Load Klarna Data to Azure Cosmos DB Using dlt
in Python
We will be using the dlt PostgreSQL destination to connect to Azure Cosmos DB. You can get the connection string for your Azure Cosmos DB database as described in the Azure Cosmos DB Docs.
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Loading data from Klarna
to Azure Cosmos DB
can be efficiently handled using the open-source python library dlt
. Klarna
is a global payment solutions provider that offers seamless online payment services for businesses and consumers, including 'buy now, pay later' options, installment plans, and direct payments. On the other hand, Azure Cosmos DB
is a fully managed NoSQL and relational database designed for modern app development. By leveraging dlt
, you can seamlessly transfer and manage your Klarna
data within Azure Cosmos DB
, enabling flexible payment solutions, improving customer satisfaction, and increasing conversion rates. For more information on Klarna
, visit their website.
dlt
Key Features
- Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality by adhering to predefined schemas. Read more - Scalability via Iterators, Chunking, and Parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques for efficient processing of large datasets. Read more - Implicit Extraction DAGs:
dlt
automatically generates an extraction DAG based on the dependencies identified between data sources and their transformations, ensuring data consistency and integrity. Read more - Schema Evolution Alerts:
dlt
enables proactive governance by alerting users to schema changes, allowing stakeholders to take necessary actions such as reviewing and validating the changes. Read more
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 klarna --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/klarna.yaml --global-limit 2
cd klarna_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:
klarna_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── klarna/
│ └── __init__.py # TODO: possibly tweak this file
├── klarna_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak klarna/__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 klarna source will look like this:
Click to view full file (39 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="klarna_source", max_table_nesting=2)
def klarna_source(
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
},
"resources":
[
# Use this API call to get a Klarna Payments session. You can read the Klarna Payments session at any time after it has been created, to get information about it. This will return all data that has been collected during the session. Read more on **[Read an existing payment session](https://docs.klarna.com/klarna-payments/other-actions/check-the-details-of-a-payment-session/)**.
{
"name": "session_read",
"table_name": "session_read",
"endpoint": {
"data_selector": "$",
"path": "/payments/v1/sessions/{session_id}",
"params": {
"session_id": "FILL_ME_IN", # TODO: fill in required path parameter
},
"paginator": "auto",
}
},
]
}
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.klarna]
# Base URL for the API
base_url = "https://api.klarna.com"
generated secrets.toml
[sources.klarna]
# secrets for your klarna source
# example_api_key = "example value"
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 klarna_pipeline.py
to postgres and supply the credentials as outlined in the destination doc linked below.
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at klarna_pipeline.py
, as well as a folder klarna
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 klarna import klarna_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="klarna_pipeline",
destination='duckdb',
dataset_name="klarna_data",
progress="log",
export_schema_path="schemas/export"
)
source = klarna_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 klarna_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline klarna_pipeline info
You can also use streamlit to inspect the contents of your Azure Cosmos DB
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline klarna_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 a pipeline using GitHub Actions by following this guide.
- Deploy with Airflow and Google Composer: Follow this guide to deploy a pipeline with Airflow and Google Composer.
- Deploy with Google Cloud Functions: This guide explains how to deploy a pipeline using Google Cloud Functions.
- More Deployment Options: Explore other deployment options and guides here.
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, ensuring timely responses to potential problems. Set up alerts - Set up tracing: Implement tracing to track the flow of data and operations within your
dlt
pipeline, aiding in debugging and performance optimization. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source Klarna
Klarna source for accessing session data and related analytics.
Resource Name | Write Disposition | Description |
---|---|---|
session_read | append | Session data including user interactions and payment sessions |
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