How to Connect Google Ads to Databricks for Analytics: 3 Methods

If you want serious marketing analytics, you need Google Ads data living alongside your product, web, and customer data inside Databricks. That is where attribution modeling becomes more accurate, campaign insights become richer, and machine learning work becomes far easier. The good news is that there are multiple reliable ways to move Google Ads data into Databricks. Even better, you can do it without creating an ETL pipeline that grows more complex every month.

This guide breaks down three practical methods to integrate Google Ads with Databricks—from fully managed connectors to multi-step warehouse workflows to custom API pipelines. Each method comes with different tradeoffs around freshness, complexity, and cost. To help you choose the right fit, we walk through all three approaches and provide an in-depth, step-by-step example for one of the most straightforward options.

Key Takeaways

• You move Google Ads data into Databricks to combine ad spend, performance metrics, and click behavior with downstream product and customer data. This unlocks better attribution modeling, unified ROI reporting, and more accurate machine learning.
• We cover three reliable methods to move Google Ads data into Databricks:
  1. Using Estuary, which provides managed connectors for Google Ads and Databricks, along with right-time data delivery and minimal engineering overhead.
  2. Using an intermediate warehouse or storage layer, where Google Ads data is first loaded into BigQuery, Snowflake, S3, or GCS, and then ingested into Databricks.
  3. Building a custom Google Ads API pipeline, using GAQL queries, the Ads API, and Databricks jobs or Autoloader to construct an end to end ingestion process yourself.
• Each method has different tradeoffs around freshness, complexity, and total cost. This article walks through all three options—so you can match your needs to the right approach.

Why send Google Ads data to Databricks

Google Ads is often the single biggest paid acquisition channel. On its own it tells you impressions, clicks, and conversions. Inside Databricks, combined with product and behavioral data, it tells you things like:

• True customer acquisition cost by cohort.
• Lifetime value by campaign and keyword.
• Which creative actually leads to repeat purchases or long term engagement.
• How performance varies by geography, device, and audience.

Databricks is a natural home for this because:

• It can store cheap, long term history in Delta.
• It gives analysts SQL and notebooks for exploration.
• Data scientists can build and deploy models over the same data.

The challenge is feeding Databricks with dependable pipelines that keep up with change in Google Ads while keeping your Databricks spend predictable.

What the Google Ads to Databricks pipeline looks like

At a high level, you are moving:

• Entity data
  • campaigns, ad_groups, ad_group_ads, keyword_view, customer, geographic_view, user_location_view, and more.
• Performance data
  • account_performance_report, ad_performance_report, display_keyword_performance_report, display_topics_performance_report, shopping_performance_report.
• Optional custom GAQL based reports
  • Any custom Google Ads Query Language (GAQL) query you want to convert into a table.

In Databricks, you usually create:

• Base tables that mirror those entities and reports.
• Derived tables that aggregate by day, campaign, device, etc.
• Modeling tables that join ads data to downstream events from your product and analytics stack.

To get from A to B you need to decide how you will:

• Authenticate to Google Ads and handle rate limits.
• Pull historical data and then keep up with fresh data.
• Manage multiple customer accounts.
• Land data reliably into Databricks SQL Warehouses and Delta tables.
• React when schemas change.

That is where the three methods differ.

How to choose the right approach

Before you pick a method, be honest about a few things:

1. Latency
   • Do you need near real time reporting, or is daily batch enough?
2. Complexity
   • How many Google Ads accounts and which reports will you use?
3. Engineering time
   • Do you want to own custom code, or would you rather configure connectors?
4. Total cost of ownership
   • This includes data movement, Databricks compute, storage, and human time.

Roughly:

• If you want dependable data pipelines, right-time control, and minimal custom code, Estuary is a strong default.
• If you already centralize everything in another warehouse and Databricks is a secondary environment, a multi hop approach can be ok.
• If you have strong internal data engineering and special requirements, a custom pipeline might be justified.

Now let us go through each method.

Method 1: Google Ads to Databricks with Estuary

Estuary provides a first party Google Ads connector and a Databricks materialization that work together as a single pipeline: capture from Google Ads, store in collections, and sync into Databricks on the schedule you define.

How Estuary connects Google Ads and Databricks:

Conceptually:

1. A Google Ads capture uses the Google Ads API to pull data and write it into Estuary collections.
2. These collections are stored in your own cloud object storage and validated by JSON schemas.
3. A Databricks materialization reads from those collections and applies changes into Delta tables in a Databricks SQL Warehouse.
4. A configurable sync schedule controls how often Estuary pushes changes into Databricks.

You get:

• Unified data movement across capture and destination.
• Right time performance through flexible sync frequency.
• Predictable data movement since you are not constantly hammering Databricks.

Prerequisites

You will need:

For Google Ads

• At least one Google Ads account with the customer ID.
• Optional:
  • A manager account customer ID if you manage multiple client accounts.
  • For CLI or manual setups, a Google Ads developer token, client ID, client secret, and refresh token. In the Estuary UI, OAuth is handled for you and you can simply log into your account.

For Databricks

• A Databricks account with:
  • Unity Catalog.
  • A SQL Warehouse.
  • A schema to materialize to.
• A personal access token (PAT) or service principal token with permission to use that warehouse.

For Estuary

• An Estuary account to configure the Google Ads capture and Databricks materialization.
  You can create a free account here: dashboard.estuary.dev/register

Here is the step by step process to set this up:

Step 1: Create your Google Ads capture

1. Go to Sources → click New Capture.
   
   

   
2. Search for Google Ads and select the connector.
   
   

   
   Click Capture to begin configuration.
   
   

   
3. Enter a Capture Name (e.g., google_ads_marketing).
4. Select your Data Plane (e.g., aws: us-east-1 c1).
5. In Customer ID(s), enter one or more 10-digit account IDs (comma-separated, no dashes).
6. Choose your Start Date for backfill.
7. (Optional) Add Custom GAQL Queries using the + Add Query button:
   • Custom Query
   • Destination Table Name
   • Primary Key
8. (Optional) Enter Login Customer ID if using an MCC account.
9. Adjust the Conversion Window (default = 14 days).
10. (Optional) Enter an End Date if you want a scheduled cutoff.
11. Click Sign in with Google and complete OAuth authentication.
12. Click Next, verify the data streams you want to use, then click Publish.

Your Google Ads capture is now active and generating collections.

Step 2: Configure your Databricks materialization (endpoint + schedule + collections)

2A. Start the materialization

1. Go to Destinations → click New Materialization.
2. Search for Databricks and select the connector to open the configuration screen.
   
   

   
3. Enter a Materialization Name (e.g., google_ads_to_databricks).
4. Select the same Data Plane used in Step 1.
   
   

   

2B. Enter Databricks endpoint settings

1. In Address, paste your Databricks SQL Warehouse hostname
   (e.g., dbc-abcdefgh-a12b.cloud.databricks.com).
2. In HTTP Path, paste your warehouse connection path.
3. Set the Catalog Name (e.g., main).
4. Set the Schema Name (e.g., default).
5. (Optional) Enable Hard Delete if you want deletes applied directly.

2C. Authenticate to Databricks

1. In Authentication, select PAT.
2. Paste your Personal Access Token.

2D. c (part of the materialization config)

1. Choose a Sync Frequency such as 30m, 1h, or 4h. For real-time data, select 0s.
   
   

   
2. (Optional) Set your Timezone (e.g., UTC).
3. (Optional) Set Fast Sync Start and Stop Time for higher frequency during peak hours.
4. (Optional) Set Fast Sync Enabled Days (e.g., M–F).

These controls let you balance freshness with Databricks cost.

2E. Set default table behaviors

1. Toggle Delta Updates default if you want new bindings to use delta-style inserts.
2. Choose a Default Naming Convention (commonly Mirror Schemas).
3. Set Default Field Depth or use the default value.

2F. Select your Google Ads collections

1. In Link Capture, click Modify and select your capture. This will automatically add all of the capture’s associated collections to the materialization.
2. Alternatively, in Collections, click Add and select individual collections such as:
   • campaigns
   • ad_groups
   • ad_group_ads
   • performance reports
   • custom GAQL tables
3. For each collection:
   • Confirm or edit the Table Name
   • Adjust the Schema if needed
   • Toggle Delta Updates for high-volume tables

2G. Set schema-handling logic

In Advanced Options, choose how to respond when Databricks rejects a schema change:

• Abort
• Backfill
• Disable Binding
• Disable Task

Choose the policy that matches your governance requirements.

Step 3: Test and publish your Databricks pipeline

1. Click Test to run all tests.
2. Estuary will validate:
   • Databricks credentials
   • Table creation permissions
   • Collection-to-table mappings
3. Click Save and Publish.

Your Google Ads → Databricks pipeline is now fully operational and syncing on your schedule.

If you want help designing or validating your pipeline, the Estuary team can support you directly. Talk to us

Pros and cons of the Estuary method

Advantages

• Right time performance: Sync scheduling lets you choose exactly when data is pushed to Databricks. You get fresh dashboards when it matters most.
• Predictable TCO: Because sync frequency is explicit and Databricks SQL Warehouses can auto stop, your Databricks usage becomes much more predictable.
• Multi account support: The Google Ads connector accepts multiple customer IDs in one configuration. Great for agencies or multi brand companies.
• Custom GAQL support: You can define custom GAQL queries and map each one to its own collection and table.
• Reliable semantics: Estuary uses a streaming oriented protocol with exactly once semantics for materializations, and handles details such as reserved words in Databricks automatically.

Tradeoffs

• You need a Databricks SQL Warehouse and a PAT or service principal token in place.
• For completely CLI based workflows you still need to manage developer tokens and secrets, although the UI can hide most of this behind OAuth.

When Estuary is the best fit

• You want dependable data pipelines without building a full ETL service.
• You care about right time insights and cost control at the same time.
• You plan to bring in other sources later, not only Google Ads, and want a single platform for capture and delivery.

Method 2: Via an intermediate warehouse or storage

In some organizations Databricks is not the first landing zone. You may already ingest Google Ads data into BigQuery, Snowflake, or cloud object storage. Databricks is then layered on top for advanced analytics and machine learning.

In this pattern:

1. Google Ads data is collected by a third party ELT tool or cloud service.
   For example data might land first in:
   • BigQuery as a set of ads reporting tables.
   • Snowflake in a marketing schema.
   • Cloud storage such as S3 or GCS as CSV or parquet files.
2. Databricks reads from that warehouse or storage through:
   • External table connectors.
   • Copy commands or Autoloader that ingest files into Delta tables.

Pros

• You leverage existing investments in an analytic warehouse.
• Many vendors know how to land data in BigQuery or S3, which can be convenient.
• It can be easy to share Google Ads data with teams that do not use Databricks.

Cons

• Data flows through multiple platforms, which introduces more points of failure.
• Latency is usually higher, especially if the upstream pipeline is a daily or hourly batch.
• You pay for storage and compute in the intermediate system as well as in Databricks.
• Schema changes can ripple across multiple jobs and are harder to reason about.

When this works well

• You already treat another warehouse as your primary system of record and Databricks is primarily for specialized use cases.
• You are comfortable with batch oriented reporting and do not need to optimize the timing on your data.
• You are okay with managing multiple vendors and orchestration layers.

Method 3: Custom Google Ads API pipeline into Databricks

The third option is to build your own pipeline on top of the Google Ads API and Databricks. This can be done with Python, Scala, Airflow, or Databricks jobs.

A typical design:

• Use official Google Ads SDKs and GAQL to query entities and reports.
• Implement your own logic for:
  • Authentication with developer tokens, refresh tokens, and OAuth.
  • Incremental fetching using time ranges, segments, or change history.
  • Handling API rate limits and errors.
• Write data to:
  • Cloud storage that Databricks ingests via Autoloader.
  • Delta tables directly using the Databricks runtime.

Pros

• Maximum flexibility. You control exactly which fields, joins, and schedules you want.
• You can bake in custom business logic that is not easy to express with generic connectors.

Cons

• Significant engineering and maintenance cost. You own all retries, backfills, and schema evolution.
• Harder to guarantee exactly once behavior and consistent history.
• As requirements grow, the pipeline can become a second product you have to maintain.

When a custom build makes sense

• You have a mature data platform team that prefers to build rather than buy.
• You need very specific behavior that off the shelf connectors cannot provide.
• You want full control of infrastructure and are willing to manage the long term cost.

Modeling Google Ads data in Databricks

Once data is flowing, you can design models that are both performant and friendly to analysts.

Base tables

• Keep one base table per Google Ads resource or report.
• Store raw fields with minimal transformation.
• Partition large tables by date to make range queries efficient.

Derived tables

• Build daily fact tables per level of analysis, for example:
  • fct_google_ads_campaign_performance
  • fct_google_ads_keyword_performance
• Add calculated metrics:
  • Cost per click, cost per conversion, conversion rate, ROAS.
• Join with geography, device, and audience dimensions.

Attribution and funnel views

• Join Google Ads clicks with web analytics sessions and product events.
• Attribute sign ups, purchases, or other key events back to the last click or multi touch models.
• Use Databricks notebooks to experiment with model variants.

Handling conversion windows and late data

• Because conversions can appear days after the original click, design your models to:
  • Recompute recent periods on each run, for example the last 30 days.
  • Keep versioned snapshots if you need auditability.

Performance tips

• Use Delta features such as Z ordering or clustering where appropriate.
• Be mindful of reserved words in column names. Estuary already quotes these when creating tables, but you should reference them correctly in SQL.
• For massive tables, consider using delta updates and designing queries that naturally filter by date or campaign.

Conclusion and next steps

Moving Google Ads data into Databricks is one of the highest leverage things you can do for marketing analytics and data science. You saw three ways to do it:

• A right time pipeline with Estuary that keeps everything in a single, managed system with flexible sync frequency and predictable data movement.
• A multi hop approach that uses an intermediate warehouse or storage layer when Databricks is not your primary system.
• A custom API integration that offers maximum control at the cost of engineering effort.

For most teams that want dependable data pipelines, clear control over freshness, and a path to reuse the same stack for other sources, Estuary is the most balanced option.

A practical next step is to:

1. Set up a small Estuary capture for one or two Google Ads accounts.
2. Materialize just a few key collections into a Databricks schema such as marketing.
3. Let your analytics partners explore the data and compare this pipeline against whatever process you are using today.

If the pilot gives you fresher insights with less operational work, you will know you are on the right track.