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Begin by logging into your Apple Search Ads account. Navigate to the reporting section where you can generate reports. Choose the specific data range and metrics you need to export. Apple Search Ads typically allows you to download this data in CSV format.
Once your desired data is configured, download the report in CSV format. This file will contain all the necessary information that you need to transfer to Firebolt, such as campaign performance, keywords, and costs.
Open the downloaded CSV file using a spreadsheet application like Excel or a text editor. Review the data to ensure that all necessary columns are present. Clean the data by removing any irrelevant columns or correcting any inconsistencies.
In this step, adjust the structure of your CSV data to match the schema of your Firebolt database. This may involve renaming column headers, changing data types, or normalizing data formats. Ensure that your data complies with Firebolt's requirements for seamless loading.
Log into your Firebolt account and create a new table that corresponds to the data structure you have prepared. Use SQL within the Firebolt console to define the table schema, ensuring it matches the transformed CSV data layout.
Upload the transformed CSV file to Firebolt. Use Firebolt's built-in capabilities to load data manually. This typically involves writing an SQL `COPY` command in the Firebolt console, specifying the source file and the target table. Make sure to handle any errors or mismatches during this process.
After loading the data, run queries within Firebolt to verify that the data has been transferred correctly. Check for completeness and accuracy by comparing metrics and totals from the original Apple Search Ads report with the data now residing in Firebolt.
This guide ensures that you can manually transfer data from Apple Search Ads to Firebolt without the need for third-party tools, leveraging manual processes and Firebolt's native capabilities.
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.
Apple Search Ads is a platform that allows businesses to promote their apps in the App Store by displaying ads to users who are searching for specific keywords. Advertisers can target their ads based on factors such as location, device type, and demographics. The platform uses a pay-per-tap model, meaning advertisers only pay when a user taps on their ad. Apple Search Ads also provides detailed analytics and insights to help advertisers optimize their campaigns and improve their return on investment. Overall, Apple Search Ads is a powerful tool for app developers and businesses looking to increase their visibility and downloads in the App Store.
Apple Search Ads API provides access to a wide range of data related to app advertising campaigns. The following are the categories of data that can be accessed through the API:
1. Campaign data: This includes information about the campaigns such as campaign name, status, budget, start and end dates, and target audience.
2. Ad group data: This includes information about the ad groups such as ad group name, status, bid amount, and target keywords.
3. Keyword data: This includes information about the keywords such as keyword text, match type, status, and performance metrics.
4. Creative data: This includes information about the ad creatives such as ad type, ad format, ad group, and performance metrics.
5. Performance data: This includes information about the performance of the campaigns, ad groups, keywords, and creatives such as impressions, clicks, conversions, and cost.
6. Attribution data: This includes information about the attribution of the app installs to the advertising campaigns such as source, medium, and campaign name.
7. Audience data: This includes information about the target audience such as demographics, interests, and behaviors.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
