LinkedIn acts as a social media platform for professionals and contains users who are spread all over the world. Due to its ability to reach millions of business-oriented prospects at the same time, an Ad campaign on LinkedIn will be immensely beneficial for your Marketing team. LinkedIn Ads is a tool that provides you the facility of paid Marketing in LinkedIn’s social media. It uses sponsored posts to publish your Ads. Linkedin Ads is a powerful marketing tool for B2B companies to generate potential customers, increase online visibility, extensive content sharing, etc.

Every day the world is generating data at a scale of petabytes and the hardware resources required to store and process this kind of data are simply too expensive. Due to this reason, many companies are relying on Cloud-based Data Warehouses which provide huge storage capacities and scalability at much lesser prices. Google BigQuery is one such Data Warehouse that allows you to store and manage your data efficiently.

Nowadays, transferring data from LinkedIn Ads to BigQuery has become a necessity for every company that uses LinkedIn Ads to target potential customers. This blog will first discuss the key features of both LinkedIn Ads and BigQuery and then will showcase two methods that you can use to transfer data from LinkedIn Ads to BigQuery. Read along to get a detailed explanation of these two methods and decide which method will suit you the best.


The following prerequisites precede data extraction from LinkedIn Ads to BigQuery.

  • Working knowledge of REST APIs.
  • A functional LinkedIn account authenticated with the OAuth 2.0 protocol.
  • A successful set-up Google BigQuery Data Warehouse.
  • Working knowledge of Databases and Data Warehouses.

Introduction to LinkedIn Ads

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LinkedIn Ads is an online advertising service that allows you to reach out to LinkedIn’s extensive user base. It enables you to leverage specific organizational and career affiliations of the users. LinkedIn Ads supports a variety of advertisements like Video content, Sponsored content, Dynamic ads, etc. Due to the popularity of LinkedIn amongst professionals, this platform provides ample opportunities to your Marketing team to reach out to millions of Leads at the same time. As a platform, LinkedIn advertising is directly comparable to Facebook in terms of functions and positioning methods and has its own advantages and disadvantages in terms of online advertising.

A Few Key Features of LinkedIn Ads are:

  • LinkedIn Ads gives you access to a large audience of about 575 million users.
  • It also allows you to tailor your budget specifically to your business needs without having to overpay or pay upfront.
  • You can also connect directly to your customers with the LinkedIn Ads’ InMail feature.

To read more about LinkedIn Ads visit here.

Introduction to Google BigQuery

Google BigQuery Logo.
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Google BigQuery is a Cloud-based Data Warehouse, part of the Google Cloud Products stack. It enables Scalable, Fast, and Easy analysis of Big Data using SQL queries. This program has the computing power of Google’s infrastructure. With built-in machine learning, BigQuery can help you process and understand data at an exceptionally fast rate. BigQuery currently enjoys a good reputation in the market due to its high performance, which can be attributed to its tree-based architecture and its columnar storage.

Few Key Features of Google BigQuery are:

  • Distributed Architecture: BigQuery does not require you to manage computer clusters manually as Google manages these resources dynamically.
  • Easy to Use: Using BigQuery is straightforward. All you need to do is load data for analysis and pay for what you use. 
  • Fast and Detailed Insights: BigQuery’s seamless integrations with front-end analytics tools like Tableau and Google Data Studio, make it very easy to gain quick insights from your data.
  • Managed Service: Performance tuning and backend configuration are handled by Google. This differentiates BigQuery from many other Data Warehouses on the market that require you to do this manually.

To read more about Google BigQuery visit here.

Methods to Load Data from LinkedIn Ads to BigQuery

Method 1: Using Custom ETL Scripts to Set up LinkedIn Ads to BigQuery Integration

You can choose this approach to transfer data from LinkedIn Ads to BigQuery if you have access to developer resources and are comfortable working with RESTful APIs and Data Warehouses. You can start with extracting data from LinkedIn Ads using REST APIs. Next, you would have to map the data to a schema in Google BigQuery and finally, load the data using appropriate commands. You would also need an infrastructure set up to transform the data for production-level deployments.

Method 2: Using Hevo Data to Set up LinkedIn Ads to BigQuery Integration

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Hevo Data will help you load data from LinkedIn Ads to BigQuery instantaneously, without the use of APIs, commands, or even a single piece of code. Hevo Data is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Hevo’s pre-built integration with 100+ other data sources(including 30+ free data sources) will take full charge of the data transfer process, allowing you to focus on key business activities.

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Methods to Set up LinkedIn Ads to BigQuery Integration

There are 2 methods you can use to set up LinkedIn Ads to BigQuery Integration:

Method 1: Using Custom ETL Scripts to Set up LinkedIn Ads to BigQuery Integration

Broadly, the steps involved in migrating data from LinkedIn Ads to Bigquery are:

Step 1: Extract Data from LinkedIn Ads

REST API is the primary source of interaction with LinkedIn Ads to gain access to your marketing data. More specifically, you can extract the data by making simple GET requests to the API. The API provides access to a lot of resources that include likes, conversion values, and the number of clicks on your Ads.

An example is shown below.


A sample response to the API call would be as follows.

    "elements": [
            "actionClicks": 0,
            "adUnitClicks": 0,
            "clicks": 177,
            "comments": 0,
            "companyPageClicks": 15,
            "conversionValueInLocalCurrency": "0.0",
            "costInLocalCurrency": "244.85000",
            "costInUsd": "244.85000",
            "dateRange": {
                "end": {
                    "day": 20,
                    "month": 3,
                    "year": 2018
                "start": {
                    "day": 12,
                    "month": 10,
                    "year": 2017
            "externalWebsiteConversions": 0,
            "externalWebsitePostClickConversions": 0,
            "externalWebsitePostViewConversions": 0,
            "follows": 0,
            "impressions": 54494,
            "landingPageClicks": 67,
            "leadGenerationMailContactInfoShares": 0,
            "leadGenerationMailInterestedClicks": 0,
            "likes": 8,
            "oneClickLeadFormOpens": 0,
            "oneClickLeads": 0,
            "opens": 0,
            "otherEngagements": 1,
            "pivot": "CAMPAIGN",
            "pivotValue": "urn:li:sponsoredCampaign:134704654",
            "pivotValues": [
            "reactions": 0,
            "sends": 0,
            "shares": 0,
            "textUrlClicks": 0,
            "totalEngagements": 186,
            "viralClicks": 0,
            "viralComments": 0,
            "viralCommentLikes": 0,
            "viralCompanyPageClicks": 0,
            "viralExternalWebsiteConversions": 0,
            "viralExternalWebsitePostClickConversions": 0,
            "viralExternalWebsitePostViewConversions": 0,
            "viralFollows": 0,
            "viralImpressions": 86,
            "viralLandingPageClicks": 0,
            "viralLikes": 0,
            "viralOneClickLeadFormOpens": 0,
            "viralOneClickLeads": 0,
            "viralOtherEngagements": 0,
            "viralReactions": 0,
            "viralShares": 0,
            "viralTotalEngagements": 0
    "paging": {
        "count": 10,
        "links": [],
        "start": 0

Step 2: Prepare and Transform Data to be Ready for Analysis

First of all, it is important to map the data that comes out of each LinkedIn Ads endpoint to a schema for your Database. As is common with JSON data, you also need to make sure that you flatten out any of the data that may be in lists or nested. It is also important to ensure that the data types in the LinkedIn Ads data are properly matched with their corresponding types in BigQuery as it supports many of the widely used data types.

More information on these data types can be found here

Step 3: Load Data from LinkedIn Ads to BigQuery

Implement the following procedure to load data from LinkedIn Ads to BigQuery:

  • Use gsutil to load the data into GCS
  • Write code to create your data tables and specify your schema. This can be done using the bq load command, via bq(BigQuery’s command-line interface). You can read more about loading data to BigQuery using the bq command here.
  • Load your data into the BigQuery table(s).

After this step the process of transferring your data from LinkedIn Ads to BigQuery is complete.

Challenges in Building Custom ETL Scripts

Although this approach looks like it involves just a few lines of code and queries, the implementation can be an elaborate process. Limitations and challenges of the manual approach include:

  1. Time-consuming Process: Manually writing code to perform ETL is very time-consuming and taxing on your engineering resources.
  2. Constant Maintenance: The LinkedIn Ads API will have to be monitored consistently as it will return inaccurate data if there are any issues. 
  3. Hard to Perform Data Transformations: Fast data transformations such as currency conversions and time standardizations cannot be performed under this method
  4. Real-time Limitations: A lot of code will have to be written and configured to enable any kind of real-time functionality. This could be particularly taxing on your engineering team

Method 2: Using Hevo Data to Set up LinkedIn Ads to BigQuery Integration

Hevo Data Logo
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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from LinkedIn Ads and 100+ other data sources to Data Warehouses such as Google BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss.

Hevo takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

Hevo Data’s Pipeline makes loading your data from LinkedIn Ads to BigQuery a hassle-free straightforward process with just two simple steps:

  • Configure your Data Source: Configure and connect your LinkedIn Ads account as a data source.
Image showig Configuration of LinkedIn Ads with Hevo in the Set up of LinkedIn Ads to BigQuery Integration.
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To get more details about Configuring LinkedIn Ads with Hevo Data visit this link.

  • Configure your Destination: Configure your Google BigQuery account as the destination.
Image showig Configuration of BigQuery with Hevo in the Set up of LinkedIn Ads to BigQuery Integration.
Image Source

To get more details about Configuring BigQuery with Hevo Data visit this link.

Isn’t that as easy as it can get? All you have to do now is relax and watch Hevo take care of the rest.

Benefits of using Hevo

  • Simplicity: Using Hevo for your data pipeline needs ensures that your data is transferred in just a few clicks from any source to any destination. Hevo is super intuitive and very easy to use. 
  • Real-time Data Transfer: Hevo’s real-time streaming architecture ensures that your data is moved instantly and without delay. Thus, allowing you to gain insights in real-time.
  • Minimal Setup: Hevo is fully managed and automated. Hence, it requires minimal effort from your end to set up.
  • Scalability: Hevo can handle data from a wide variety of sources like Sales and Marketing applications, Analytics applications, Databases, etc. at any scale. Thus, enabling you to scale your data infrastructure as your needs expand. 
  • Reliable Data Load: Hevo has a fault-tolerant architecture that ensures consistent data load with no data loss.


Marketing teams are generating enormous amounts of data regularly from their Ad campaigns on LinkedIn. This data is then transformed and stored in Data Warehouses like BigQuery for further analysis. This article discussed in detail the two methods that you can use to transfer your data from LinkedIn Ads to BigQuery. If you are ready to spend your efforts, time, and resources on building the ETL process manually then you should implement the Custom coding method.

Also check our article on how to connect LinkedIn ads to Google data studio.

Visit our Website to Explore Hevo

Hevo Data, on the other hand, will automate this process for you and will provide you a seamless and hassle-free way to set up your LinkedIn Ads to BigQuery Integration. Hevo Data’s Pipeline automatically maps your data to its relevant tables in BigQuery, giving you real-time access to your data.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

Share your experience of setting up LinkedIn Ads to BigQuery Integration in the comments section below.

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