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."
Braze allows you to export data using their built-in data export functionality. Use the Braze API to initiate data export. You can export data such as user engagement, campaign performance, or user profiles in CSV or JSON format. Begin by setting up an API key in Braze, then use the Braze API to generate the export request. Be sure to specify the data fields you need and the format (CSV or JSON).
Once the export process is complete, download the data files from the Braze dashboard or via the API. You can script this process using a language like Python to automate downloading files to a local or remote server. Ensure you have authentication set up correctly to access the files.
Before importing the data into MSSQL, you should clean and prepare it. If the data is in CSV format, check for any inconsistencies or errors such as missing values or incorrect data types. Use a scripting language like Python or a data processing tool like Pandas to handle any necessary data transformation, such as renaming columns or converting data types to match your MSSQL schema.
Set up your MSSQL database and create the necessary table(s) to hold the data. Define the schema based on the structure of the data you exported from Braze. Ensure that the data types in MSSQL correspond to those in the export files to prevent errors during the import process.
If your data is in JSON format, you will need to convert it into a format that MSSQL can import, such as CSV or directly to SQL INSERT statements. For CSV files, ensure they are properly delimited and enclosed to handle any text fields correctly. Tools like csvkit or custom scripts can help in this conversion process.
Utilize the Bulk Copy Program (BCP) utility, a command-line tool provided by Microsoft, to import the prepared data into MSSQL. BCP allows you to import large volumes of data efficiently. Construct the BCP command with the necessary parameters to target your MSSQL database and table, and execute the import. Handle any potential errors by checking the BCP output logs.
After the data import process, validate that the data in MSSQL matches the original data from Braze. Run queries to check row counts, data types, and sample data values to ensure integrity. Conduct any additional quality assurance checks to confirm that the data has been accurately and completely transferred. Make necessary corrections if discrepancies are found.
By following these steps, you can successfully transfer data from Braze to an MSSQL destination 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.
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:
