Does Google have a native Google Ads to BigQuery connector?

Yes. BigQuery Data Transfer Service has a Google Ads connector, and Google lists it among the connectors with no transfer charge; BigQuery storage and query costs still apply. It runs at most once every 24 hours with a refresh window of 7 days by default.

When is the Data Transfer Service the better choice?

When Google Ads, and other Google sources it covers, are all you need in BigQuery and a daily load is enough. If you also need Meta, LinkedIn, CRM or revenue data in the same warehouse, a single pipeline for every source is easier to run.

How many Google Ads tables does Weld sync to BigQuery?

Weld's Google Ads schema has 42 tables, covering accounts, campaigns, ad groups, ads, keywords, budgets and labels, plus daily and hourly performance reports by campaign, ad group, ad, keyword, country and province. You choose which to sync.

Is the Google Ads sync to BigQuery incremental?

Yes. The first sync loads history from the start date you choose, and later syncs are incremental. You can also re-sync a single table when you need to reload it.

Can I sync several Google Ads accounts into one dataset?

Yes. Select every ad account you want when you set up the connection. Tables carry account ids, and the account table holds each account's currency and time zone.

Can I send data from BigQuery back to Google Ads?

Yes. Weld's reverse ETL updates customer lists in Google Ads from a SQL model in BigQuery, on a schedule or as part of an orchestration.

Google Ads to BigQuery

Sync Google Ads campaigns, ad groups, ads, keywords and their daily and hourly performance into BigQuery. Google's own Data Transfer Service does this at no transfer charge and is a good fit inside Google Cloud; Weld lands 42 tables that sit next to your other marketing and revenue sources and can push audiences back to Google Ads.

Start for free View setup guide

4.8 on G2

Source

Weld

Warehouse

Checked against Weld and vendor docs on 10 October 2026

Weld · New sync

SourceGoogle AdsConnected

Tables to sync

campaign_stats25 cols
campaign22 cols
ad_group_stats28 cols
keyword_stats26 cols
account15 cols
campaign_hourly_stats26 cols

Extract
Load
Keep in sync

DestinationGoogle BigQueryReady

google_ads dataset

google_ads.campaign_statsSynced
google_ads.campaignSyncing
google_ads.ad_group_statsSynced
google_ads.keyword_statsSynced
google_ads.accountSynced
google_ads.campaign_hourly_statsSynced

Google Ads tables: 32
fields, typed: 591
agent context included: MCP

Trusted by data and AI teams

Options

Three ways to get Google Ads data into BigQuery

Google's BigQuery Data Transfer Service has a Google Ads connector with no transfer charge, so start there if Google Ads is your only source. The differences show up in table design, refresh behaviour and how many other sources you need in the same warehouse.

BigQuery Data Transfer Service

Best for: Google Ads data in BigQuery at no transfer charge
Effort: Low: configure in the Google Cloud console
Freshness: At most once every 24 hours
Good to know: Loads date-partitioned match and stats tables. Refresh window of 7 days by default, up to 30. Incremental transfers are not supported, and custom GAQL reports cannot use WHERE, ORDER BY or LIMIT.

Custom script on the Google Ads API

Best for: Teams with engineers to own a pipeline
Effort: High: OAuth, GAQL queries, API version upgrades
Freshness: Whatever you schedule
Good to know: Full control over which GAQL queries run, but you maintain the code and move it to new Google Ads API versions yourself.

Weld

Best for: Google Ads next to your other marketing and revenue data
Effort: Low: authorise, pick accounts and reports, schedule
Freshness: Incremental syncs on your schedule
Good to know: 42 tables of objects and daily and hourly reports, with country, province and device splits. Reverse ETL can update Google Ads customer lists from BigQuery.

How it works

How to sync Google Ads to Google BigQuery with Weld.
In the app, with the API or an agent.

Setup guide API guide MCP docs

Weld · Data warehouse

Data warehouseGoogle BigQueryConnected

Data locationChosen when you connect

CredentialsService account or key, stored encrypted

Schema

What lands in Google BigQuery
Every Google Ads table, keys intact.

Weld's Google Ads tables come in two kinds: objects, which describe how the account is set up, and reports, which hold daily or hourly performance. Most reporting starts from these.

Open the schema explorer

campaign_stats25 columns
- campaign_id→ campaignlong
- account_id→ accountlong
- campaign_namestring
- datedate
- account_descriptive_namestring
Daily cost, clicks, impressions, conversions and conversion value per campaign, split by device and ad network type. Sum across those splits for campaign totals.
campaign22 columns
- idPKlong
- account_id→ accountlong
- ad_serving_optimization_statusstring
- advertising_channel_sub_typestring
- advertising_channel_typestring
Campaign settings: name, status, channel type, start and end dates, bidding and optimisation score. Joins to the stats tables on campaign id.
ad_group_stats28 columns
- ad_group_id→ ad_grouplong
- account_id→ accountlong
- campaign_id→ campaignlong
- ad_group_namestring
- datedate
The same daily metrics one level down, per ad group, with campaign and account columns for rollups.
keyword_stats26 columns
- ad_group_id→ ad_groupstring
- campaign_id→ campaignstring
- resource_namestring
- customer_idstring
- customer_resource_namestring
Daily performance per keyword criterion, with ad group and campaign ids. Pair with ad_group_criterion for keyword text and match type.
account15 columns
- idPKlong
- auto_tagging_enabledboolean
- currency_codestring
- descriptive_namestring
- final_url_suffixstring
One row per ad account with currency code and time zone. Use it to label spend with the right currency when you sync several accounts.
campaign_hourly_stats26 columns
- campaign_id→ campaignlong
- account_id→ accountlong
- campaign_namestring
- datedate
- hourlong
Campaign metrics by date and hour, for intraday pacing and dayparting analysis.

All 32 tables

account
campaign
ad_group
campaign_criterion
ad_stats
audience_stats
ad
ad_group_criterion
ad_group_province_stats
ad_group_hourly_stats
ad_group_stats
ad_group_country_stats
campaign_hourly_stats
keyword_stats
campaign_stats
search_term_keyword_stats
search_term_stats
campaign_budget
user_list
campaign_criterion_proximity
campaign_criterion_schedule
user_interest
campaign_bid_modifier
campaign_bidding_strategy
account_hourly_stats
account_stats
ad_group_bidding_strategy
label
mobile_device
operating_system_version
and 2 more

Example query

Monthly spend, conversions and ROAS by campaign.
In Google BigQuery SQL.

Runs in BigQuery against the tables Weld lands, here in a dataset named google_ads. Google Ads reports cost in micros, so cost_micros is divided by one million; the account's currency_code says which currency that is.

Google BigQuery · query

1select2  date_trunc(s.date, month) as month,3  a.currency_code,4  s.campaign_name,5  round(sum(s.cost_micros) / 1e6, 2) as spend,6  sum(s.clicks) as clicks,7  round(sum(s.conversions), 1) as conversions,8  round(safe_divide(sum(s.conversions_value), sum(s.cost_micros) / 1e6), 2) as roas9from google_ads.campaign_stats as s10join google_ads.account as a on a.id = s.account_id11group by month, a.currency_code, s.campaign_name12order by month desc, spend desc

Weld MCP server

Ask your agent.
It queries Google BigQuery for you.

Connect Claude, Cursor, ChatGPT or your own agent to Weld over MCP. It reads the Google Ads context, finds the tables it needs and runs the query in Google BigQuery.

Set up the MCP server

Claude CodeOpus 5

ConnectedWeld MCPAgent contextGoogle Adstables32fields591

How many campaign rows do we have per account? Use our Google Ads data in Google BigQuery.

Reading the Google Ads context first, so the query uses the right tables and keys.

●weld·search_available_references

⎿raw.google_ads.campaign

raw.google_ads.account

+ 30 more Google Ads tables

●weld·get_reference_schema

⎿campaign · 22 columns

joins account on account_id

●weld·run_query

⎿select a.id, count(*) from {{raw.google_ads.campaign}} c

join {{raw.google_ads.account}} a on c.account_id = a.id

group by 1

submitted

●weld·get_query_status

⎿DONE

●weld·get_query_result

⎿rows returned

✓Answered from Google Ads data in Google BigQuery

>Ask about your Google Ads dataMCP

Under the hood

Things to know.
Before you sync Google Ads.

Reverse ETL

ELT vs. reverse ETL: Google Ads and Google BigQuery

ELT (Extract, Load, Transform) moves data from Google Ads into Google BigQuery for analysis. This is the approach shown on this page, and the one to use for dashboards, reports, and data models.

Reverse ETL works the other direction: it pushes enriched data from Google BigQuery back into Google Ads. For example, you could sync computed lead scores or customer segments from your warehouse into Google Ads to personalize outreach.

Weld supports both directions, so you can build a complete data loop between Google Ads and Google BigQuery.

Google BigQuery to Google Ads

Money is stored in micros: Google Ads returns monetary amounts in micros, where one million equals one unit of the account's currency. Divide cost_micros and the other _micros columns by 1,000,000, and keep accounts in different currencies apart.
Stats tables are split by device and network: Report tables such as campaign_stats carry device and ad_network_type columns, so one campaign has several rows per day. Group by the columns you need, or sum across them for daily totals.
Rate limits are shared across your account: Google Ads applies global rate limits to everything that uses the API for your account. If other tools call it too, syncs may slow down; Weld throttles its Google Ads syncs once usage reaches 80% of the limit.
Permissions differ for reading and writing: Syncing data into BigQuery needs viewer access to the ad accounts. Pushing customer lists back to Google Ads with reverse ETL needs editor access.

Sources: Weld Google Ads connector docs · Weld Google Ads schema · Weld BigQuery setup guide · Google Cloud: Load Google Ads data into BigQuery · Google Cloud: BigQuery pricing, Data Transfer Service · Google Ads API: Money and micros

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