Data as of Oct 11, 2026A topic in Marketing Reporting & Analytics Dashboards.
Reviewed by Dimitry Apollonsky
Fivetran holds a narrow lead over Funnel for custom marketing data pipelines. Neither brand separates itself on specific queries for PPC software or agency client reporting.
extracting marketing data into warehouses for custom reporting pipelines
storing and mapping cross-channel marketing data for dashboard creation
pulling marketing metrics directly into spreadsheets, warehouses, and Looker Studio
syncing marketing data through configurable open-source and managed pipelines
supplying platform advertising metrics to external marketing reporting workflows
ChatGPT SearchOct 3, 2026
If your goal is to pull marketing metrics from multiple platforms, transform them, and feed recurring reports, there are several good approaches. The right one mainly depends on how much customization and engineering control you need.
| Tool | Best for | Custom pipelines | Reporting destinations |
|---|---|---|---|
| Supermetrics | Marketing teams & reporting | High | Sheets, Excel, Looker Studio, Power BI, warehouses, APIs |
Google AI ModeOct 4, 2026
ChatGPT SearchOct 5, 2026
what PPC reporting software should I use?
Summary
Recommendations for PPC reporting tools are divided across multiple extraction and dashboard tools without a single standout choice.
| Funnel | Marketing data foundation | High | BI tools, warehouses, reports, applications |
| Fivetran | Engineering/warehouse pipelines | Very high | BigQuery, Snowflake, databases, etc. |
| Airbyte | Open-source/custom integrations | Very high | Warehouses, databases, APIs |
| Custom API + Python | Maximum control | Maximum | Anywhere |
Supermetrics is purpose-built around marketing data. It has prebuilt connectors for sources such as Google Ads, Meta, GA4, Shopify and HubSpot, and lets you transform/blend the data before sending it to reporting tools or a warehouse.
The particularly useful feature for your question is its Connector Builder: you can define a custom connector using JSON to retrieve data from a private API or other unsupported source.
It also has an API for building your own dashboards, applications, automated reporting workflows, and warehouse pipelines.
Good architecture:
Google Ads + Meta + GA4 + CRM → Supermetrics → warehouse → SQL transformations → Looker/Power BI/report
Funnel is another marketing-focused option. It currently advertises 600+ connectors, data standardization, historical storage, and the ability to send the resulting data to different destinations.
I'd look at Funnel particularly if you want a central marketing-data layer rather than simply plugging individual sources directly into spreadsheets.
Fivetran is more general-purpose ELT infrastructure. It supports hundreds of sources and destinations, including marketing/reporting sources such as Google Ads, GA4, Facebook Ads, LinkedIn, Adobe Analytics and others.
For genuinely unsupported APIs, its Connector SDK lets you build a custom connector in Python while Fivetran handles things such as retries and connection-level resyncs.
This is attractive if your architecture is:
Marketing APIs → Fivetran → Snowflake/BigQuery → dbt/SQL → BI
Airbyte is worth considering when you want an open-source/self-hostable approach and don't want to be locked into a marketing-specific platform. You can use existing connectors or build your own API connector, then land the data in your warehouse.
It's particularly interesting if your team is comfortable with Python, APIs, Docker and cloud infrastructure.
For a small number of sources, a custom pipeline can actually be very clean:
Google Ads API ─┐
Meta API ───────┤
GA4 API ────────┼→ Python extraction → Warehouse → SQL/dbt → Reports
HubSpot API ────┘You get complete control over:
The tradeoff is maintenance: API authentication, schema changes, retries and platform-specific quirks become your responsibility.
If you're primarily a marketer/analyst: Supermetrics or Funnel.
If you have a data/engineering team and a warehouse: Fivetran or Airbyte.
If you need unusual APIs or highly customized transformations: custom Python, potentially combined with Fivetran's Connector SDK or Supermetrics' Connector Builder.
If the final output is mostly Google Sheets/Looker Studio: Supermetrics is particularly convenient because it is designed around that reporting workflow.
If you tell me which sources you need (e.g. Google Ads + Meta + GA4 + HubSpot) and where the reports live (Sheets, Looker Studio, Power BI, Tableau, etc.), I can map out a concrete pipeline and compare the tools/cost/complexity for that setup.
Building custom data pipelines for marketing metrics depends heavily on your technical resources, budget, and whether you want a dedicated marketing data hub or an infrastructure-level data stack.[](https://funnel.io/blog/enterprise-etl-tools)
The best tools are broken down below by category to help you choose the right fit for your reporting stack:
These platforms focus explicitly on ad networks, social platforms, and CRMs, offering pre-built data cleaning, currency normalization, and attribution mapping out of the box.[](https://www.salesforce.com/marketing/analytics/tools/)[[2](https://www.salesforce.com/marketing/analytics/tools)](https://funnel.io/blog/enterprise-etl-tools)
If your strategy relies on landing raw data into a cloud data warehouse (like Snowflake, Google BigQuery, or Amazon Redshift) and transforming it later, use these modern stack staples:[](https://www.cometly.com/post/marketing-data-integration-tools)[[2](https://www.salesforce.com/marketing/analytics/tools)](https://funnel.io/blog/enterprise-etl-tools)
If you prefer writing custom Python scripts or SQL queries to extract exact campaign metrics, you need orchestration to automate the runs:
To help narrow down which tool fits your stack, tell me:
Supermetrics, Adverity, Improvado, Funnel.io, and Windsor.ai are top platforms that allow you to create custom data extraction and integration pipelines specifically designed for marketing reporting.[](https://www.tapclicks.com/blog/best-data-integration-tools)
If you require deep custom code or developer-focused infrastructure rather than pre-built marketing connectors:
If you tell me which specific marketing channels (e.g., Meta Ads, Google Ads, TikTok) and reporting destination (e.g., BigQuery, Looker Studio, Power BI) you plan to use, I can recommend the most cost-effective platform for your stack.
ChatGPT SearchOct 4, 2026
Yes. If by “custom data extraction pipelines” you mean pull data from multiple marketing platforms, transform/normalize it with your own logic, and automatically push it into a warehouse, BI tool, spreadsheet, or reporting layer, there are several good options.
| Platform | Best for | Custom transformation | Marketing-specific | Warehouse support |
|---|---|---|---|---|
| Funnel | Sophisticated marketing reporting | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
Yes. For custom marketing-data extraction pipelines, I’d shortlist these:
| Tool | Best for | Customization | Engineering effort |
|---|---|---|---|
| Supermetrics | Marketing teams pulling ads/CRM/analytics data | High | Low |
| Airbyte | Custom APIs + lots of data sources | Very high | Medium |
| Fivetran | Reliable managed ELT with minimal maintenance | High | Low–Medium |
| BigQuery + Dataform | Building your own warehouse/reporting layer | Very high | Medium–High |
| Dagster/Airflow | Complex multi-step pipelines and orchestration | Very high | High |
1. Supermetrics — best if these are primarily marketing reports
It has 170+ data-source connectors covering things like Google Ads, GA4, Facebook Ads, Instagram, and LinkedIn, and supports custom data imports. Its Connector Builder lets you define custom API connectors using JSON, so you can pull from an API that isn't natively supported.
It can send data to destinations including warehouses, Google Sheets, Excel, and Power BI, and its API can return JSON/CSV for more programmatic workflows.
Best architecture:
Google Ads / Meta / GA4 / CRM / custom API → Supermetrics → warehouse → dashboard
2. Airbyte — best if you want to own the extraction layer
Airbyte is particularly attractive when you have custom APIs, unusual sources, or rapidly changing requirements. Its 2026 comparison describes 700+ connectors and options for managed or self-hosted deployment.
I'd choose this over Supermetrics when your marketing reporting is becoming a broader data engineering project rather than simply a reporting workflow.
3. BigQuery + Dataform — best for a scalable reporting backbone
If you're comfortable with SQL, this is an excellent foundation. Google has an explicit marketing-analytics ELT tutorial that extracts marketing data into BigQuery and uses Dataform for scheduled transformations.
Dataform gives you version-controlled SQL transformations, dependencies, testing, documentation, and scheduling.
BigQuery ·
A strong production setup would be:
Marketing APIs → Supermetrics/Airbyte → BigQuery → Dataform → Looker/Power BI
That separates extraction from business logic, which becomes very valuable once you have metrics like CAC, ROAS, attributed revenue, LTV, and channel-level spend being reused across reports.
If you tell me your data sources (e.g. Meta Ads, Google Ads, HubSpot, Salesforce, GA4), destination (Sheets/Looker/Power BI/warehouse), and whether you have engineers, I can recommend a specific pipeline architecture and the cheapest/simplest tool combination.
Google AI ModeOct 5, 2026
Setting up custom data extraction pipelines for marketing reports depends on your technical resources, budget, and the variety of data sources (e.g., Meta Ads, Google Analytics 4, HubSpot, LinkedIn Ads) you need to unify.
The top tools available for marketing data pipelines are categorized below by their strengths:
| ✅ |
| Supermetrics | Reporting + broad connectors | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
| Windsor.ai | No-code custom pipelines | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
| Coupler.io | Affordable no-code reporting | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ✅ |
| Fivetran | Enterprise data engineering | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ✅ |
| Airbyte | Highly customizable / open-source | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ✅ |
| Improvado | Enterprise marketing data | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
| Adverity | Enterprise marketing intelligence | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
Funnel is particularly interesting if you're building a repeatable marketing-data model, rather than simply exporting Facebook/Google Ads data into a dashboard.
You can define business logic, standardize campaign/channel naming, map data to budgets and targets, and transform the data before sending it to destinations. Funnel says it supports 36+ destinations and can either transform data inside Funnel or let your data team transform it downstream.
I'd pick it for: multi-channel agency/client reporting, standardized marketing models, governed reporting.
Supermetrics is excellent when the goal is something like:
Meta + Google Ads + LinkedIn + GA4 + HubSpot → transformations → BigQuery/Sheets/Looker/Excel
It supports 150+ data sources and is explicitly designed around marketing-data collection, unification, reporting and activation.
I'd pick it for: marketers/analysts who want lots of connectors without building API integrations themselves.
Windsor is a good middle ground between a marketing reporting connector and a traditional ETL platform. It supports 350+ sources and provides scheduled syncs, schema matching, incremental loading and in-app transformations.
I'd pick it for: teams that want relatively sophisticated pipelines but don't want to maintain code.
Coupler supports 400+ sources and lets you blend data, filter/sort it, create calculations, aggregate data and unify multiple sources without SQL. It also supports automated refreshes and reporting destinations.
I'd pick it for: agency reporting, Google Sheets/Looker Studio workflows, and teams that don't need a heavy data-engineering platform.
Fivetran is less marketing-specialized but much stronger if marketing data is just one part of a larger company data platform.
It offers 700+ connectors, normalized schemas, scheduled pipelines, transformations and warehouse-centric workflows.
A typical architecture would be:
Google Ads / Meta / HubSpot / Salesforce → Fivetran → Snowflake/BigQuery → dbt → Looker/Tableau
I'd pick it for: data teams, larger organizations, and warehouse-first architectures.
Airbyte is particularly attractive if customization is more important than turnkey marketing reporting. It has both Cloud and self-hosted/open-source options and is designed for moving data from APIs/databases into warehouses and other destinations.
I'd pick it for: engineering teams that want control over connectors, deployment and pipeline architecture.
These are worth considering when you're dealing with large numbers of marketing sources, clients, markets, currencies, campaign taxonomies and governance requirements.
A current 2026 comparison puts Improvado and Adverity alongside Funnel and Supermetrics in the marketing-specific data-platform category, while positioning Fivetran/Airbyte more toward warehouse/data-engineering workflows.
If I were choosing today:
For a marketing team:
→ Supermetrics or Funnel
For an agency building standardized client reporting:
→ Funnel or Coupler.io
For no-code but more flexible pipelines:
→ Windsor.ai
For a data team with BigQuery/Snowflake:
→ Fivetran + dbt or Airbyte + dbt
For a large enterprise marketing-data operation:
→ Funnel, Improvado, or Adverity
The key distinction is that Funnel/Windsor/Supermetrics/Coupler are marketing-first, whereas Fivetran/Airbyte are data-engineering-first. Current comparisons similarly separate marketing-data platforms from warehouse-oriented ELT tools.
If you tell me your sources (e.g. Meta Ads + Google Ads + GA4 + HubSpot + Salesforce) and destination (BigQuery, Snowflake, Looker Studio, Power BI, etc.), I can narrow this to the 3 best platforms and compare their pipeline capabilities and approximate 2026 pricing.
To help narrow down the ideal stack, tell me: