Loading Data from GitLab
to Azure Cloud Storage
with dlt
in Python
Join our Slack community or book a call with our support engineer Violetta.
This documentation provides a guide on loading data from GitLab
to Azure Cloud Storage
using the open-source Python library dlt
. GitLab
is a web-based DevOps lifecycle tool that offers a Git repository manager, wiki, issue-tracking, and CI/CD pipeline features under an open-source license. Azure Cloud Storage
is a destination that stores data on Microsoft Azure, enabling the creation of data lakes. You can upload data in formats such as JSONL, Parquet, or CSV. This guide will walk you through the steps to integrate these tools using dlt
. For more information on GitLab
, visit here.
dlt
Key Features
Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage.Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more about adjusting a schema.Schema Evolution:
dlt
enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations,dlt
notifies stakeholders, allowing them to take necessary actions, such as reviewing and validating the changes, updating downstream processes, or performing impact analysis.Scaling and Finetuning:
dlt
offers several mechanisms and configuration options to scale up and finetune pipelines, including running extraction, normalization, and load in parallel, and writing sources and resources that are run in parallel via thread pools and async execution. Read more about performance.Advanced Topics:
dlt
is a constantly growing library that supports many features and use cases needed by the community. Join our Slack to find recent releases or discuss what you can build withdlt
. Build a pipeline tutorial.
Getting started with your pipeline locally
dlt-init-openapi
0. Prerequisites
dlt
and dlt-init-openapi
requires Python 3.9 or higher. Additionally, you need to have the pip
package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.
1. Install dlt and dlt-init-openapi
First you need to install the dlt-init-openapi
cli tool.
pip install dlt-init-openapi
The dlt-init-openapi
cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt
source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.
# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you
# can just hit Enter and all will be rendered.
dlt-init-openapi gitlab --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Public/gitlab.yaml --global-limit 2
cd gitlab_pipeline
# install generated requirements
pip install -r requirements.txt
The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt
:
dlt>=0.4.12
You now have the following folder structure in your project:
gitlab_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── gitlab/
│ └── __init__.py # TODO: possibly tweak this file
├── gitlab_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak gitlab/__init__.py
This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api
source set up in a different way. The generated file for the gitlab source will look like this:
Click to view full file (579 lines)
from typing import List
import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig
@dlt.source(name="gitlab_source", max_table_nesting=2)
def gitlab_source(
api_key: str = dlt.secrets.value,
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
"auth": {
"type": "api_key",
"api_key": api_key,
"name": "Private-Token",
"location": "header"
},
"paginator": {
"type":
"page_number",
"page_param":
"page",
"total_path":
"",
"maximum_page":
20,
},
},
"resources":
[
# Get the current appearance
{
"name": "get_api_v4_application_appearance",
"table_name": "api_entities_appearance",
"endpoint": {
"data_selector": "$",
"path": "/application/appearance",
}
},
# List all registered applications
{
"name": "get_api_v4_applications",
"table_name": "api_entities_application",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/applications",
}
},
# Return avatar url for a user
{
"name": "get_api_v4_avatar",
"table_name": "api_entities_avatar",
"endpoint": {
"data_selector": "$",
"path": "/avatar",
"params": {
"email": "FILL_ME_IN", # TODO: fill in required query parameter
# the parameters below can optionally be configured
# "size": "OPTIONAL_CONFIG",
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_groups_id_badges_badge_id",
"table_name": "api_entities_badge",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/groups/{id}/badges/{badge_id}",
"params": {
"badge_id": {
"type": "resolve",
"resource": "get_api_v4_groups_id_badges",
"field": "id",
},
"id": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_groups_id_badges",
"table_name": "api_entities_badge",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/groups/{id}/badges",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "per_page": "20",
# "name": "OPTIONAL_CONFIG",
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_projects_id_badges_badge_id",
"table_name": "api_entities_badge",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/badges/{badge_id}",
"params": {
"badge_id": {
"type": "resolve",
"resource": "get_api_v4_projects_id_badges",
"field": "id",
},
"id": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_projects_id_badges",
"table_name": "api_entities_badge",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/badges",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "per_page": "20",
# "name": "OPTIONAL_CONFIG",
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_groups_id_badges_render",
"table_name": "api_entities_basic_badge_details",
"endpoint": {
"data_selector": "$",
"path": "/groups/{id}/badges/render",
"params": {
"id": {
"type": "resolve",
"resource": "get_api_v4_groups_id_badges",
"field": "id",
},
"link_url": "FILL_ME_IN", # TODO: fill in required query parameter
"image_url": "FILL_ME_IN", # TODO: fill in required query parameter
},
}
},
# This feature was introduced in GitLab 10.6.
{
"name": "get_api_v4_projects_id_badges_render",
"table_name": "api_entities_basic_badge_details",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/badges/render",
"params": {
"id": {
"type": "resolve",
"resource": "get_api_v4_projects_id_badges",
"field": "id",
},
"link_url": "FILL_ME_IN", # TODO: fill in required query parameter
"image_url": "FILL_ME_IN", # TODO: fill in required query parameter
},
}
},
# Retrieve a batched background migration
{
"name": "get_api_v4_admin_batched_background_migrations_id",
"table_name": "api_entities_batched_background_migration",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/batched_background_migrations/{id}",
"params": {
"id": {
"type": "resolve",
"resource": "get_api_v4_admin_batched_background_migrations",
"field": "id",
},
# the parameters below can optionally be configured
# "database": "main",
},
}
},
# Get the list of batched background migrations
{
"name": "get_api_v4_admin_batched_background_migrations",
"table_name": "api_entities_batched_background_migration",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/batched_background_migrations",
"params": {
# the parameters below can optionally be configured
# "database": "main",
},
}
},
# Get a single repository branch
{
"name": "get_api_v4_projects_id_repository_branches_branch",
"table_name": "api_entities_branch",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/repository/branches/{branch}",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
"branch": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# Get a project repository branches
{
"name": "get_api_v4_projects_id_repository_branches",
"table_name": "api_entities_branch",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/repository/branches",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "per_page": "20",
# "search": "OPTIONAL_CONFIG",
# "regex": "OPTIONAL_CONFIG",
# "sort": "OPTIONAL_CONFIG",
# "page_token": "OPTIONAL_CONFIG",
},
}
},
# This feature was introduced in GitLab 8.12.
{
"name": "get_api_v4_broadcast_messages_id",
"table_name": "api_entities_broadcast_message",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/broadcast_messages/{id}",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# This feature was introduced in GitLab 8.12.
{
"name": "get_api_v4_broadcast_messages",
"table_name": "api_entities_broadcast_message",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/broadcast_messages",
"params": {
# the parameters below can optionally be configured
# "per_page": "20",
},
}
},
# This feature was introduced in GitLab 14.1.
{
"name": "get_api_v4_bulk_imports_import_id",
"table_name": "api_entities_bulk_import",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/bulk_imports/{import_id}",
"params": {
"import_id": {
"type": "resolve",
"resource": "get_api_v4_bulk_imports",
"field": "id",
},
},
}
},
# This feature was introduced in GitLab 14.1.
{
"name": "get_api_v4_bulk_imports",
"table_name": "api_entities_bulk_import",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/bulk_imports",
"params": {
# the parameters below can optionally be configured
# "per_page": "20",
# "sort": "desc",
# "status": "OPTIONAL_CONFIG",
},
}
},
# This feature was introduced in GitLab 14.1.
{
"name": "get_api_v4_bulk_imports_import_id_entities_entity_id",
"table_name": "api_entities_bulk_imports",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/bulk_imports/{import_id}/entities/{entity_id}",
"params": {
"entity_id": {
"type": "resolve",
"resource": "get_api_v4_bulk_imports_import_id_entities",
"field": "id",
},
"import_id": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# This feature was introduced in GitLab 14.1.
{
"name": "get_api_v4_bulk_imports_import_id_entities",
"table_name": "api_entities_bulk_imports",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/bulk_imports/{import_id}/entities",
"params": {
"import_id": {
"type": "resolve",
"resource": "get_api_v4_bulk_imports",
"field": "id",
},
# the parameters below can optionally be configured
# "status": "OPTIONAL_CONFIG",
# "per_page": "20",
},
}
},
# This feature was introduced in GitLab 14.1.
{
"name": "get_api_v4_bulk_imports_entities",
"table_name": "api_entities_bulk_imports",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/bulk_imports/entities",
"params": {
# the parameters below can optionally be configured
# "per_page": "20",
# "sort": "desc",
# "status": "OPTIONAL_CONFIG",
},
}
},
# Get the details of a specific instance-level variable
{
"name": "get_api_v4_admin_ci_variables_key",
"table_name": "api_entities_ci_variable",
"primary_key": "key",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/ci/variables/{key}",
"params": {
"key": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# List all instance-level variables
{
"name": "get_api_v4_admin_ci_variables",
"table_name": "api_entities_ci_variable",
"endpoint": {
"data_selector": "$",
"path": "/admin/ci/variables",
"params": {
# the parameters below can optionally be configured
# "per_page": "20",
},
}
},
# This feature was introduced in GitLab 13.2. Returns a single instance cluster.
{
"name": "get_api_v4_admin_clusters_cluster_id",
"table_name": "api_entities_cluster",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/clusters/{cluster_id}",
"params": {
"cluster_id": {
"type": "resolve",
"resource": "get_api_v4_admin_clusters",
"field": "id",
},
},
}
},
# This feature was introduced in GitLab 13.2. Returns a list of instance clusters.
{
"name": "get_api_v4_admin_clusters",
"table_name": "api_entities_cluster",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/clusters",
}
},
# This feature was introduced in GitLab 8.11.
{
"name": "get_api_v4_groups_id_access_requests",
"table_name": "api_entities_custom_attribute",
"endpoint": {
"data_selector": "custom_attributes",
"path": "/groups/{id}/access_requests",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "per_page": "20",
},
}
},
# This feature was introduced in GitLab 8.11.
{
"name": "get_api_v4_projects_id_access_requests",
"table_name": "api_entities_custom_attribute",
"endpoint": {
"data_selector": "custom_attributes",
"path": "/projects/{id}/access_requests",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "per_page": "20",
},
}
},
# Retrieve dictionary details
{
"name": "get_api_v4_admin_databases_database_name_dictionary_tables_table_name",
"table_name": "api_entities_dictionary_table",
"primary_key": "table_name",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/admin/databases/{database_name}/dictionary/tables/{table_name}",
"params": {
"database_name": "FILL_ME_IN", # TODO: fill in required path parameter
"table_name": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
{
"name": "list_project_jobs",
"table_name": "api_entities_job",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/jobs",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "scope": "OPTIONAL_CONFIG",
},
}
},
{
"name": "get_single_job",
"table_name": "api_entities_job",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/jobs/{job_id}",
"params": {
"job_id": {
"type": "resolve",
"resource": "list_project_jobs",
"field": "id",
},
"id": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# This feature was introduced in GitLab 15.2.
{
"name": "get_api_v4_metadata",
"table_name": "api_entities_metadata",
"endpoint": {
"data_selector": "$",
"path": "/metadata",
}
},
# This feature was introduced in GitLab 8.13 and deprecated in 15.5. We recommend you instead use the Metadata API.
{
"name": "get_api_v4_version",
"table_name": "api_entities_metadata",
"endpoint": {
"data_selector": "$",
"path": "/version",
}
},
# Metric Images for alert
{
"name": "get_api_v4_projects_id_alert_management_alerts_alert_iid_metric_images",
"table_name": "api_entities_metric_image",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/projects/{id}/alert_management_alerts/{alert_iid}/metric_images",
"params": {
"id": "FILL_ME_IN", # TODO: fill in required path parameter
"alert_iid": "FILL_ME_IN", # TODO: fill in required path parameter
},
}
},
# List the current limits of a plan on the GitLab instance.
{
"name": "get_api_v4_application_plan_limits",
"table_name": "api_entities_plan_limit",
"endpoint": {
"data_selector": "$",
"path": "/application/plan_limits",
"params": {
# the parameters below can optionally be configured
# "plan_name": "default",
},
}
},
]
}
return rest_api_source(source_config)
2. Configuring your source and destination credentials
dlt-init-openapi
will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml
.
The dlt
cli will have created a .dlt
directory in your project folder. This directory contains a config.toml
file and a secrets.toml
file that you can use to configure your pipeline. The automatically created version of these files look like this:
generated config.toml
[runtime]
log_level="INFO"
[sources.gitlab]
# Base URL for the API
base_url = "https://www.gitlab.com/api/v4"
generated secrets.toml
[sources.gitlab]
# secrets for your gitlab source
api_key = "FILL ME OUT" # TODO: fill in your credentials
2.1. Adjust the generated code to your usecase
At this time, the dlt-init-openapi
cli tool will always create pipelines that load to a local duckdb
instance. Switching to a different destination is trivial, all you need to do is change the destination
parameter in gitlab_pipeline.py
to filesystem and supply the credentials as outlined in the destination doc linked below.
The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials]
section in your secrets.toml
.
[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"
By default, the filesystem destination will store your files as JSONL
. You can tell your pipeline to choose a different format with the loader_file_format
property that you can set directly on the pipeline or via your config.toml
. Available values are jsonl
, parquet
and csv
:
[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at gitlab_pipeline.py
, as well as a folder gitlab
that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.
The main pipeline script will look something like this:
import dlt
from gitlab import gitlab_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="gitlab_pipeline",
destination='duckdb',
dataset_name="gitlab_data",
progress="log",
export_schema_path="schemas/export"
)
source = gitlab_source()
info = pipeline.run(source)
print(info)
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python gitlab_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline gitlab_pipeline info
You can also use streamlit to inspect the contents of your Azure Cloud Storage
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline gitlab_pipeline show
5. Next steps to get your pipeline running in production
One of the beauties of dlt
is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:
The Deploy section will show you how to deploy your pipeline to
- Deploy with GitHub Actions: Learn how to deploy your pipeline using GitHub Actions. Follow the step-by-step guide to set up automated workflows. Read more
- Deploy with Airflow and Google Composer: Discover how to deploy your pipeline with Airflow and Google Composer. This guide provides detailed instructions to get your pipeline running in a managed Airflow environment. Read more
- Deploy with Google Cloud Functions: Understand how to deploy your pipeline using Google Cloud Functions. This walkthrough helps you set up serverless functions for your pipeline. Read more
- Explore other deployment options: Check out various other methods to deploy your pipeline, including different cloud providers and orchestration tools. Read more
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline to ensure it runs smoothly in production. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified of any issues or changes in your
dlt
pipeline, helping you maintain a robust data workflow. Set up alerts - Set up tracing: Implement tracing to gain insights into the performance and execution of your
dlt
pipeline, making debugging and optimization easier. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source GitLab
Loads various GitLab API data including badges, clusters, jobs, and metadata.
Resource Name | Write Disposition | Description |
---|---|---|
api_entities_badge | append | Represents badges associated with GitLab entities. |
api_entities_basic_badge_details | append | Contains basic details about badges in GitLab. |
api_entities_batched_background_migration | append | Information about batched background migrations. |
api_entities_cluster | append | Details about clusters in GitLab. |
api_entities_dictionary_table | append | Represents dictionary tables used in GitLab. |
api_entities_metric_image | append | Stores metric images used in GitLab. |
api_entities_broadcast_message | append | Broadcast messages sent within GitLab. |
api_entities_avatar | append | Information about avatars in GitLab. |
api_entities_plan_limit | append | Limits associated with different plans in GitLab. |
api_entities_custom_attribute | append | Custom attributes assigned to GitLab entities. |
api_entities_job | append | Details about jobs executed in GitLab CI/CD pipelines. |
api_entities_metadata | append | Metadata related to various GitLab entities. |
api_entities_application | append | Information about applications integrated with GitLab. |
api_entities_branch | append | Details about branches in Git repositories managed by GitLab. |
api_entities_appearance | append | Appearance settings and customizations in GitLab. |
api_entities_bulk_imports | append | Information about bulk imports executed in GitLab. |
api_entities_ci_variable | append | CI/CD variables used in GitLab pipelines. |
api_entities_bulk_import | append | Details about individual bulk import operations in GitLab. |
Additional pipeline guides
- Load data from Clubhouse to CockroachDB in python with dlt
- Load data from Cisco Meraki to The Local Filesystem in python with dlt
- Load data from Google Cloud Storage to Dremio in python with dlt
- Load data from Cisco Meraki to YugabyteDB in python with dlt
- Load data from Spotify to PostgreSQL in python with dlt
- Load data from Google Sheets to Google Cloud Storage in python with dlt
- Load data from HubSpot to Microsoft SQL Server in python with dlt
- Load data from SAP HANA to BigQuery in python with dlt
- Load data from Aladtec to EDB BigAnimal in python with dlt
- Load data from Google Analytics to Azure Cosmos DB in python with dlt