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."
To begin, ensure you have access to your Firebase project. Navigate to the Firebase console and select your project. In the "Realtime Database" section, go to the "Data" tab and ensure you have the necessary permissions to read data. You may need to generate a private key for server access by navigating to the "Project Settings" > "Service Accounts" > "Generate New Private Key". Download this key as it will be needed for authentication.
Install the Firebase Admin SDK in your local environment. This can be done using Node.js by running `npm install firebase-admin`. Initialize the SDK in your script using the private key generated in the previous step. This will allow you to authenticate and interact with your Firebase Realtime Database programmatically.
Use the Firebase Admin SDK to read data from your Realtime Database. Write a script to connect to the database and retrieve the data. This can be done using methods provided by the SDK, such as `admin.database().ref('/path/to/data').once('value')` to fetch data at specific nodes. Make sure to handle the data appropriately, either by storing it in a local structure or directly preparing it for transformation.
Once the data is extracted, transform it into a format that is compatible with TiDB. This typically involves converting JSON data from Firebase into SQL insert statements or a structured format like CSV. During this transformation, ensure that data types in Firebase are mapped correctly to TiDB types (e.g., strings to VARCHAR, numbers to INT).
Ensure you have access to your TiDB cluster. This involves setting up a connection using a MySQL client or command-line tool since TiDB is MySQL-compatible. Obtain your TiDB credentials, including hostname, port, username, and password. Test the connection to ensure you can successfully connect to your TiDB database.
With the data transformed into a compatible format, write a script or use a command-line tool to load the data into TiDB. If using a script, establish a connection to TiDB using a MySQL client library (such as `mysql-connector` for Python or `mysql2` for Node.js). Execute SQL `INSERT` statements or use `LOAD DATA` commands to import CSV files into your TiDB tables. Ensure that the data schema in TiDB matches the structure of your transformed data.
After loading the data into TiDB, verify that the data transfer was successful. Run queries to check that the number of records and key data points match between Firebase and TiDB. Perform data integrity checks to ensure that no data was lost or corrupted during the transfer process. Address any discrepancies as needed to maintain accurate and consistent data across both platforms.
By following these steps, you can manually transfer data from Firebase Realtime Database to TiDB 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.
The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.
Firebase's API gives access to a wide range of data types, including:
1. Real-time database: This includes data that is stored in real-time and can be accessed and updated in real-time.
2. Cloud Firestore: This is a NoSQL document database that stores data in documents and collections.
3. Authentication: This includes user data such as email, password, and authentication tokens.
4. Cloud Storage: This includes data such as images, videos, and other files that are stored in the cloud.
5. Cloud Functions: This includes data that is processed by serverless functions in the cloud.
6. Cloud Messaging: This includes data related to push notifications and messaging.
7. Analytics: This includes data related to user behavior and app usage.
8. Performance Monitoring: This includes data related to app performance and user experience.
9. Remote Config: This includes data related to app configuration and feature flags.
Overall, Firebase's API provides access to a wide range of data types that are essential for building modern web and mobile applications.
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:
