

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 Braze. Braze allows you to export user data and events via their API. Utilize the Braze API to extract the necessary datasets. You can schedule data exports using Braze�s Currents feature or directly call the `/users/export/ids` endpoint for user data or `/events/data_series` for event data. Make sure to configure your requests to output data in a format such as JSON or CSV.
Prepare a local or cloud-based environment where you can process the exported data. This could be a server with Python, Java, or any data-processing toolset installed. Ensure that your environment has access to the exported files and is capable of running scripts to transform and load data.
Convert the exported Braze data into a format compatible with Apache Iceberg. Iceberg commonly uses Parquet or Avro file formats. Write a script using Python (Pandas library), Apache Spark, or another ETL tool to transform the JSON or CSV data into Parquet or Avro format. This transformation ensures optimal storage and querying performance in Iceberg.
Set up Apache Iceberg in your data processing environment. You can use Apache Iceberg with a variety of compute engines like Apache Spark or Apache Flink. Install Iceberg libraries in your chosen compute engine environment. For instance, if using Spark, ensure you have the Iceberg Spark runtime included in your Spark session.
Define the schema for your Iceberg tables to match the structure of the transformed Braze data. This involves specifying column names, data types, and any partitioning strategy you wish to use. Use Apache Iceberg�s API to create the table schema. This can typically be done in your processing script using SQL commands or Iceberg API calls.
Load the transformed data into the Iceberg table. Use your compute engine to write data into Iceberg. For example, if using Spark, you can write the Parquet or Avro files into Iceberg tables using Spark DataFrame API like `write.format("iceberg")`. Ensure that your data is correctly partitioned and indexed as per the defined schema.
After loading, verify that the data has been correctly transferred from Braze to Apache Iceberg. Perform data integrity checks by running queries on the Iceberg tables to ensure the data is complete and accurate. Additionally, execute performance tests to confirm that the data can be queried efficiently, adjusting your partitioning strategy if necessary to optimize query performance.
This guide outlines a direct method to move data from Braze to Apache Iceberg without relying on third-party connectors, focusing on manual integration and transformation using available tools and APIs.
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.
Braze is a customer engagement platform that helps businesses build meaningful relationships with their customers. It offers a suite of tools for creating personalized and relevant messaging across multiple channels, including email, push notifications, in-app messaging, and more. With Braze, businesses can track customer behavior and preferences, segment their audience, and deliver targeted campaigns that drive engagement and revenue. The platform also includes advanced analytics and reporting capabilities, allowing businesses to measure the impact of their campaigns and optimize their strategies over time. Overall, Braze helps businesses create more effective and engaging customer experiences that drive loyalty and growth.
Braze's API provides access to a wide range of data related to customer engagement and marketing campaigns. The following are the categories of data that can be accessed through Braze's API:
1. User data: This includes information about individual users such as their name, email address, phone number, and location.
2. Campaign data: This includes data related to marketing campaigns such as email campaigns, push notifications, and in-app messages. It includes information about the campaign's performance, such as open rates, click-through rates, and conversion rates.
3. Event data: This includes data related to user actions such as app installs, purchases, and other interactions with the app or website.
4. Segmentation data: This includes data related to user segments, such as demographics, behavior, and interests.
5. Messaging data: This includes data related to messaging channels such as email, push notifications, and in-app messages. It includes information about message content, delivery, and engagement.
6. Analytics data: This includes data related to user behavior and engagement, such as session length, retention rates, and revenue generated.
Overall, Braze's API provides access to a wealth of data that can be used to optimize marketing campaigns and improve customer engagement.
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: