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FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The Google Ads API is the modern programmatic interface to Google Ads and the next generation of the AdWords API and it is a paid online advertising platform offered by Google. Google Ads is a paid search channel. Google Ads is a key digital marketing tool for any business which is looking to get meaningful ad copy in front of its target audience. Google AdWords is a well known marketplace where companies pay to have their website ranked at the top of a search results page, based on keywords.
Google Ads API provides access to a wide range of data related to advertising campaigns, including:
- Campaigns: Information about the campaigns, such as name, status, budget, and targeting settings.
- Ad groups: Details about the ad groups, including name, status, and targeting criteria.
- Ads: Information about the ads, such as type, format, and performance metrics.
- Keywords: Data related to the keywords used in the campaigns, including search volume, competition, and performance metrics.
- Bidding: Details about the bidding strategies used in the campaigns, such as manual bidding or automated bidding.
- Conversions: Information about the conversions generated by the campaigns, including conversion rate, cost per conversion, and conversion tracking settings.
- Audience: Data related to the audience targeting used in the campaigns, such as demographics, interests, and behaviors.
- Location: Information about the geographic targeting used in the campaigns, including location targeting settings and performance metrics.
Overall, the Google Ads API provides a comprehensive set of data that can be used to optimize advertising campaigns and improve their performance.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
The Google Ads API is the modern programmatic interface to Google Ads and the next generation of the AdWords API and it is a paid online advertising platform offered by Google. Google Ads is a paid search channel. Google Ads is a key digital marketing tool for any business which is looking to get meaningful ad copy in front of its target audience. Google AdWords is a well known marketplace where companies pay to have their website ranked at the top of a search results page, based on keywords.
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
1. Go to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "New Source" button and select "Google Ads" from the list of available connectors.
3. Enter a name for your connector and click on "Next".
4. Enter your Google Ads credentials, including your client ID, client secret, refresh token, and developer token.
5. Click on "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Google Ads account.
6. Once the connection is successful, select the accounts that you want to sync with Airbyte.
7. Choose the sync mode that you want to use, either "Full Refresh" or "Incremental".
8. Set the frequency of your sync and click on "Create Source" to save your settings.
9. Your Google Ads source connector is now set up and ready to use. You can view your data in the Airbyte dashboard and start syncing it with your destination.
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
When dealing with data, many provider names come up for tool recommendations, and on top of that list is Google. It offers modern tools for almost every aspect of data management. Two of the well-known tools provided by Google are Google Ads and BigQuery.
Google Ads is a leading advertising platform that allows you to create and run advertisements for your target audience. The platform provides several features, such as a keyword planner, performance analytics, and campaign manager for efficient marketing and advertising.
On the other hand, BigQuery is a serverless data warehouse that enables you to store and manage huge datasets in real time. It is part of the Google Cloud Platform (GCP) and is specially designed for complex analytics.
You can leverage the advertising capabilities of Google Ads and the data management capabilities of BigQuery by connecting both tools. This article will discuss Google ads data integration to Google BigQuery using two straightforward methods.
Why Replicate Data From Google Ads to BigQuery?
Replicating data from Google Ads to BigQuery can significantly enhance your ability as a marketer to take meaningful insights from data and make more data-driven decisions. Here are some specific reasons for considering the migration of data from Google Ad Manager to BigQuery:
- Centralized Data: In most cases, advertising data travels to multiple data sources and platforms for data analysis and other tasks. Moving Google Ads data to BigQuery centralizes the advertising data in one location, streamlining the data management process.
- Detailed Insights From Campaigns: Robust querying features of BigQuery allow you to do a granular analysis of Google Ads campaigns. This can include analyzing performance data at various levels, such as ad group, keyword, and ad levels. Additionally, you can define custom metrics according to your specific analytical needs to achieve meaningful data.
- Handle Large Datasets: If you run extensive advertising campaigns, you must deal with large amounts of data in real time. The distributed processing architecture of BigQuery allows you to manage huge loads of datasets efficiently and ensures that performance doesn't compromise as the data volume grows.
- Integration with Other Sources: BigQuery enables you to integrate seamlessly with many data sources besides Google Ads. This capability provides a holistic view of your business data by combining information from other sources such as CRM systems, Google Analytics, or other analytical platforms.
Methods to Integrate Google Ads to BigQuery
- Method 1: Using Airbyte to connect Google Ads to BigQuery.
- Method 2: Using BigQuery Data Transfer Service to integrate Google Ads into BigQuery.
Method 1: Using Airbyte to Connect Google Ads to BigQuery
In this method, you’ll learn to use Airbyte to synchronize data from Google Ads to BigQuery. Airbyte is a cloud-based data integration tool that provides automated, reliable, and scalable solutions for your ETL workflows. Below is a step-by-step guide to this process:
Step 1: Configure Google Ads As a Source
- Create or log in to your Airbyte account.
- Once you are logged in, click on the Sources tab on the left navigation bar on the home page.
- Use the search box on top to locate the Google Ads connector. Once you see the Google Ads connector, click on it.
- You’ll be redirected to the Create a source page. Fill in the details like Source name and Customer IDs.
- After filling in the details, sign in to your Google account from which you have activated Google Ads.
- Click on Set up source.
Step 2: Configure Google BigQuery As a Destination
- After setting up the source, click on the Destinations tab from the left just below Sources.
- Look for the search box and type in BigQuery. Then, click on the BigQuery connector.
- On the Create a destination page, fill in the connection details, including Destination name, Project ID, Dataset Location, and Default Dataset ID.
- Select the Loading Method between: GCS Staging and Standard Inserts. The first method writes large batches of records to a file, uploads the file to GCS, then uses COPY INTO to load your data into BigQuery. It provides best-in-class speed, reliability, and scalability and is the recommended loading method. However the Standard Inserts method does direct loading using SQL INSERT statements. This method is suggested only for quick testing.
- Provide the Service Account Key JSON (required for cloud, optional for open-source). Click on Set up destination.
Step 3: Create a Connection Between Google Ads And BigQuery
- Once the source and destination are configured, you must establish a connection within Airbyte. You can click the Connections tab on the left navigation bar or select Create a new connection after setting up the destination.
- Select Google Ads as a source (Step 1) and BigQuery as a destination (Step 2) to establish a connection.
- Provide a unique Connection Name and configure connection details according to your requirements. You can select Replication frequency, tweak the Streams section, and select sync mode.
- Lastly, click on Set up connection and run sync by clicking Sync now.
That's all. You have completed connecting Google Ad Manager to BigQuery within a few clicks using Airbyte.
Method 2: Using BigQuery Data Transfer Service to Integrate Google Ads Data Into BigQuery
In this method, you’ll know how to manually replicate data from Google Ads to BigQuery using BigQuery Data Transfer Service. This service, provided for Google Ads, lets you automate the management and scheduling of data loads for Google Ads reporting data. Start by activating the BigQuery Data Transfer Service, then create a BigQuery dataset for storing the data, and finally, transfer the data by configuring specific details. Here is a detailed guide:
Prerequisites
Required Permissions
Ensure you have the following required permissions for the transfer:
- Read access to the Customer ID of Google Ads that is used to transfer the data.
- bigquery.transfers.update permissions for creating a transfer.
- bigquery.datasets.get and bigquery.datasets.update both the permissions on the dataset you want to store the data.
Step 1: Create a Project And Enable BigQuery API
- In the Google Cloud console, navigate to the project selector page.
- Select a project or create one.
- Verify that the BigQuery API is already activated for the existing project. If not, you can enable it from the BigQuery API page.
Step 2: Activate BigQuery Data Transfer Service
- Go to the API library of Google Console and open the BigQuery Data Transfer API page.
- Select the project from Step 1 in the dropdown menu.
- Click the ENABLE button.
Step 3: Create a BigQuery Dataset to Store Google Ads Data
- Go to the BigQuery page.
- In the Explorer panel, create a dataset for your project.
- Click on Actions > Create dataset.
- On the Create data set page, enter the unique dataset name for Data set ID, choose the geographic location for Location type, and configure other required details.
- Click CREATE DATA SET.
Step 4: Create Google Ads Data Transfer Using Console
- Again, navigate to the BigQuery page in Google Console.
- Click on the Data transfers section from the left navigation bar.
- On the Data transfers page, click on + CREATE TRANSFER.
- Select Google Ads as the Source option in the Source type section.
- Enter a unique transfer name for the Display name in the Transfer config name section.
- Then, on Schedule options:
- Select the frequency at which you want to conduct the transfer under Repeat Frequency. There are three options to choose from: Hours, Days, and On-demand. If you choose Days, provide a time that is valid in UTC.
- If applicable, select Start now or Start at a set time.
- For Dataset, choose the dataset you established in Step 3 in the Destination settings section.
- Then, in the Data source details section, enter Google Ads customer ID on the Customer ID text box and configure other details optimally.
- Navigate to the Service menu and select a service account that is associated with the Google Cloud project.
- You can choose to tweak the Notification options section as well. There are two options: Email Notifications and Pub/Sub notifications. Toggle the email notifications option to receive progress notifications by email. Click here to learn about Pub/Sub notification details.
- Click Save.
That concludes it. Follow the steps mentioned above carefully, and you can quickly integrate the data from Google Ad Manager to BigQuery.
Conclusion
You have learned two straightforward approaches to integrating Google Ads to BigQuery. Both methods have their use cases and benefits. The first method uses Airbyte to automate the connection between both tools. You have to set up the source and destination, and just like that, you’re done with the task within a few clicks.
In contrast, the other method uses BigQuery Data Transfer Service to integrate data, requiring more effort than the first one. You have to manually mention all the complex configurations required for the transfer in Google Cloud.
We suggest using Airbyte to automate the whole process of Ads data integration to Google Bigquery. In addition, you can take advantage of its user-friendly interface, modern orchestration capabilities, and more than 300 pre-built connectors, which give you many options for connecting sources and destinations.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Google Ads API provides access to a wide range of data related to advertising campaigns, including:
- Campaigns: Information about the campaigns, such as name, status, budget, and targeting settings.
- Ad groups: Details about the ad groups, including name, status, and targeting criteria.
- Ads: Information about the ads, such as type, format, and performance metrics.
- Keywords: Data related to the keywords used in the campaigns, including search volume, competition, and performance metrics.
- Bidding: Details about the bidding strategies used in the campaigns, such as manual bidding or automated bidding.
- Conversions: Information about the conversions generated by the campaigns, including conversion rate, cost per conversion, and conversion tracking settings.
- Audience: Data related to the audience targeting used in the campaigns, such as demographics, interests, and behaviors.
- Location: Information about the geographic targeting used in the campaigns, including location targeting settings and performance metrics.
Overall, the Google Ads API provides a comprehensive set of data that can be used to optimize advertising campaigns and improve their performance.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: