Meta Ads and LinkedIn Ads in Data Studio: Practical Connection Options

• Why non-Google ad platforms are harder
• Option 1: Partner connectors
• Option 2: Scheduled exports into Google Sheets
• Option 3: Load into BigQuery
• The part people underestimate: definitions
• What I usually recommend

Google Ads data flows into Data Studio (formerly Looker Studio) with a native connector, so founders often assume Meta Ads and LinkedIn Ads will be just as easy. They aren’t. I’m Harsh Khubchandani, an independent Data Studio consultant in New Delhi, and this is how I explain the options when a client wants all their paid channels in one report.

§Why non-Google ad platforms are harder

Data Studio ships with Google-made connectors for Google products. For Meta, LinkedIn, TikTok and most other ad platforms, there is no Google-built connector. That leaves three broad routes, each with a different balance of cost, effort and reliability.

§Option 1: Partner connectors

The Data Studio connector gallery lists many partner connectors built by third-party companies. They connect to the ad platform’s API and show the data as a normal data source.

• Pros: quickest to set up, no code, refreshes automatically.
• Cons: most are paid subscriptions, pricing usually scales with accounts or data sources, and you depend on the vendor keeping up with API changes.

I tell clients to check the pricing page, the free-trial terms and which metrics and breakdowns are actually supported before committing. Not every connector exposes every field the platform’s own reports show.

§Option 2: Scheduled exports into Google Sheets

Some teams export campaign data from the ad platform into a Google Sheet on a schedule, using an add-on, an automation tool, or a simple manual download each week.

• Pros: low or no cost, easy to inspect, the team understands it.
• Cons: breaks quietly when a column changes, gets slow as history grows, and manual steps get skipped during busy weeks.

This works for small accounts with a few campaigns and a weekly review rhythm.

§Option 3: Load into BigQuery

For clients spending meaningfully across several platforms, I prefer landing every platform’s data into BigQuery, either with a data pipeline tool or a scheduled script, then building one standardised table with the same columns for every channel: date, platform, campaign, spend, impressions, clicks, conversions.

• Pros: one consistent model, fast reports, full history, easy to add channels later.
• Cons: more setup, ongoing pipeline costs depending on the tool, and someone needs to own it.

§The part people underestimate: definitions

Whichever route you pick, platforms define things differently. A “conversion” in Meta, LinkedIn and Google Ads may use different attribution windows and counting rules. Putting them side by side in one table without notes creates arguments in every review meeting.

So before building, I agree with the client:

1. Which conversion action counts for each platform.
2. Whether to show platform-reported conversions, GA4 or CRM conversions, or both.
3. How currency and time zones are handled if accounts differ.

These go on a documentation page inside the report.

§What I usually recommend

Small budget, one or two platforms: Sheets exports, reviewed weekly. Growing spend across several platforms: a partner connector or a BigQuery pipeline, chosen after comparing real costs. Either way, a single standard table and written definitions matter more than the connector.

If you want Meta, LinkedIn and Google Ads in one honest report, I start with a free 30-minute review and a fixed written price. Find out more at datastudioconsultant.com.

Harsh Khubchandani, Data Studio Consultant, New Delhi, India