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 your data from Amplitude. Log into your Amplitude account, navigate to the "Data" section, and select the dataset you want to export. Use the export feature to download your data in a CSV or JSON format, which are commonly supported by Amplitude.
Once the data is exported, ensure that it is formatted correctly for transformation. If necessary, clean the data by removing any unwanted columns or rows and ensure that the data types for each field are consistent and accurate for the intended MongoDB schema.
Set up a local environment to process the data. Install the necessary tools such as Python or Node.js, which will help you script the transformation process. Ensure you have access to tools for reading CSV/JSON files, such as Python's `pandas` library or Node.js's `fs` module.
Write a script to transform the exported data into a format suitable for MongoDB, typically BSON (Binary JSON). This involves parsing the CSV or JSON data, restructuring it according to your MongoDB schema, and serializing it into BSON format, if necessary.
Prepare your MongoDB environment to receive the data. Install MongoDB on your local machine or set up a cloud-based MongoDB instance. Create a new database and define the necessary collections where the data will be stored. Ensure the database is configured to handle the expected data volume and structure.
Use a script to insert the transformed data into MongoDB. Utilize MongoDB's native drivers for your chosen programming language (e.g., `pymongo` for Python or `mongodb` package for Node.js) to connect to your MongoDB instance and perform the data insertion. Test the insertion process with a small subset of data before executing the full import.
After the data is imported, verify and validate the integrity of the data within MongoDB. Run queries to ensure that the data is correctly inserted and that there are no discrepancies or data loss. Check for consistency with the original dataset in Amplitude and perform any necessary data integrity checks.
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.
Amplitude is a cross-platform product intelligence solution that helps companies accelerate growth by leveraging customer data to build optimum product experiences. Advertised as the digital optimization system that “helps companies build better products,” it enables companies to make informed decisions by showing how a company’s digital products drive business. Amplitude employs a proprietary Amplitude Behavioral Graph to show customers the impact of various combinations of features and actions on business outcomes.
Amplitude's API provides access to a wide range of data related to user behavior and engagement on digital platforms. The following are the categories of data that can be accessed through Amplitude's API:
1. User data: This includes information about individual users such as their demographics, location, and device type.
2. Event data: This includes data related to user actions such as clicks, page views, and purchases.
3. Session data: This includes information about user sessions such as the duration of the session and the number of events that occurred during the session.
4. Funnel data: This includes data related to user behavior in a specific sequence of events, such as a checkout funnel.
5. Retention data: This includes data related to user retention, such as the percentage of users who return to the platform after a certain period of time.
6. Revenue data: This includes data related to revenue generated by the platform, such as the total revenue and revenue per user.
7. Cohort data: This includes data related to groups of users who share a common characteristic, such as the date they signed up for the platform.
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:
