

For example, some connectors specifically expose Meta Ads campaign, ad set, ad, creative, placement, demographic, and conversion-level tables rather than only summarized reports. Datawarehouse.io Datawarehouse.io

A practical warehouse architecture

A common setup:

Meta Ads / TikTok Ads / LinkedIn Ads
              |
              v
        ETL/ELT connector
              |
              v
      Snowflake / BigQuery / Redshift
              |
              v
     dbt transformations
              |
              v
   BI + custom ML/analytics

I’d avoid tools that only export CSV reports if your goal is deep analysis. You’ll usually want normalized tables that let you join:

• ad spend
• impressions/clicks
• creatives
• customer conversions
• CRM revenue
• product data
• attribution models

My default recommendation

For most companies:

• Small analytics team: Supermetrics or Dataslayer
• Growing data team: Airbyte or Stitch
• Enterprise marketing analytics: Fivetran or Adverity
• BigQuery-centric marketing analytics: OWOX is worth evaluating because it is built around warehouse collection and marketing data marts. Owox

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks, etc.) and ad platforms (Meta, TikTok, LinkedIn, Snapchat, Reddit, X, Pinterest), I can narrow this to 2–3 best options.

If your priority is granular social/ad data in your warehouse for your own SQL/dbt analysis, I’d shortlist these:

• supermetrics.com — my default recommendation. It is purpose-built for marketing data and supports 170+ sources, including Meta/Facebook Ads, TikTok Ads, LinkedIn Ads, Pinterest, Snapchat, Reddit, and Google Ads. It can automatically load data into BigQuery, Snowflake, Redshift, and other warehouses, with incremental loading and configurable dimensions/metrics. Supermetrics Supermetrics Supermetrics
• fivetran.com — best if your data engineering team already uses Fivetran. Its Facebook Ads connector is fully managed and designed to replicate detailed ad data into a warehouse/lake. Fivetran also has an ad-reporting dbt package that standardizes data across Facebook, Google, TikTok, LinkedIn, Reddit, Snapchat, and other ad platforms while retaining platform-specific fields for deeper analysis. Fivetran Fivetran

How I'd choose

One important caveat: “granular” doesn't mean raw impression-level/user-level data. Ad platforms generally expose reporting APIs with pre-aggregated dimensions and metrics rather than unrestricted raw event data. Fivetran So I'd evaluate vendors specifically on whether they expose the platform dimensions and breakdowns you actually need—campaign → ad set/ad → placement → device → geography, creative IDs, actions/conversions, etc.

For a typical modern warehouse stack, I'd start with Supermetrics unless you already have Fivetran standardized across your data estate. It has particularly broad coverage of paid-social sources and explicit warehouse destinations. Supermetrics Documentation

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and the ad platforms you use, I can give you a tighter Supermetrics vs. Fivetran vs. Airbyte recommendation, including likely cost and data-granularity tradeoffs.

For granular social/ad data → data warehouse → custom SQL/BI analysis, I’d shortlist Funnel and Supermetrics, with Funnel as my default recommendation.

My pick: funnel.io

Funnel is particularly well suited if your goal is to get campaign/ad-set/ad/creative-level data from multiple advertising platforms into a warehouse and then do your own modeling.

• Granular dimensions + metrics: you can choose the dimensions and metrics you export rather than being restricted to a prebuilt reporting schema. Funnel Help Funnel Help
• Broad marketing coverage: Funnel currently advertises 600+ marketing connectors, which is useful if your stack extends beyond Meta/Google/TikTok. Funnel
• Warehouse destinations: supports destinations including BigQuery, Snowflake, Redshift, S3, and others. Funnel Help Funnel Help
• Custom transformations: you can create custom dimensions/metrics and transformations before the data reaches downstream analysis. Funnel Funnel
• Creative data: Funnel has a dedicated creative-export capability, which is attractive if you want to analyze performance by creative attributes rather than just campaign hierarchy. Funnel Help

Strong alternative: supermetrics.com

I'd choose Supermetrics if you prioritize maximum control over individual source fields and have a more conventional marketing-data stack. It supports automated warehouse transfers across 170+ marketing platforms and destinations including BigQuery, Snowflake, and Redshift. Supermetrics

It has native connectors for major ad sources including Facebook/Instagram Ads, Google Ads, TikTok Ads, and LinkedIn Ads, and its warehouse tooling supports incremental loading. Supermetrics Supermetrics Supermetrics Supermetrics

How I'd decide

One caveat: neither platform magically gives you fields the underlying ad API doesn't expose. For genuinely granular analysis—especially creative, placement, audience, attribution, and breakdown-level data—I'd validate the exact fields available for your specific Meta/TikTok/Google accounts before signing a contract.

If you tell me your warehouse (Snowflake/BigQuery/Redshift/etc.) and the ad platforms (Meta, TikTok, Google, LinkedIn, Reddit, etc.), I can give you a more specific recommendation and compare Funnel vs. Supermetrics vs. Airbyte/Census-style alternatives for your stack.

For granular social ad data exports into a data warehouse for custom analysis, I’d shortlist these platforms. The right choice depends on whether you want a managed ELT pipeline, maximum raw granularity, or a marketing-focused semantic layer.

Google Data Studio Porter### My recommendations by scenario

1. You have a data engineering team and want the most flexible warehouse model

• Pick Fivetran or Airbyte.
• Land raw ad objects/events into Snowflake, BigQuery, Redshift, or Databricks.
• Build your own models for: - campaign/ad set/ad hierarchy
• creative performance
• attribution windows
• audience breakdowns
• spend pacing
• incremental lift experiments

2. You are a marketing analytics team that wants data quickly

• Pick Funnel or Supermetrics.
• They are designed around paid media workflows and reduce API maintenance. Google Data Studio

3. You need Meta + TikTok + LinkedIn + Google Ads unified for BI

• Look at Funnel, Windsor.ai, or Porter Metrics. Several of these platforms provide connectors across multiple advertising ecosystems and warehouse destinations. SpendMix Porter

Data model I’d aim for in the warehouse

Regardless of tool, I’d avoid only importing summarized reports. Try to capture:

• ad_account
• campaign
• ad_set / ad_group
• ad
• creative
• daily_performance - impressions
• clicks
• spend
• conversions
• revenue
• video views
• conversion_events
• audience_breakdowns
• placement_breakdowns
• device/geography breakdowns

For custom analysis, the key question is whether the platform exposes raw API dimensions rather than only prebuilt reporting metrics.

If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.), ad platforms (Meta/TikTok/LinkedIn/X/etc.), and expected scale (accounts, daily rows), I can narrow this to a top 2–3 choice.

If your priority is granular social-ad data in a warehouse for your own SQL/modeling, I’d shortlist Fivetran and Supermetrics, with Fivetran as my default recommendation.

My pick: fivetran.com

Fivetran is particularly strong when the warehouse is the center of your analytics stack. Its Meta/Facebook Ads connector pulls campaign, ad set, ad, creative, and Insights data into warehouses such as Snowflake, BigQuery, Databricks, and others. It also supports custom reports with selectable breakdowns, action breakdowns, attribution windows, and aggregation levels. Fivetran Fivetran

That makes it a good fit if you want to do things like:

• Analyze performance at ad × placement × geography × demographic grain.
• Join ad data with CRM, orders, product, and web analytics.
• Build your own attribution or incrementality models.
• Preserve relatively raw source data and control transformations in SQL/dbt.
• Standardize multiple ad platforms inside one warehouse.

Fivetran also has dbt-compatible Facebook Ads models, which can give you a cleaner analytics layer on top of the underlying data. Fivetran

When I'd choose Supermetrics instead

supermetrics.com is arguably more attractive if you're primarily a marketing analytics team and want lots of ready-made dimensions/metrics without as much data-engineering work.

For example, Supermetrics currently documents 537 Facebook Ads metrics and 254 dimensions, with warehouse destinations including BigQuery, Snowflake, Redshift, and Azure. Supermetrics It also supports a broad set of marketing sources, which can be useful if you're building a cross-channel marketing warehouse rather than a general-purpose ELT platform. Supermetrics

Quick decision

Bottom line: If you have a data team and already operate Snowflake/BigQuery/Databricks, I'd start with Fivetran. If the people configuring the pipeline are primarily marketers/analysts and you want a large catalog of marketing-specific fields with minimal engineering, I'd evaluate Supermetrics first.

One important caveat: with social APIs, “granular” doesn't mean every combination of dimensions is necessarily available. Meta imposes compatibility/availability constraints on certain breakdowns and metrics, so I'd validate your exact required grain—e.g. ad × day × placement × country × age × gender—during the evaluation rather than relying on a vendor's field count. Fivetran Supermetrics

If you tell me which social platforms (Meta, TikTok, LinkedIn, Snapchat, Pinterest, etc.) and which warehouse (Snowflake, BigQuery, Databricks, Redshift), I can narrow this to the best 2–3 options for your exact stack.

If your priority is granular paid-social data → warehouse → custom SQL/analysis, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default recommendation.

My pick: supermetrics.com

It’s particularly strong when you want to get close to the underlying ad-platform reporting data without building and maintaining APIs yourself.

• Granular breakdowns: Meta/Facebook data can be pulled at campaign, ad-set, and creative level, with audience and placement dimensions. Supermetrics
• Broad social coverage: Meta, TikTok, LinkedIn, Pinterest, Snapchat, etc., alongside Google Ads and other marketing sources. Its API currently lists a large set of supported sources. Supermetrics Supermetrics
• Warehouse destinations: BigQuery, Snowflake, Redshift, Azure Synapse and others. Supermetrics
• Good fit for data teams: You can schedule transfers and select metrics/dimensions rather than being locked into a prebuilt dashboard. Supermetrics
• API option: If you eventually want to build your own ingestion layer or internal analytics product, Supermetrics also exposes a unified marketing-data API. Supermetrics

Strong alternative: funnel.io

I'd favor Funnel if you want more marketing-data modeling/governance in addition to extraction. Funnel says it has 600+ connectors, stores original data, applies transformations/business rules, and exports the resulting data to warehouses and BI tools. Funnel Funnel

Bottom line: For a data warehouse team doing custom analysis—especially if you're thinking Snowflake/BigQuery + dbt + your own attribution/ROAS models—I'd start with Supermetrics. The important thing is to validate the exact dimensions and breakdowns you need (especially Meta attribution, placement, creative, geo, age/gender, and action/conversion fields) before committing, because "granular" varies considerably by platform/API.

If you tell me which warehouse (Snowflake, BigQuery, Databricks, etc.) and which ad platforms, I can narrow this down to the best 2–3 options and compare field-level granularity, historical backfill, refresh frequency, and likely cost.

If your priority is granular social-ad data in a warehouse for custom SQL/BI analysis, I’d recommend fivetran.com.

Why Fivetran is my first choice

• Good raw-data access: its Facebook Ads connector is designed to replicate detailed source data into your warehouse rather than just producing prebuilt dashboards. Fivetran
• Broad ad-platform coverage: Fivetran supports Facebook/Meta, Google, TikTok, LinkedIn, Pinterest, Reddit, Snapchat, X, Amazon Ads and others. Its ad-reporting dbt package can standardize these sources for cross-channel analysis. GitHub
• Works with common warehouses: including Snowflake, BigQuery, Redshift, Databricks and Postgres. GitHub
• Useful dbt layer: Fivetran provides models at account, campaign, ad group/ad and other grains, while retaining platform-specific data for deeper analysis. GitHub GitHub
• Less pipeline maintenance: it handles API authentication, incremental loading, schema changes and other ingestion plumbing. Fivetran

The main alternative: Supermetrics

supermetrics.com is worth considering if your team is especially marketing/analytics-oriented. It offers 170+ marketing-data sources and direct transfers to BigQuery, Snowflake, Redshift, Databricks and other warehouses. Supermetrics

I'd lean Fivetran when your data engineering team wants raw-ish, granular source data + dbt/modeling control. I'd lean Supermetrics when you want a more marketing-centric, no-code data-transfer experience.

One caveat: "granular" is constrained by each ad platform's API. For example, Meta has historical limits on certain breakdowns, hourly data, reach, and unique counts, so the connector can't export information the underlying API no longer exposes. Supermetrics

My shortlist:

1. Fivetran — best overall for a serious warehouse/data-team stack
2. Supermetrics — best marketing-focused alternative
3. Custom API ingestion — best only if you need fields/granularity the connectors don't expose and have engineering capacity

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and which ad platforms (Meta, Google, TikTok, LinkedIn, etc.), I can narrow this down and compare Fivetran vs. Supermetrics vs. Airbyte specifically on granularity, refresh frequency, historical backfill, and cost.

If your priority is granular social ad data in a warehouse for custom SQL/modeling, my first recommendation is supermetrics.com.

Why I’d pick Supermetrics

• Strong warehouse-first workflow: It automatically transfers marketing data into BigQuery, Snowflake, Redshift, and other warehouses, with incremental loading and managed schemas. Supermetrics
• Good social coverage: Facebook/Instagram Ads, LinkedIn Ads, TikTok and other marketing sources are supported, so you can centralize cross-channel data rather than build separate pipelines. Supermetrics Supermetrics
• Granularity: For Meta specifically, you can work with detailed dimensions and metrics rather than just high-level campaign totals. Supermetrics also exposes the underlying connector fields for custom reporting. Supermetrics
• Less engineering: It handles authentication, API extraction, scheduling and loading, leaving your data team to do the interesting work in the warehouse.

The main alternative: Adverity

I'd choose adverity.com instead if you're building a more sophisticated marketing-data integration/governance layer. It supports 600+ connectors, transformations, data mapping, and warehouse destinations. Its Meta connector explicitly supports account/campaign/ad-set/ad levels, breakdowns such as age, gender, country and placement, and custom daily granularity. Adverity Adverity Documentation

Bottom line: For a team saying “get granular Meta/TikTok/LinkedIn/etc. ad data into Snowflake/BigQuery and let us analyze it ourselves”, I'd start with Supermetrics. If you're building a centralized enterprise marketing-data platform with substantial transformation and governance requirements, I'd evaluate Adverity alongside it.

One important caveat: the APIs themselves impose historical/breakdown limits. For example, Meta currently limits some breakdowned reach data to the most recent 13 months and ad-insights data to 37 months, so no connector can magically preserve data that the source API won't provide. Supermetrics Adverity Documentation

For your use case, I’d shortlist supermetrics.com first.

It’s particularly well suited when the goal is raw-ish, granular paid-social data in your warehouse rather than another reporting dashboard:

• Broad social/ad coverage: Facebook Ads, Instagram Insights, TikTok Ads, LinkedIn Ads, Pinterest, Reddit, Snapchat, X, etc. are supported. Supermetrics Documentation
• Warehouse-native delivery: it supports destinations including BigQuery, Snowflake, Redshift, S3 and Google Cloud Storage, with automated transfers and incremental loading. Supermetrics Documentation Supermetrics
• Granularity/control: Supermetrics exposes field lists and lets you select the dimensions and metrics you want, which is important if you're doing your own modeling rather than accepting a vendor's predefined reporting layer. Supermetrics Documentation
• Good fit for custom analysis: you can centralize multiple ad platforms and then do your own attribution, cohorting, creative analysis, spend normalization, etc. in SQL/dbt rather than being constrained by the connector's UI.

One strong alternative: Funnel

funnel.io is the other one I'd seriously evaluate. It currently advertises 600+ connectors, supports warehouse destinations such as BigQuery, Snowflake and Redshift, and lets you configure specific metrics/dimensions and filters for each data source. Funnel Funnel Knowledge Base

I'd lean Supermetrics if your priority is data engineering / warehouse extraction and analytical control. I'd lean Funnel if you also want a more complete marketing-data management/reporting layer around the warehouse.

My pick: Supermetrics, assuming your main requirement is “get as much useful ad-level data as the APIs permit into our warehouse reliably, then let our data team own the analysis.”

One caveat: neither platform can magically provide fields that the underlying ad-platform APIs don't expose, so I'd validate the exact dimensions/metrics at your desired grain (campaign → ad set → ad/creative, breakdowns by placement, age, geography, etc.) before committing. Supermetrics publishes field lists for its sources, which makes that validation relatively straightforward. Supermetrics Documentation

If your priority is granular social/ad-platform data → warehouse → custom SQL/BI analysis, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default recommendation.

My pick: Supermetrics

supermetrics.com is particularly strong if you want broad platform coverage and control over the fields you ingest.

• Very broad source coverage: Facebook/Meta Ads, TikTok Ads, LinkedIn Ads, Pinterest Ads, Reddit Ads, Snapchat, X Ads, Google Ads, etc. Supermetrics Supermetrics Docs
• Warehouse-native workflow: supports BigQuery, Snowflake, Redshift, Azure Synapse, Databricks and other destinations. Supermetrics
• Granularity: its TikTok connector, for example, currently exposes hundreds of metrics and dimensions and multiple table groups such as AD, AGE_GENDER, and GEO. Supermetrics Docs
• Incremental loading: designed to transfer only new/updated data rather than repeatedly rebuilding everything. Supermetrics
• API access: useful if you eventually want to orchestrate or customize extraction beyond the standard warehouse transfers. Supermetrics Docs

That makes it a good fit for analyses like ad → ad set → campaign → creative → placement → demographic/geography, followed by your own attribution, incrementality, CAC, LTV, or cross-channel models in the warehouse.

Strong alternative: Funnel

funnel.io is arguably better if you want a more opinionated marketing-data layer rather than primarily an extraction service.

Funnel lets you configure data sources by selecting the metrics, dimensions, filters, and granularity you want, then export them to warehouses such as BigQuery, Snowflake, and Redshift. Funnel Help Funnel Help It also has a very large connector catalog—currently advertising 635 connectors. Funnel

How I'd choose

Bottom line: I'd start with Supermetrics if your data warehouse is the source of truth and your analytics/data team wants raw-ish, granular platform data to model themselves. I'd choose Funnel if marketing operations and standardized cross-channel reporting are equally important.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and the ad platforms you use (Meta, TikTok, Google, LinkedIn, Reddit, etc.), I can narrow this down to the best 2–3 options and compare field-level granularity, historical backfill, refresh frequency, and likely cost.

If your priority is granular social-ad data in your warehouse for custom SQL/BI analysis, I’d shortlist these:

My recommendation: Fivetran

For a team saying “give us the most granular data possible and we'll do the analysis ourselves,” I'd start with Fivetran.

Its Facebook Ads connector, for example, is designed to extract a deep level of source data into your warehouse, rather than forcing you into a prebuilt dashboard/reporting model. Fivetran Fivetran also has connectors/models covering Facebook, Google, LinkedIn, TikTok, Reddit, Snapchat, Pinterest, Amazon Ads and others, with a dbt package for standardizing cross-platform ad reporting. Fivetran GitHub

That makes a stack like:

Meta / TikTok / LinkedIn / Google Ads → Fivetran → Snowflake/BigQuery/Databricks → dbt → your analysis

particularly attractive.

When I'd pick Supermetrics instead

Choose Supermetrics if your organization is more marketing-led and you want less engineering involvement. It supports 170+ marketing sources and handles schema management, incremental loading and transfers into warehouses such as BigQuery, Snowflake, Redshift and Databricks. Supermetrics

It's also particularly good for social-ad dimensions: its Meta connector exposes fields ranging from spend/reach/engagement through creative-level and audience-performance breakdowns. Supermetrics

When I'd pick Adverity

I'd look at Adverity Connect if you're an enterprise with lots of advertising platforms, agencies, brands or markets. It currently advertises 600+ connectors, transformation/harmonization capabilities, data-quality monitoring, and delivery to warehouses including Snowflake, BigQuery, Databricks, Redshift and others. Adverity

Bottom line:

• Data team + maximum analytical control → Fivetran
• Marketing team + easy setup + broad marketing coverage → Supermetrics
• Enterprise multi-brand/multi-platform governance → Adverity

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and which ad platforms (Meta, TikTok, LinkedIn, Google, Reddit, etc.), I can narrow this down to the best 1–2 options and compare field-level granularity, historical backfill, API limitations, refresh frequency, and likely cost.

For a team saying “give us the most granular data possible and we'll do the analysis ourselves,” I'd start with Fivetran.

Its Facebook Ads connector, for example, is designed to extract a deep level of source data into your warehouse, rather than forcing you into a prebuilt dashboard/reporting model. Fivetran Fivetran also has connectors/models covering Facebook, Google, LinkedIn, TikTok, Reddit, Snapchat, Pinterest, Amazon Ads and others, with a dbt package for standardizing cross-platform ad reporting. Fivetran GitHub

That makes a stack like:

Meta / TikTok / LinkedIn / Google Ads → Fivetran → Snowflake/BigQuery/Databricks → dbt → your analysis

particularly attractive.

When I'd pick Supermetrics instead

Choose Supermetrics if your organization is more marketing-led and you want less engineering involvement. It supports 170+ marketing sources and handles schema management, incremental loading and transfers into warehouses such as BigQuery, Snowflake, Redshift and Databricks. supermetrics.com

It's also particularly good for social-ad dimensions: its Meta connector exposes fields ranging from spend/reach/engagement through creative-level and audience-performance breakdowns.

When I'd pick Adverity

I'd look at Adverity Connect if you're an enterprise with lots of advertising platforms, agencies, brands or markets. It currently advertises 600+ connectors, transformation/harmonization capabilities, data-quality monitoring, and delivery to warehouses including Snowflake, BigQuery, Databricks, Redshift and others.

Bottom line:

For granular social-ad data → data warehouse → custom SQL/analytics, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default recommendation.

My pick: Supermetrics

supermetrics.com is particularly well suited if your data team wants control over the raw-ish marketing data rather than being locked into a reporting UI.

• Granularity: Meta/Facebook Ads supports campaign, ad set, and creative-level data, plus audience and placement breakdowns. Supermetrics
• Warehouse destinations: BigQuery, Snowflake, Redshift, Databricks, Azure Synapse, etc. Supermetrics Supermetrics
• Broad social coverage: Facebook/Instagram, LinkedIn, TikTok and other major marketing platforms are available. Supermetrics Supermetrics
• Good for custom analysis: it supports selecting dimensions/metrics, transformations, incremental loading, and cross-source analysis in the warehouse. Supermetrics Supermetrics
• Scale: Supermetrics currently advertises 170+ marketing data sources for warehouse transfers. Supermetrics

Strong alternative: Funnel

funnel.io is worth evaluating if you have a large number of ad platforms/accounts. It advertises 600+ marketing data sources and supports warehouse exports including BigQuery, Snowflake and Redshift. Funnel Funnel Help

One particularly interesting feature is Creative Exports, which can deliver creative assets and metadata to object storage for downstream analytics. Funnel Help Funnel also says it doesn't charge based on data volume/row count, though its pricing uses flexpoints for connectors, accounts and destinations. Funnel

How I'd choose

Bottom line: Start with Supermetrics if the core requirement is “get granular ad-platform data into Snowflake/BigQuery and let our analysts do whatever they want with it.” I'd run a proof of concept against your most important platform—probably Meta—and specifically verify the dimensions/breakdowns, historical backfill, data freshness, API limitations, and handling of deleted/renamed ads before committing.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist these platforms:

My recommendation by scenario

1. Enterprise / high-volume / serious custom modeling → Fivetran

• Good choice if you have Snowflake, BigQuery, Redshift, Databricks, or a similar warehouse.
• It can replicate ad platform data into your warehouse and supports advertising connectors including Meta/Facebook Ads and other platforms. www.fivetran.com
• Strong fit if your analysts want to join ad data with CRM, product, revenue, and attribution models.
• Pair well with dbt for standardized campaign/ad-level models; Fivetran provides ad reporting transformation models across multiple ad platforms. fivetran.com

2. Marketing team owns analytics, engineering support is limited → Supermetrics

• Strong choice if the main users are growth/marketing analysts.
• It supports automated transfers of marketing data into warehouses such as BigQuery, Snowflake, Redshift, and others, with incremental loading and normalized schemas. supermetrics.com
• Good coverage for social platforms and marketing sources. docs.supermetrics.com

3. Data engineering team wants flexibility → Airbyte

• Better if you want to customize connectors, run infrastructure yourself, or avoid vendor lock-in.
• Expect more work around monitoring, schema changes, and connector maintenance.

Features I’d evaluate before choosing

For “granular” social ad analysis, verify the platform can extract:

• Campaign → ad set/ad → creative hierarchy
• Daily breakdowns by:
  • campaign
  • placement
  • audience
  • geography
  • device
  • creative
• Spend, impressions, reach, clicks, conversions
• Video metrics (3s views, completion rates, watch time)
• Attribution windows and conversion breakdowns
• Historical backfills
• API quota handling
• Schema-change management

A common architecture

A setup I see work well:

Meta Ads / TikTok Ads / LinkedIn Ads / Reddit Ads
→ Fivetran or Supermetrics
→ Snowflake / BigQuery / Databricks
→ dbt transformations
→ Looker / Tableau / Power BI / custom analysis

If you expect to build custom attribution models, incrementality analysis, creative fatigue models, or MMM, I’d lean toward Fivetran + dbt. If you mainly need campaign reporting and analyst self-service, Supermetrics is often faster to deploy. supermetrics.com

A couple of details would change the recommendation: which warehouse are you using (Snowflake, BigQuery, Databricks, etc.), and which ad platforms matter most (Meta, TikTok, LinkedIn, Reddit, X, Pinterest)?

For this use case, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default recommendation.

My pick: Supermetrics

It’s particularly well suited if your priority is getting granular paid-social data into a warehouse and then doing your own analysis in SQL/dbt/etc.

Why:

• Broad ad-platform coverage: Facebook/Meta, TikTok, LinkedIn, Pinterest, Reddit, Snapchat, Google Ads, etc. are supported. supermetrics.com
• Warehouse-native delivery: supports BigQuery, Snowflake, Redshift, Azure Synapse, Databricks and other destinations. supermetrics.comfunnel.io
• Granular dimensions/metrics: you can select fields and build transfers around the level of detail you need rather than being limited to a canned reporting schema. docs.supermetrics.com
• Incremental loading and backfills: useful when you're maintaining a durable analytical dataset rather than just refreshing a dashboard. supermetrics.com
• It also handles the API/schema plumbing, so your data team doesn't have to maintain individual ad-platform integrations. supermetrics.comfunnel.io

Strong alternative: Funnel

I'd lean toward Funnel if you want a more marketing-data-centric transformation layer before the warehouse. It supports hundreds of connectors and lets you configure custom dimensions/metrics, while exporting to destinations including BigQuery, Snowflake and Redshift. funnel.io

How I'd choose

One caveat: don't choose based merely on the number of connectors. For your use case, I'd validate the exact Meta/TikTok/LinkedIn fields and breakdowns you need, historical backfill limits, API refresh latency, and how each tool handles platform attribution changes. Those details matter much more than connector count.

If you tell me your warehouse (Snowflake/BigQuery/Redshift/etc.) and which ad platforms you use, I can give you a tighter 2–3 platform comparison, including likely architecture and tradeoffs.

If your priority is granular social-ad data in a warehouse for custom SQL/BI analysis, I’d shortlist Fivetran and Airbyte, with Fivetran as my default recommendation.

My pick: Fivetran

Fivetran is a strong fit if you want a managed, low-maintenance pipeline. Its Meta/Facebook Ads connector supports deep source replication and custom reports, including configurable fields, attribution windows, breakdowns, action breakdowns, and aggregation. www.fivetran.comgithub.com

The important bit for your use case is that it doesn't just dump a high-level campaign summary. Fivetran's connector supports granular structures such as ad-level insights and secondary tables for metrics like actions, action_values, and cost_per_action_type, preserving breakdown dimensions so you can do your own analysis downstream. beta.fivetran.com

It also supports common warehouse destinations including Snowflake, BigQuery, Databricks, Redshift, and S3. www.fivetran.com

When I'd choose Airbyte instead

Airbyte is compelling if your data team wants more control over extraction and schema or expects to build/customize connectors. Its Facebook Marketing connector lets you define custom Insights streams with a chosen account/adset/campaign/ad grain, fields, breakdowns, and action breakdowns. github.com

That's particularly attractive if "granular" means we want to control exactly what comes out of the Marketing API, rather than simply getting a vendor's normalized model.

Quick decision

Fivetran also has a Google Ads connector, so if you're trying to get Meta + Google + other paid channels into one warehouse, it becomes especially attractive. www.fivetran.com

One caveat: before buying, I'd test your actual required grain and dimensions against the connector. Meta's API has combinations of fields/breakdowns that aren't all simultaneously supported, so "supports Meta Ads" doesn't necessarily mean "can reproduce every report you can construct in Ads Manager." Airbyte explicitly exposes these custom-insight controls, while Fivetran exposes custom reports. www.fivetran.comgithub.com

If you tell me which social platforms (Meta, TikTok, LinkedIn, Pinterest, Snapchat, etc.) and which warehouse (Snowflake, BigQuery, Databricks, Redshift), I can narrow this to the best 2–3 options for your exact stack.

If your priority is granular social ad data → warehouse → custom SQL/analytics, my first choice would be Supermetrics.

Why I’d pick Supermetrics

• Broad paid-social coverage: Meta/Facebook Ads, TikTok Ads, LinkedIn Ads, Pinterest, Snapchat, Reddit, X, etc., plus Google Ads and other marketing sources. supermetrics.com
• Warehouse-native workflow: It supports BigQuery, Snowflake, Redshift, Azure Synapse, Databricks and others, with automated transfers and incremental loading. supermetrics.com
• Good granularity: For Meta specifically, its current connector exposes hundreds of metrics/dimensions and tables at campaign, ad set, ad, video, geo, age/gender, etc. levels. docs.supermetrics.com
• Useful for custom analysis: Once landed in your warehouse, you can join ad data to CRM, revenue, GA4, product, or attribution data and build your own metrics/models rather than being constrained by the ad platform UI. supermetrics.com
• Less engineering overhead: It handles API authentication, schema management, incremental loads, and error recovery rather than requiring your team to maintain individual ad-platform pipelines. supermetrics.com

The main caveat: I'd validate the exact fields you consider "granular" before buying. Ad APIs have platform-specific limitations and fields disappear/change over time; Meta, for example, has made recent changes to historical data, attribution windows, and breakdown availability. docs.supermetrics.com

My shortlist

For a team saying "we want raw-ish, granular ad data in Snowflake/BigQuery and then we'll do our own analysis", I'd start with Supermetrics, then compare it against Funnel on field coverage, historical backfill, API freshness, and cost at your expected row/volume level.

If you tell me your warehouse (Snowflake/BigQuery/Redshift/etc.) and the ad platforms you use (Meta, TikTok, Google, LinkedIn, etc.), I can narrow this down to the best 2–3 options and compare their granularity and likely tradeoffs.

For exporting granular social ad data into a data warehouse for custom analysis, I’d shortlist platforms based on how much control your data team wants versus how much managed transformation you need.

My recommendation by scenario

1. You have a data engineering team and want maximum analytical flexibility

• Choose Fivetran, OWOX, or Weld.
• Land the rawest available data in your warehouse.
• Build your own models for:
  • campaign → ad set → ad hierarchy
  • creative performance
  • audience/placement breakdowns
  • attribution joins with CRM and revenue data

2. You are a marketing analytics team without much engineering support

• Choose Adverity or a similar marketing-focused platform.
• You’ll get more normalization across Meta, TikTok, LinkedIn, Google, etc., without building as much transformation logic. www.adverity.com

3. You need very granular Meta/TikTok/LinkedIn data
Prioritize platforms that expose:

• ad-level objects (not only campaign summaries)
• creative IDs and metadata
• placement/device/demographic breakdowns
• conversion action arrays
• historical backfills
• raw API fields when possible

For example, some connectors explicitly support granular Meta structures such as campaigns, ad sets, ads, creatives, placements, demographics, and conversion metrics. datawarehouse.io

Architecture I’d generally recommend

Social APIs
   |
   v
ELT connector (Fivetran / OWOX / Weld / Adverity)
   |
   v
Cloud warehouse
(Snowflake / BigQuery / Databricks / Redshift)
   |
   v
dbt models
   |
   v
BI + custom analysis + attribution models

If this is for a serious analytics stack, I’d lean toward OWOX or Weld for warehouse-native teams, or Adverity for a marketing operations organization that needs standardized reporting across many channels. weld.appwww.owox.com

A key question that changes the recommendation: are you primarily pulling Meta/TikTok/LinkedIn ads, or do you need all paid media + CRM/revenue joins for attribution?

For granular social ad data → your data warehouse → custom analysis, I’d shortlist these three:

My recommendation

I'd start with Fivetran if your priority is genuinely granular, warehouse-native analysis. It is designed around replication into your warehouse rather than primarily around marketing reporting, which makes it a better architectural fit when your team wants to build its own attribution, incrementality, cohort, LTV, or creative-level models. www.fivetran.com

Choose Supermetrics instead if marketers need more control over the available ad-platform dimensions/metrics and you want a marketing-data specialist rather than a general-purpose ELT platform. Its documented Facebook Ads coverage is particularly extensive. supermetrics.comdocs.supermetrics.com

One important thing to evaluate in a POC: don't just compare connector counts. Test the exact fields you care about—ad/ad-set/campaign grain, creative IDs, placement, geography, age/gender, hourly data, conversion/action types, attribution windows, and historical backfill—and verify how each vendor handles API changes and Meta/TikTok reporting limitations.

If you tell me your warehouse (Snowflake/BigQuery/Redshift/etc.) and the social platforms (Meta, TikTok, LinkedIn, Pinterest, Reddit, etc.), I can narrow this to the best 1–2 options and compare granularity, historical backfill, pricing model, and raw-vs-modeled data.

If your priority is granular paid-social data → warehouse → custom SQL/BI analysis, my first recommendation is Funnel.

Why Funnel

• Broad social coverage: Facebook/Instagram, TikTok, LinkedIn, Pinterest, Snapchat, Reddit, etc., with hundreds of marketing connectors. funnel.io
• Warehouse-native exports: supports destinations including BigQuery, Snowflake, Redshift, Databricks, SQL databases, and cloud storage. funnel.io
• Granularity: you can select the dimensions and metrics you want exported rather than being limited to dashboard-level aggregates. funnel.iohelp.funnel.iohelp.funnel.iosupermetrics.com
• Creative-level data: Funnel can export creative assets and metadata—including Facebook Ads at the ad/creative level—to S3, GCS, or Azure Blob for downstream analysis. help.funnel.io
• Good fit for an analytics team: it gives you a managed ingestion/normalization layer without forcing your engineers to maintain individual ad-platform APIs.

One important caveat: “granular” doesn't necessarily mean raw impression/click event data. Ad platforms generally expose reporting data that's already aggregated by dimensions such as date, campaign, ad set, and ad. Funnel explicitly notes this limitation. help.funnel.io

Runner-up: Supermetrics

Supermetrics is probably the strongest alternative. It advertises 170+ marketing sources, incremental warehouse loading, and direct integrations with BigQuery, Snowflake, Redshift, and others. supermetrics.com It also provides explicit field-level documentation and warehouse schemas for Facebook Ads. supermetrics.com

My pick: Funnel if you're building a durable marketing-data layer for analysts/data scientists. Supermetrics if you want something particularly mature around field selection, reporting, and warehouse transfers.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and the ad platforms you use, I can narrow this down to the best 2–3 options and compare API coverage, historical backfill, refresh latency, schema flexibility, and approximate cost.

If your priority is granular social/ad data in your warehouse for your own SQL/modeling, I’d shortlist these three:

My pick: Improvado

For custom analysis rather than dashboarding, I'd lean Improvado. Its positioning is much closer to "marketing data engineering pipeline": extract granular data, transform/normalize it, and land it in your warehouse. It currently lists 1,000+ data sources and explicitly supports Snowflake, BigQuery, Redshift and Databricks. improvado.io

I'd evaluate it against Funnel if you care more about self-service configuration and controlling exactly which fields get exported.

One important diligence point: don't just compare the number of connectors. Ask each vendor for a sample raw export/schema for your exact ad platforms (Meta, TikTok, LinkedIn, Pinterest, Reddit, etc.) and check whether they preserve ad → ad set → campaign → creative, breakdowns, attribution windows, conversion types, and historical revisions. That's where "granular" can mean very different things between vendors.

For granular social/paid-ad data into a warehouse for custom SQL analysis, my first choice would be Supermetrics.

Why:

• Broad ad-platform coverage: Facebook/Meta Ads, Instagram, LinkedIn Ads, TikTok Ads, Pinterest Ads, Reddit Ads, Snapchat, X, etc. are supported. supermetrics.com
• Warehouse-native delivery: It supports BigQuery, Snowflake, Redshift, Databricks and other warehouse destinations, with automated transfers and incremental loading. supermetrics.com
• Good granularity: You can select dimensions/metrics and work from field-level source data rather than being limited to prebuilt dashboards. Their documentation provides field lists and warehouse-table schemas. docs.supermetrics.com
• Low engineering overhead: It handles API connections, schema management, incremental loads and error recovery, which is attractive if your data team doesn't want to maintain individual ad-platform APIs. supermetrics.com

When I'd choose something else

Improvado — I'd evaluate this if you're an enterprise/multi-channel organization and want heavier cross-channel normalization and a larger integration catalog. Its current Facebook Ads integration advertises 200+ extracted fields and 1,000+ integrations. improvado.io

Funnel — worth considering if marketing teams need to manage the extraction layer themselves and you also care about creative-level exports. Funnel supports creative exports to object storage for downstream analytics. help.funnel.io

My shortlist

One important caveat: “granular” varies significantly by platform and connector. Before committing, I'd test your exact required grain—for example date × account × campaign × ad set × ad × placement × creative—and verify that every desired metric/dimension is actually available from the underlying API. Supermetrics explicitly notes that available fields and historical ranges vary by data source. docs.supermetrics.com

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and which ad platforms you use, I can narrow this to the best 1–2 options and compare field-level granularity, refresh latency, historical backfill, and likely cost.

For granular social/paid-ad data into a warehouse for custom analysis, I’d shortlist Supermetrics and Funnel, with Fivetran as the more engineering-oriented option.

My recommendation: Supermetrics

Supermetrics is probably the best fit if your priority is marketing-data breadth + granular dimensions/metrics + easy warehouse delivery.

• 170+ marketing data sources, including Facebook Ads, TikTok, LinkedIn Ads, Snapchat, Pinterest, etc. supermetrics.comsupermetrics.comhelp.funnel.iowww.fivetran.com
• Direct exports to BigQuery, Snowflake, Redshift, Azure Synapse, Databricks, and other destinations. supermetrics.com
• Lets you select dimensions, metrics, transformations, and schedules, rather than being limited to a fixed reporting schema. supermetrics.com
• It maintains standard warehouse schemas—for example, dedicated Facebook Ads warehouse tables—while also exposing field lists for deeper extraction. supermetrics.com

When I'd choose Funnel instead

Funnel is particularly compelling if your analysts want to control the grain and manipulate marketing data before it lands in the warehouse. Its Facebook connector explicitly lets you choose granularity down to Campaign → Ad Set → Ad, and Funnel supports custom dimensions and metrics. help.funnel.io

Where Fivetran fits

Fivetran makes more sense if this is primarily a data-engineering/ELT problem rather than a marketing-data management problem. Its Facebook Ads connector emphasizes deep source extraction into a warehouse/lake with an analyst-friendly schema. www.fivetran.com

My decision rule:

One important caveat: “granular” doesn't necessarily mean raw event-level data. Ad platforms often expose metrics already aggregated by day/campaign/ad rather than individual impressions or clicks. Funnel explicitly calls this out for advertising APIs. help.funnel.io

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.) and ad platforms (Meta, TikTok, LinkedIn, Google, Reddit, etc.), I can narrow this to the best choice and compare field-level granularity, historical backfill, refresh frequency, and pricing model.

For this use case, I’d recommend Supermetrics.

It’s particularly well suited if your goal is granular paid-social data → warehouse → your own SQL/BI/modeling, rather than another dashboarding tool.

Why Supermetrics

• Broad social/ad coverage: Facebook Ads, Instagram Insights, LinkedIn Ads, TikTok Ads, Pinterest Ads, Reddit Ads, Snapchat, and others are supported. supermetrics.com
• Warehouse-native delivery: It supports BigQuery, Snowflake, Redshift, Azure Synapse, Azure SQL, Databricks and other destinations, with automated transfers and incremental loading. supermetrics.com
• Granular dimensions/metrics: You can select the fields and dimensions you want rather than being confined to prebuilt dashboard metrics. Supermetrics also documents field lists and warehouse table schemas for its sources. docs.supermetrics.com
• Less pipeline maintenance: It handles API connections, schema management, refreshes and error recovery, so your data engineers don't have to maintain individual Meta/TikTok/LinkedIn API integrations. supermetrics.com
• Good fit for custom analysis: Once the data is in your warehouse, you can join ad data with orders, CRM, product and customer data and build your own attribution/ROAS/LTV models in SQL. supermetrics.com

The main alternative I'd consider

Airbyte is worth considering if you have a stronger data-engineering team and prioritize raw/replicated data, extensibility, and control over marketing-specific convenience. Its Facebook Marketing connector supports incremental or full-refresh syncs and 50+ warehouse/database destinations, and Airbyte has a much broader general-purpose connector ecosystem. airbyte.com

My rule of thumb:

If you tell me which warehouse (Snowflake/BigQuery/Databricks/etc.) and which ad platforms (Meta, TikTok, LinkedIn, Google, etc.), I can narrow this down to 2–3 platforms and compare granularity, historical backfill, API limitations, freshness, and likely cost.

If your priority is granular social-ad data in your warehouse for custom analysis, my default recommendation is Fivetran.

Why Fivetran

• Very granular: Its Meta/Facebook Ads connector supports ad, ad-set, campaign, and account-level reporting, plus dimensions such as device, placement, geography, demographics, and action breakdowns. fivetran.com
• Customizable extraction: You can specify fields, breakdowns, action breakdowns, aggregation, and attribution windows rather than being stuck with a fixed reporting schema. beta.fivetran.com
• Warehouse-native: It loads the raw-ish source data into your warehouse/lake, where you can join it to orders, CRM, web analytics, product data, etc. www.fivetran.com
• Good for serious analytics: Fivetran also has dbt-compatible models that turn the underlying data into analytics-ready account/campaign/ad-set/ad and geographic models. fivetran.com
• Handles awkward Meta metrics: Things like actions, action_values, and cost_per_action_type are broken into secondary tables so you can preserve their underlying granularity. fivetran.com

The main alternative: Supermetrics

I'd consider Supermetrics if your organization is more marketing/BI-oriented than data-engineering-oriented. It supports warehouse destinations including BigQuery, Snowflake, Redshift, and Azure and has broad coverage across advertising platforms. supermetrics.com

My pick: Fivetran if your data team will own the pipeline and you want maximum control over the grain and downstream modeling. Supermetrics if marketers/analysts will configure most of the reporting themselves.

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks, etc.) and the platforms you need (Meta, TikTok, LinkedIn, Pinterest, Snapchat, Google Ads), I can narrow this to the best 2–3 options and compare coverage, granularity, refresh rates, and likely cost.

If your priority is granular social ad data in a warehouse for custom analysis, I’d recommend Fivetran as the default choice.

Why Fivetran

• Granular Meta data: its Facebook Ads connector supports custom reports with configurable fields, breakdowns, action breakdowns, attribution windows, and aggregation. fivetran.com
• Warehouse-first: it replicates the source data into your warehouse/data lake rather than primarily optimizing for dashboarding. www.fivetran.com
• Good analytical grain: you can query at ad/ad-set/campaign levels, and Fivetran creates secondary tables for things like actions, action_values, and cost_per_action_type. fivetran.comfivetran.com
• Broad platform coverage: particularly attractive if you're eventually combining Meta with Google Ads, TikTok, LinkedIn, etc., rather than building one-off API pipelines.
• dbt support: Fivetran has a Facebook Ads dbt package that turns the raw data into analytics-ready models across account, campaign, ad set, ad, and geographic reporting. fivetran.com

The main alternative: Supermetrics

Supermetrics is worth considering if your team is more marketing/BI-oriented and wants a broad catalog of advertising connectors with relatively easy configuration. It supports Facebook Ads transfers to data warehouses/cloud storage and currently lists 100+ data-source connectors. docs.supermetrics.com

I'd lean Fivetran for an engineering/data team and Supermetrics for a marketing analytics team.

One important caveat: the platform can't overcome limitations in Meta's Marketing API. For example, historical availability, demographic/device breakdowns, attribution, and some combinations of metrics/breakdowns are constrained by Meta itself. Fivetran documents a maximum historical retrieval window of 37 months for Facebook Ads. fivetran.com

Bottom line: If you have Snowflake, BigQuery, Redshift, Databricks, etc. and want analysts to be able to slice social advertising data however they want, I'd start with Fivetran → warehouse → dbt rather than a dashboard-oriented marketing ETL tool.

For granular social ad data → your data warehouse → custom SQL/BI analysis, I’d shortlist Funnel and Supermetrics, with Funnel as my default recommendation.

My pick: Funnel

Funnel is particularly well suited if your priority is controlling the grain and fields that land in the warehouse rather than just building marketing dashboards.

• Ad-level granularity: Funnel's Meta connector lets you explicitly choose the level, including Campaign → Ad Set → Ad, and configure additional fields. help.funnel.io
• Creative-level data: It supports creative exports with fields such as ad creative data, and has equivalent configurations for TikTok, Google, LinkedIn, Pinterest, Snapchat, Reddit, etc. help.funnel.io
• Warehouse destinations: BigQuery, Snowflake, Redshift and other destinations are supported. help.funnel.io
• Custom datasets: You can define metrics/dimensions and filters per data source rather than accepting a fixed reporting schema. help.funnel.io
• Historical/backfill capability: Funnel documents support for importing past data and then scheduling ongoing updates. help.funnel.io

That's a good fit for a setup like:

Meta / TikTok / Google / LinkedIn → Funnel → Snowflake/BigQuery → dbt → BI / ML / custom analysis

Strong alternative: Supermetrics

Supermetrics is probably the better choice if you have a lot of different marketing sources and want broad connector coverage with relatively little engineering. It currently advertises 170+ marketing sources, automated warehouse transfers, incremental loading, and integrations with BigQuery, Snowflake and Redshift. supermetrics.com

Its Meta connector supports granular campaign/ad-set/creative, audience and placement data, while its Google Ads connector supports detailed campaign, keyword and creative data. supermetrics.com

How I'd decide

Bottom line: If your phrase "granular social ad data" means we want the underlying ad-level facts in Snowflake/BigQuery and will do our own modeling, I'd start with Funnel. If you need the widest possible collection of marketing sources and a more mature all-in-one marketing data platform, I'd evaluate Supermetrics alongside it.

One thing I'd specifically test in a trial: whether each platform exposes every breakdown you care about without forcing aggregation—especially Meta placement, creative, demographic, attribution-window, and conversion dimensions. That's where seemingly similar ETL products can differ substantially.