Summarize
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."
Begin by accessing your Apple Search Ads data. Use the Apple Search Ads API to download the data you need. You'll need to authenticate using your Apple Developer credentials and request the data using appropriate API endpoints. Ensure you retrieve data in a format that can be easily processed, such as JSON or CSV.
Once you've retrieved the data, prepare it for export. This involves cleaning the data, removing any unnecessary fields, and ensuring consistency in format. Depending on your needs, you might convert JSON data to CSV or another format that's easier to work with in subsequent steps.
Set up a local environment where you can manipulate and process your data. Install necessary tools like Python or Apache Spark to handle data processing. Ensure your environment can read and write to the file systems you plan to use with Apache Iceberg.
Apache Iceberg works efficiently with columnar data formats like Parquet. Use a scripting language or data processing tool to convert your cleaned data into Parquet format. This step ensures that the data is optimized for storage and querying in Iceberg.
Set up Apache Iceberg on your local machine or server. This involves installing necessary dependencies and configuring Iceberg to work with your chosen file system (such as HDFS or a local file system). Ensure that you have a compatible Hive or Spark environment set up to interact with Iceberg.
Define the schema for your Iceberg table. This schema should match the structure of your Parquet data. Use SQL-like commands to create a table in Iceberg, specifying the necessary columns and data types.
Finally, load the Parquet data files into your Apache Iceberg table. Use Spark SQL or a compatible query engine to insert data into Iceberg. Verify that your data has been correctly loaded by running some sample queries to check the contents of your Iceberg table.
By following these steps, you should be able to move your data from Apple Search Ads to Apache Iceberg without relying on third-party connectors or integrations.
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:
