How to Migrate Data from Apple Search Ads to BigQuery?

Apple Search Ads is one of the highest-converting mobile advertising channels available today. According to Apple’s data spanning November 2024 through October 2025, nearly 65 percent of App Store downloads happen directly after a search, ads in the top position convert at around 60 percent, and Apple reports more than 800 million weekly App Store visitors. For app marketers, that means a constant stream of campaign data that needs to be analyzed, acted on, and reported quickly.

We know you are short on time & need a quick way out. This can be a walk in the park if you just need to download and upload a couple of CSV files. Or you could directly opt for an automated tool that fully handles complex transformations and frequent data integrations for you.

This guide covers two proven methods to migrate Apple Search Ads data to BigQuery, a no-code automated approach with Hevo and a manual CSV upload method, so you can choose the right fit for your team and get started today.

Use Cases of Connecting Apple Search Ads to BigQuery

• Cross-Channel Ad Analysis: Combine Apple Search Ads data with other advertising platforms (e.g., Google Ads, Facebook Ads) to get a holistic view of cross-channel marketing efforts, measure overall ad effectiveness, and compare performance across platforms.
• Campaign Performance Analysis: Analyze the performance of Apple Search Ads campaigns, including metrics like impressions, taps, conversions, and cost-per-tap (CPT) across various keywords and audience segments.
• Audience Segmentation: Combine Apple Search Ads data with other datasets in BigQuery (e.g., CRM data, website analytics) to perform advanced audience segmentation and gain deeper insights into user behavior.
• Cost Optimization: Use BigQuery’s powerful querying capabilities to identify underperforming keywords, campaigns, or regions that are not delivering sufficient ROI.

How to connect Apple Search Ads to BigQuery?

Method 1: Automate the Data Replication process using a No-Code Tool
Method 2: Downloading & Uploading data as CSV files via custom code

Method 1: Automate the Data Replication process using a No-Code Tool

Step 1:  Configure Apple Search Ads as a source. 

Step 2: Connect to your Google BigQuery account via a user or service account.

After implementing the 2 simple steps, Hevo will take care of building the pipeline for replicating data from Apple Search Ads to BigQuery based on the inputs given by you while configuring the source and the destination.

The pipeline will automatically replicate new and updated data from Apple Search Ads to BigQuery every 6 hours (by default). However, you can adjust the Apple Search Ads to BigQuery data replication frequency per your requirements.

Data Replication Frequency

Default Pipeline Frequency	Minimum Pipeline Frequency	Maximum Pipeline Frequency	Custom Frequency Range (Hrs)
6 Hrs	1 Hr	24 Hrs	1-24

Method 2: Downloading & Uploading data as CSV files via custom code

Step 1: Download Report

In Apple Search Ads, custom reports can be created by choosing different metrics per requirement. And these custom reports can be downloaded as CSV files by clicking the download icon next to the report in the Actions column of the Custom Reports page.

Step 2: Load CSV file to BigQuery

• Get the required IAM permissions to load data into a BigQuery table or partition.
• CSV data can be uploaded to BigQuery using Python. For a detailed setup of Python for BigQuery, check the BigQuery Python API reference documentation.
• Finally, use the below Python Code to upload CSV data and complete the Apple Search Ads to BigQuery data replication process.

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# Set table_id to the ID of your project table
table_id = "your-project.your_dataset.your_table_name" #edit accordingly

job_config = bigquery.LoadJobConfig(
    schema=[
        bigquery.SchemaField("name", "STRING"),
        bigquery.SchemaField("post_abbr", "STRING"),
    ],
    skip_leading_rows=1,
    # The default source format is CSV
)
filename= "****.csv" #Give CSV filename here

load_job = client.load_table_from_file(
    filename, table_id, job_config=job_config
)  # Make an API request.

load_job.result()  # Waits for the job to be complete.

destination_table = client.get_table(table_id)  # Make an API request.
print("Loaded {} rows.".format(destination_table.num_rows))

By printing the last lines, you get to know how many rows of data are uploaded.

Exporting & importing CSV files to provide data for your marketing & sales teams is an effective option for the following cases:

• Little to No Transformation Required: Carrying out complex data preparation and standardization tasks is impossible using the above method. Hence, it is an excellent choice if your Ad campaign data is already in an analysis-ready form for your business analysts.
• One-Time Data Transfer: At times, business teams only need this data quarterly, yearly, or once when looking to migrate all the data completely. For these rare occasions, the manual effort is justified.
• Few Reports: Downloading and uploading only a few CSV files is fairly simple and can be done quickly.   

A problem occurs when your business teams require fresh data from multiple reports every few hours. It becomes vital to clean and standardize the data for people to make sense of it when it is available in multiple formats. As a result, you soon find yourself devoting a sizable amount of your engineering bandwidth to building new data connectors. To guarantee a transfer with no data loss, you must also keep an eye out for any changes in these connectors and repair data pipelines as needed. These on-priority tasks can easily eat up 40-50 % of the time needed to complete your core engineering duties.

Comparing the Two Methods: No-Code Tool vs CSV Upload

There are two ways to move your Apple Search Ads data into BigQuery. The right choice depends on how often your team needs fresh data, how much engineering effort you can invest, and whether you need ongoing automation or a one-time transfer.

• Method 1: Hevo Data (No-Code) is best for teams that need automated, real-time data sync with zero engineering overhead and no custom scripts to maintain
• Method 2: CSV Upload is best for one-time or infrequent transfers where data is already in analysis-ready form and no transformation is needed

Here is a side-by-side comparison to help you choose the right approach:

Limitations of using CSV to migrate data from Apple Search Ads to BigQuery

• Manual Effort: All downloading and uploading, formatting operations of data, involving I/O processes for transferring CSV files, are time-consuming activities, requiring frequent human intervention.
• Data Freshness: The updates are not in real time, resulting in delayed access to fresh data, which may lead to BigQuery giving the user outdated data, hence prompting the user for outdated reports and slowing up decision-making.
• Scalability Issues: With the magnitude of accumulated data, very cumbersome management of large CSV files becomes possible, increasing the chances of file corruption, slow data transfers, and even system crashes.
• Error-Prone: It is prone to mistakes since manual handling of CSV files could result in missing and incomplete data.

Conclusion

In this blog, we have given you a tutorial on integrating your Apple Search Ads data into BigQuery. Unlike old methods such as importing & exporting CSV files for your marketing team’s rare Apple Search Ads data replication requests, you can easily integrate it by using the methods mentioned in this tutorial. Imagine your data updates need to happen every few hours, and your marketers are constantly searching to boost their ROI by monitoring real-time campaign performance. Don’t worry! You won’t need to bite the bullet and spend months developing & maintaining custom data pipelines. You can make all the hassle disappear in minutes by taking a ride with Hevo’s 150+ plug-and-play integrations. 

Saving countless hours of manual data cleaning & standardizing, Hevo’s pre-load data transformations get it done in minutes via a simple drag n drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

Sign up for a 14-day free trial with Hevo and streamline your data integration. Also, check out Hevo’s pricing page for a better understanding of the plans.

Frequently Asked Questions