How to load data from Gitlab to AWS Datalake

Learn how to use Airbyte to synchronize your Gitlab data into AWS Datalake within minutes.

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Set up a Gitlab connector in Airbyte

Connect to Gitlab or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up AWS Datalake for your extracted Gitlab data

Select AWS Datalake where you want to import data from your Gitlab source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Gitlab to AWS Datalake in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Gitlab as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Gitlab

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.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Gitlab with AWS Datalake in minutes

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Prerequisites

A Gitlab account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Gitlab and AWS Datalake, for seamless data migration.

When using Airbyte to move data from Gitlab to AWS Datalake, it extracts data from Gitlab using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their Gitlab data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Gitlab as a source connector

1. First, navigate to the GitLab source connector page on Airbyte.com.

2. Click on the "Add Source" button to begin the process of adding your GitLab credentials.

3. In the "Connection Configuration" section, enter a name for your GitLab connection.

4. Next, enter your GitLab API token in the "Personal Access Token" field. You can generate a new token in your GitLab account settings.

5. In the "GitLab URL" field, enter the URL for your GitLab instance.

6. In the "Project ID" field, enter the ID of the project you want to connect to. You can find this ID in the URL of the project page on GitLab.

7. If you want to include only certain branches or tags in your data sync, you can specify them in the "Branches" and "Tags" fields.

8. Finally, click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your GitLab instance.

9. If the test is successful, click on the "Save" button to save your GitLab connection.

10. You can now use this connection to create a new GitLab source in Airbyte and begin syncing your data.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Gitlab data to AWS Datalake

Once you've successfully connected Gitlab as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Gitlab from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Gitlab objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Gitlab to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Gitlab data.

Use Cases to transfer your Gitlab data to AWS Datalake

Integrating data from Gitlab to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Gitlab data, extracting insights that wouldn't be possible within Gitlab alone.
Data Consolidation: If you're using multiple other sources along with Gitlab, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Gitlab has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Gitlab data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Gitlab data.
Data Science and Machine Learning: By having Gitlab data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Gitlab provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Gitlab table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Gitlab account as an Airbyte data source connector.
Configure AWS Datalake as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Gitlab to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Gitlab to AWS Datalake Manually

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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.

What is Gitlab?

What data can you extract from Gitlab?

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.

How do I transfer data from Gitlab?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up GitLab to AWS Datalake as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from GitLab to AWS Datalake and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

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Warehouses and Lakes

Engineering Analytics

How to load data from Gitlab to AWS Datalake

Learn how to use Airbyte to synchronize your Gitlab data into AWS Datalake within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Gitlab and AWS Datalake

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Gitlab as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Gitlab

What is AWS Datalake

Integrate Gitlab with AWS Datalake in minutes

Try for free now

Prerequisites

A Gitlab account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.