Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
To extract data, first configure access to the Apple Search Ads API. Log into your Apple developer account, navigate to the Search Ads section, and generate an API key. Ensure you have the necessary permissions to access the data you need.
Use a programming language like Python to make HTTP requests to the Apple Search Ads API. Utilize libraries such as `requests` to authenticate using your API key and retrieve data. Focus on fetching the specific metrics and reports you require for your analysis.
After extracting the data, parse the JSON responses into a structured format, such as a pandas DataFrame (if using Python). This step involves cleaning and transforming the data to match your target schema in ClickHouse, ensuring the data types and formats are compatible.
Before loading data, configure your ClickHouse database and create the necessary tables. Define the schema according to the transformed data structure. Use SQL commands to create tables with appropriate columns and data types.
Use a native client or a library like `clickhouse-connect` for Python to establish a connection to your ClickHouse instance. Ensure that your network configurations allow a secure connection between your machine and the ClickHouse server.
Write a script to insert the transformed data into ClickHouse. This can be done using the `INSERT INTO` SQL command within your chosen programming environment. Ensure batch inserts to optimize performance, especially for large datasets.
To maintain up-to-date data, automate the entire ETL process. Use a task scheduler like cron (on Unix-based systems) or Task Scheduler (on Windows) to run your script at desired intervals. This ensures continuous data flow from Apple Search Ads to ClickHouse.
By following these steps, you can efficiently transfer data from Apple Search Ads to a ClickHouse warehouse without relying on third-party tools, thus maintaining full control over the data transfer process.
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:
