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."
First, identify the data you want to transfer from GitLab. GitLab provides APIs and built-in export features. For issues, merge requests, or similar data, use the GitLab API to query and download data in JSON or CSV format. For a complete project export, use the GitLab UI to download a project export, which includes repository data and other project-related information.
If you haven't already installed DuckDB, download and install it from the official DuckDB website. DuckDB is a lightweight, in-process SQL database management system. You can install it on your local machine or server where you want to process and store the data.
Once you have exported the data from GitLab, clean and structure it if necessary. This may involve converting JSON to CSV or ensuring CSV files have a consistent format. Tools like jq for JSON processing can be helpful. Ensure your data is in a tabular format suitable for SQL ingestion.
Open DuckDB in a terminal or use a DuckDB client. Create a new database or connect to an existing one. Use DuckDB's SQL commands to create tables that match the structure of your data. Then, load the CSV files into these tables using the `COPY` command. For example:
```sql
CREATE TABLE gitlab_data (
id INTEGER,
title STRING,
description STRING,
created_at TIMESTAMP
);
COPY gitlab_data FROM 'path/to/your/data.csv' (FORMAT CSV, HEADER TRUE);
```
After loading the data, verify that it has been transferred correctly by performing simple SQL queries to check counts, data types, and sample records against your original data from GitLab. This ensures that the data in DuckDB mirrors what you exported from GitLab.
With your data successfully loaded, you can now use DuckDB's SQL capabilities to transform and analyze the data as needed. This may include creating views, joining tables, or performing aggregations and calculations. DuckDB's SQL support allows you to perform complex queries efficiently.
Once your data is in DuckDB and you have completed any initial transformations or analyses, back up your database file. DuckDB stores data in a single file, making it easy to copy or move this file to a secure backup location. Regular backups ensure data safety and facilitate disaster recovery.
By following these steps, you can successfully move data from GitLab to DuckDB without relying on third-party tools, maintaining control over your data throughout the process.
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.
GitLab is web-based Git repository manager. Whereas GitHub emphasizes infrastructure performance, GitLab’s focus is a features-oriented system. As an open-source collaborative platform, it enables developers to create code, review work, and deploy codebases collaboratively. It offers wiki, code reviews, built-in CI/CD, issue-tracking features, and much more.
GitLab's API provides access to a wide range of data related to a user's GitLab account and projects. The following are the categories of data that can be accessed through GitLab's API:
1. User data: This includes information about the user's profile, such as name, email, and avatar.
2. Project data: This includes information about the user's projects, such as project name, description, and visibility.
3. Repository data: This includes information about the user's repositories, such as repository name, description, and access level.
4. Issue data: This includes information about the user's issues, such as issue title, description, and status.
5. Merge request data: This includes information about the user's merge requests, such as merge request title, description, and status.
6. Pipeline data: This includes information about the user's pipelines, such as pipeline status, duration, and job details.
7. Job data: This includes information about the user's jobs, such as job status, duration, and artifacts.
8. Group data: This includes information about the user's groups, such as group name, description, and visibility.
Overall, GitLab's API provides access to a comprehensive set of data that can be used to automate and streamline various aspects of a user's GitLab workflow.
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:
