How to Migrate Data from Apple Search Ads to BigQuery?

Building an all-new data connector is challenging, especially when you are already overloaded with managing & maintaining your existing custom data pipelines. To fulfill an ad-hoc Apple Search Ads to BigQuery connection request from your marketing team, you’ll have to invest a significant portion of your engineering bandwidth.

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.

Either way, with this article’s stepwise guide to connect Apple Search Ads to BigQuery effectively, you can set all your worries aside and quickly deliver time-sensitive campaign data to your data-hungry marketing team in 7 nifty minutes.

What is Apple Search Ads?

Apple Search Ads is a marketing platform designed and managed by Apple that any app developer and marketer might use in the promotion of an iOS app directly available in the App Store. With ASA, one is able to prepare campaigns with a budget, ad groups, and targeted keywords. Adverts are automatically adjusted with custom keyword-bidding strategies and the search match feature.

Key Features of Apple Search Ads

• Keyword Targeting: Target specific keywords that users search for on the App Store to show relevant ads.
• Audience Refinement: Target ads based on factors like device type, location, gender, and customer type (new users, returning users, or those who installed other apps).
• Enhanced Performance using Campaigns: The four different types of campaigns- Brand, category, competitor, and discovery provide ways to drive better results.
• Cost-Per-Tap (CPT) Bidding: Pay only when users tap on your ad, and set your maximum CPT bid to control spending.

What is BigQuery?

Google BigQuery is a fully managed and serverless enterprise cloud data warehouse. It uses Dremel technology, which transforms SQL queries into tree structures. BigQuery provides an outstanding query performance owing to its column-based storage system.

Key Features:

• Machine Learning: BigQuery ML allows users to train and run machine learning models in BigQuery using only SQL syntax.
• Serverless Architecture: BigQuery manages servers and storage in the background, so a user does not need to.
• High Scalability: It scales seamlessly to handle petabytes of data.
• SQL Compatibility: It supports ANSI SQL, which is useful for people who already know SQL and want to write and run queries. This also allows a user to combine various BI tools for data visualization.

How to connect Apple Search Ads to BigQuery?

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

Method2: Downloading & Uploading data as CSV files via custom code

Follow the below steps to integrate Apple Search Ads to Bigquery via CSV files:

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 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 are 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.

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 data that is accumulated, very cumbersome management of large CSV files becomes possible, and all this increases 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 data and incomplete files.

Use Cases of Connecting Apple Search Ads to BigQuery

• 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.
• 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.

Summing It All Together

Just by importing & exporting CSV files for your marketing team’s rare Apple Search Ads data replication requests, you can easily hit it right out of the ballpark. But what if these data updates need to happen every few hours? Your marketers are always on the hunt 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 hassle go away 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. 

Frequently Asked Questions