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 exporting the data from AppsFlyer. This can typically be done by accessing the AppsFlyer dashboard and using their built-in export functions. You may need to export data as CSV or JSON files, depending on what AppsFlyer supports for your specific use case. Ensure you have the necessary permissions to access and export the data.
Set up your AWS environment by creating an S3 bucket, which will serve as the storage location for your data. Use the AWS Management Console to create an S3 bucket, ensuring you select the appropriate region and configure the necessary permissions to allow data uploads.
Before uploading the data to AWS, ensure it is in a format compatible with your data lake. This might involve transforming the data from CSV or JSON into a parquet or ORC format using tools like Python scripts or AWS Glue. This step helps optimize storage and query performance within the data lake.
Use the AWS CLI (Command Line Interface) or AWS SDKs (Software Development Kits) to upload your transformed data files to the S3 bucket. Use the command `aws s3 cp` for CLI or appropriate methods for SDK to transfer files securely. Ensure that the bucket policies and IAM roles are correctly configured to allow data uploads.
Set up AWS Glue to catalog the data you uploaded to S3. Create a Glue Crawler that will automatically scan the data in the S3 bucket and populate the AWS Glue Data Catalog with metadata. This step is crucial as it makes the data discoverable and queryable using AWS services like Athena.
After cataloging, use AWS Athena to query the data. Athena allows you to run SQL queries directly on the data stored in S3. Verify that the Glue catalog is correctly linked to Athena, allowing you to perform queries and validate the integrity and structure of the data.
To maintain a steady flow of data from AppsFlyer to AWS, consider automating the entire process. Use AWS Lambda functions to trigger data extraction, transformation, and loading processes. AWS CloudWatch can be used to schedule these functions or to monitor the workflow, ensuring the data lake remains up-to-date with the latest data from AppsFlyer.
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.
AppsFlyer is a mobile attribution and marketing analytics platform that helps businesses measure and optimize their mobile app marketing campaigns. It provides real-time data and insights on user acquisition, engagement, retention, and revenue, allowing businesses to make data-driven decisions to improve their app performance and ROI. AppsFlyer's platform integrates with over 5,000 partners, including ad networks, social media platforms, and analytics tools, to provide a comprehensive view of the entire mobile app marketing ecosystem. With its advanced fraud protection and privacy compliance features, AppsFlyer ensures that businesses can trust their data and protect their users' privacy.
AppsFlyer's API provides access to a wide range of data related to mobile app marketing and user engagement. The following are the categories of data that can be accessed through the API:
1. Attribution data: This includes information about the source of app installs, such as the ad network, campaign, and creative.
2. In-app events data: This includes data about user actions within the app, such as purchases, registrations, and other custom events.
3. Retargeting data: This includes data about users who have engaged with the app in the past and can be targeted with specific campaigns.
4. Audience data: This includes data about the characteristics of app users, such as demographics, interests, and behaviors.
5. Ad revenue data: This includes data about the revenue generated by ads within the app, such as impressions, clicks, and conversions.
6. Fraud prevention data: This includes data about potential fraudulent activity, such as fake installs or clicks.
7. Raw data: This includes all of the above data in its raw form, allowing for custom analysis and reporting.
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:
