Synchronizing data between advertisement and analytics platforms makes a robust infrastructure for marketing your organization's products or services. Bing Ads and Google BigQuery are two leading tools in their respective fields. 

By integrating data from Bing Ads into Google BigQuery, you can leverage your data's advanced data management and analysis capabilities to gain useful insights. This will help you make more data-driven decisions to create better advertisement campaigns and optimize performance and ROI.

Let’s look into a quick overview of the platforms and two easy methods of synchronizing data from Bing Ads to BigQuery.

Bing Ads Overview

Microsoft Advertising, formerly known as Bing Ads, is an online marketing platform developed by Microsoft. It allows advertisers to display ads, product listings, service offers, and videos to Bing, Yahoo, and DuckDuckGo search engine users.

The workings of Bing Ads are similar to Google Ads. The only catch is it's for a search engine owned by Microsoft. Google owns over 84% of the search engine market, but Microsoft comes second with only 9%. The gap may seem significant. However, with over a billion unique monthly users, Bing has plenty of audience to offer for your advertisement needs.

Let’s look at some key features of Bing Ads.

• Target Options: You can use Bing Ads to target audiences based on their demographics, location, time of the day, days of the week, and type of devices. With a diversity of target options, advertising campaigns can be more effective.
• ‍Orchestration: Bing Ads includes comprehensive reporting and orchestration tools. You can track a wide variety of metrics, including click-through rates, conversion rates, impressions, and more, to make data-driven campaigns.

Google BigQuery Overview

Google BigQuery is a leading enterprise data warehouse that allows you to store and manage petabyte-size datasets. It stores data in a columnar format, which helps to increase efficiency in query execution by reducing the amount of data processed in queries.

Besides its robust querying capabilities, BigQuery's scalable, distributed analysis engine lets you query terabytes of data in seconds and petabytes in minutes. In addition, it offers many built-in features, including machine learning, geospatial analysis, and business intelligence, which makes it a fully managed storage system.

Some of the key features of BigQuery are:

• Serverless: BigQuery has a serverless model, which means users don’t have to manage any server infrastructure. This eliminates the need for any database administration and allows you to focus more on analyzing data rather than worrying about infrastructure.

• Machine Learning Integration: BigQuery offers a machine-learning tool that allows you to utilize regular SQL queries to develop and run machine-learning models. With the help of this integration, you can apply machine learning to your data more efficiently.

Methods to Move Data From Bing Ads to BigQuery

• Method 1: Using Airbyte to connect Bing Ads to BigQuery
• Method 2: Using CSV Import/Export to load data from Bing Ads to BigQuery manually

Method 1: Using Airbyte to Connect Bing Ads to BigQuery

Airbyte is a data integration tool that streamlines the process of extracting and loading data from different sources to data warehouses. You can use Airbyte to synchronize data between Bing Ads and BigQuery. With its user-friendly interface and orchestration capabilities, it automates the process of connection between the two platforms.

Here is a detailed guide on using Airbyte to connect the tools:

Step 1: Set Bing Ads as the Source

• Create an account or log in to Airbyte.

• After logging in, open the Airbyte cloud platform and navigate to the Sources page.
• Use the search bar to search for Bing Ads and click on the connector card when it appears in the search results.

• You'll be redirected to the Create a source page. Fill in the Developer Token.

• Click on Authenticate your Bing Ads account > Set up source.

Step 2: Set BigQuery As the Destination

• After successfully setting up the source, navigate to the Destinations tab on the left-side pane.
• In the search field, type BigQuery and click on the BigQuery connector card when you see it in the search results.

• On the Create a destination page, fill in the configuration details, such as Destination name, Project ID, Dataset Location, and Default Dataset ID. 
• At the bottom, select Loading Method:‍
• GCS Staging: This is the recommended loading method in Airbyte, providing best-in-class speed, reliability, and scalability.‍
• Standard Inserts: Direct loading using SQL INSERT statements. This method is recommended only for quick testing.

• Input Service Account Key JSON (required for cloud, optional for open-source).
• Click on Set up destination.

Step 3: Create a Bing Ads to BigQuery Connection

• Now, you must establish a connection between the source and destination within Airbyte. You can select the Create a new connection option after creating the destination or simply click on the Connections tab on the left-side pane.

‍

• Select Bing Ads (Step 1) as a source and BigQuery (Step 2) to create a connection. 
• Configure the connection details and provide a unique Connection Name. In configuration details, you can tweak the Streams section, select sync mode, and select Replication frequency according to your requirements. 
• Click on Set up connection, followed by sync now to start the sync. 

And just like that, you have successfully synchronized data between Bing Ads and BigQuery using Airbyte without requiring much technical expertise or manual intervention.

Method 2: Using CSV Import/Export to Load Data From Bing Ads to BigQuery Manually

This method describes how to manually export data from Bing Ads and import it to BigQuery using CSV files. It requires a lot of configuration for both tools. However, you don't need to use any other software or service to perform this task.

Here's a step-by-step guide:

Step 1: Create a BigQuery Project 

• Navigate to the Google Cloud console and create a Project.
• After creating the project, go to BigQuery and select the project you created in Step 1.
• You can configure the project name or other settings while creating the BigQuery project.

Step 2: Export Data From Bing Ads

• Go to your Bing Ads account and navigate to the data you want to load in Google BigQuery.
• Click on the Reports tab on the top menu. If you wish to have daily data exports, ensure the Show (unit of time) is set to Day.

• You can also configure other details in the General settings section. This includes Date range, Time zone, and Format (ensure it is set to .zip(.csv)).
• Then, click on Download.

Step 3: Import and Configure Data On BigQuery

• Go to the BigQuery Project created in Step 1.
• Click on the project ID in the left-side navigation bar, followed by +CREATE DATASET. 

• Give a unique name to your dataset, and provide a Description or Labels according to your requirements.
• Ensure the encryption method is set, then click Create dataset. 
• Select the newly created dataset in the left navigation bar to see the dataset configurations.
• Click +CREATE TABLE.

• Navigate to the Source section. Then, select the following options: ‍
• Create a table from > Upload.
• Browse the Microsoft Ads report CSV file you downloaded in Step 2 and upload it on Select file.
• The File format should be selected as CSV. 
• Now, go to the Destination section and select the following options:
• Click the Search for a project and find your project from the dropdown menu.
• Select the Dataset name you created earlier.
• Enter the name of the table you want to create on the Table name. This should be representative of the table of data you are uploading.
• Next, click on the Schema section and select the following options: 
• Check the Auto detect check box to allow BigQuery to auto-detect the data schema. You can also select Edit as text to manually name schema and set type (string, date, boolean, etc) and mode (nullable, required, or repeated).
• Select the Partition and cluster settings section. Choose Partition by ingestion time or No partitioning according to your requirements. Partitioning the table divides the data into smaller segments, allowing smaller data sections to query more efficiently.
• In the Advanced Options section:
• Set Field delimiter to Comma. 
• Choose 1 in the Header Rows to skip the field if the data includes a header row. 
• Configure other details such as the Number of errors allowed, Unknown values, Jagged rows, and Encryption method according to your requirements. 
• Finally, click the Create Table button, and BigQuery will start populating the Bing Ads data into the table.

• In the BigQuery project, select the Dataset ID of the populated dataset.
• You can start writing SQL queries in BigQuery for the Bing Ads data you imported.

That completes the process of syncing data from Bing Ads to BigQuery.

Conclusion

You have now seen two straightforward methods of synchronizing data from Bing Ads to BigQuery. The first method uses Airbyte, a data integration tool that automates the entire synchronization process. You only need to select Bing Ads as a source and BigQuery as the destination to connect both tools.

On the other hand, the second method uses a manual way of loading data from Bing Ads to BigQuery. First, you must export data from Bing Ads, then configure and load it in BigQuery, which requires manual intervention and expertise.

Considering the challenges associated with using CSV files, Airbyte is a more appropriate solution. It has an extensive library of pre-built connectors and a robust open-source community with over 15,000 engineers. This helps you streamline the connection between Bing As and BigQuery and any supported data source and destination of your choice.