

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
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


"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."


“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.”

"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 navigating to the GitHub repository that contains the data you wish to move. Ensure you have the necessary permissions to access and download the data. If the repository is private, you will need authentication credentials.
Identify the data files in the repository that need to be transferred. Use GitHub's web interface to manually download these files to your local machine. Click on each file, then select "Download" or "Raw" to save them in the desired format (e.g., CSV, JSON).
Once downloaded, inspect the data files to ensure they are structured correctly for import into MySQL. You may need to clean or reformat the data, ensuring it matches the schema of your MySQL tables. This can involve converting data types, handling missing values, or normalizing data.
Open your MySQL client (like MySQL Workbench or command line) and create a new database if it doesn't already exist. Define tables that match the structure of your data. Use SQL commands to define table columns, data types, and any necessary constraints.
```sql
CREATE DATABASE my_database;
USE my_database;
CREATE TABLE my_table (
id INT PRIMARY KEY,
name VARCHAR(100),
value INT
);
```
Convert your data files into a format suitable for SQL import, such as CSV. Ensure that the data fields are separated by commas and that text fields are properly quoted. You may use scripts or tools like Excel to help format your data correctly.
Use the MySQL `LOAD DATA INFILE` command to import the data from the CSV files into your MySQL tables. This requires the files to be accessible by the MySQL server, so you may need to adjust the file path accordingly.
```sql
LOAD DATA LOCAL INFILE '/path/to/your/file.csv'
INTO TABLE my_table
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 LINES;
```
After importing, run SQL queries to verify the data has been transferred correctly. Check for discrepancies or errors in the data. You may want to count the number of rows, check for null values, or compare against original data samples to ensure integrity.
```sql
SELECT COUNT(*) FROM my_table;
SELECT * FROM my_table WHERE some_column IS NULL;
```
By following these steps, you can manually transfer data from a GitHub repository to a MySQL database 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.
GitHub is a renowned and respected development platform that provides code hosting services to developers for building software for both open source and private projects. It is a heavily trafficked platform where users can store and share code repositories and obtain support, advice, and help from known and unknown contributors. Three features in particular—pull request, fork, and merge—have made GitHub a powerful ally for developers and earned it a place as a (developers’) household name.
GitHub's API provides access to a wide range of data related to repositories, users, organizations, and more. Some of the categories of data that can be accessed through the API include:
- Repositories: Information about repositories, including their name, description, owner, collaborators, issues, pull requests, and more.
- Users: Information about users, including their username, email address, name, location, followers, following, organizations, and more.
- Organizations: Information about organizations, including their name, description, members, repositories, teams, and more.
- Commits: Information about commits, including their SHA, author, committer, message, date, and more.
- Issues: Information about issues, including their title, description, labels, assignees, comments, and more.
- Pull requests: Information about pull requests, including their title, description, status, reviewers, comments, and more.
- Events: Information about events, including their type, actor, repository, date, and more.
Overall, the GitHub API provides a wealth of data that can be used to build powerful applications and tools for developers, businesses, and individuals.
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: