Import LinkedIn Ads Data to Google BigQuery Easily

Why and How to Integrate LinkedIn Ads with BigQuery Using Keboola

[1col]Why Automate LinkedIn Ads Data to BigQuery?

LinkedIn Ads is essential for B2B marketers, but manual data exports are slow, error-prone, and limit your ability to analyze campaigns across platforms. By automating the flow of LinkedIn Ads data into Google BigQuery, you gain:

[centralized]Centralized Marketing Analytics: Blend LinkedIn, Google Ads, Facebook Ads, and web analytics in one data warehouse for cross-channel insights. See how Keboola enables unified analytics.
[analytics]Advanced Campaign Analysis: Use SQL and BI tools on historical LinkedIn Ads data to uncover trends and optimize campaigns.
[collaboration]Sales and Marketing Alignment: Link LinkedIn Ads data with CRM systems like Salesforce to measure true campaign ROI.
[clock]Time and Cost Savings: Stop wasting hours on manual exports. With Keboola, your pipeline runs on a schedule, ensuring up-to-date dashboards.
[scalable]Scalability: BigQuery stores unlimited historical data and handles massive datasets for year-over-year comparisons and deep analysis.

[2col]How Keboola Makes LinkedIn Ads to BigQuery Simple

Keboola is a no-code data platform that lets you build ETL/ELT pipelines visually. It offers:

[connection]Prebuilt Connectors: Extract data from LinkedIn Ads (via LinkedIn Marketing API) and load it to BigQuery without scripts.
[easy]No-Code UI: Set up authentication, data mapping, and scheduling through a simple web interface.
[automation]Automated Flows: Chain extract and load steps into a repeatable, automated workflow.
[secure]Secure Credentials: Manage OAuth and Google Service Accounts securely, with encryption and monitoring.

Step-by-Step: Import LinkedIn Ads Data to BigQuery

Set Up the LinkedIn Ads Extractor

Add the LinkedIn Ads component in Keboola.
Authorize Keboola to access your LinkedIn Ads account (OAuth — quick and secure).
Choose your date range, ad account(s), and report type (e.g., campaign stats, ad performance).
Save and run the extraction to pull your data into Keboola's storage.

Configure the BigQuery Writer

Add the Google BigQuery component in Keboola.
Paste your Google Service Account JSON key for authentication.
Define the destination dataset and table(s) in BigQuery.
Map source columns to BigQuery data types (e.g., INTEGER for clicks, DATE for date fields).
Choose load type: full (overwrite) or incremental (append new data).

Create and Test the Flow

Set up a Flow to run the LinkedIn Ads extraction, then the BigQuery load.
Run the flow and verify the data lands correctly in BigQuery.

Schedule and Automate

Configure automatic scheduling (e.g., daily, hourly) so your BigQuery data stays fresh.
Set up notifications for job failures or anomalies for peace of mind.

[2col]Real-World Use Cases

[360]Unified Marketing Dashboards: Combine LinkedIn Ads with Google Ads, Facebook Ads, and CRM data in BigQuery for a 360° marketing view. Learn more about BigQuery integration.
[analytics]Campaign Optimization: Drill into campaign, creative, and audience metrics to maximize ROI. Run custom SQL analysis and visualize results in Looker Studio or Tableau.
[decision]Sales Impact Measurement: Join LinkedIn Ads conversions with Salesforce deals to attribute revenue accurately.
[data-science]Historical and Predictive Analytics: Store multi-year ad data in BigQuery to spot trends, seasonality, and feed machine learning models.

Detailed Keboola Setup Guide

1. LinkedIn Ads Extraction:

Add the LinkedIn Ads data source component. Authenticate via LinkedIn OAuth.
Select ad account(s) and specify extraction parameters like date range, campaigns, and report type.
Use Keboola's templates to join campaign metadata with performance metrics for richer context.
Run extraction and check data in Keboola Storage (tables like

linkedin_ads_stats

2. Google BigQuery Writing:

Add the BigQuery data destination component.
Authenticate using a Google Service Account (steps provided in Keboola docs).
Configure your dataset and map columns with correct data types.
Choose between full or incremental load modes.

3. Orchestrate the Flow:

Create a Flow in Keboola, adding the LinkedIn Ads extraction and BigQuery load steps in order.
Test run the flow; monitor status in Keboola's UI.
Verify your data appears in BigQuery with correct schema and values.

4. Automate and Monitor:

Schedule the Flow for automatic updates (e.g., nightly).
Configure notifications for failure or anomalies to ensure reliability.

Tips for Data Engineers and Marketers

[tool]Schema Mapping: Use Keboola's preview and mapping UI to ensure proper types (INTEGER, NUMERIC, STRING, DATE).
[clean]Data Quality: Preview extracted tables before loading to BigQuery. Keboola makes it easy to spot missing fields or mismatches.
[efficient]Incremental Loads: For ongoing updates, use incremental load mode and date filters to append only fresh data.
[data-source]Blending with Other Data: Use Keboola to join LinkedIn Ads with other data sources (e.g., Salesforce, Google Analytics) for richer insights.

Benefits of Using Keboola

[easy]No Code Required: Set up robust ETL workflows without scripting or engineering resources.
[automation]End-to-End Automation: From data extraction to loading and scheduling, Keboola handles every step.
[secure]Secure and Scalable: Credentials are encrypted, and both Keboola and BigQuery scale for high data volumes.
[reliable]Production-Ready: Monitoring, alerting, and version control mean your pipelines are enterprise-grade.