How to load data from Gitlab to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Gitlab data into Databricks Lakehouse within minutes.
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.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- 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
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
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."
How to Sync to Manually
Step 1: Export Data from GitLab
Begin by exporting the data you need from GitLab. This can be done through GitLab's web interface by navigating to the project you wish to export. In the project settings, look for the "Export project" option, which will package the repository, issues, merge requests, and other metadata into a downloadable file, typically in a `.tar.gz` format.
Step 2: Extract Data Locally
After downloading the exported file, extract it on your local machine. Use a command like `tar -xvzf .tar.gz` to unpack the contents. This will give you access to the raw data files and directories, which often include JSON, CSV, or other format files containing the required data.
Step 3: Transform Data for Compatibility
Once extracted, review the data structure and format it as needed for compatibility with Databricks. Convert or clean these files using scripting languages such as Python or Shell scripts, ensuring the data aligns with the schema you intend to use in Databricks. This might involve converting JSON to CSV, flattening data structures, or cleaning up data entries.
Step 4: Install Databricks CLI
To interact with Databricks from your local environment, install the Databricks Command Line Interface (CLI). You can do this using pip with the command `pip install databricks-cli`. The CLI will facilitate file uploads to the Databricks environment.
Step 5: Authenticate with Databricks CLI
Configure the Databricks CLI by setting up authentication. Use the command `databricks configure --token` and provide the necessary information, such as your Databricks host URL and a personal access token, which can be generated from your Databricks account settings.
Step 6: Upload Data to Databricks File System (DBFS)
Use the Databricks CLI to upload your transformed data files to the Databricks File System (DBFS). This can be done using the command `databricks fs cp dbfs:/` for each file or directory you need to move. Ensure all files are correctly placed in the desired directory within DBFS.
Step 7: Load Data into Databricks Lakehouse
Finally, within your Databricks workspace, create a notebook to load the data from DBFS into the Lakehouse. Use Spark DataFrame operations to read the files you uploaded. For example, use `spark.read.csv("dbfs://.csv")` or `spark.read.json()` depending on your file format. Transform and save the DataFrame as a table in Databricks Lakehouse to begin using the data.
By following these steps, you can effectively move data from GitLab to Databricks Lakehouse without relying on third-party connectors or integrations.