Loading Klarna
Data to Dremio
Using dlt
in Python
Join our Slack community or book a call with our support engineer Violetta.
Klarna
is a global payment solutions provider offering seamless online payment services for businesses and consumers. Klarna
provides tools for payment processing, including 'buy now, pay later' options, installment plans, and direct payments. With Klarna
, businesses can offer flexible payment solutions, improve customer satisfaction, and increase conversion rates. Dremio
is the only data lakehouse solution that meets leaders at all stages of their data journey, providing lakehouse flexibility, scalability, and performance. This documentation covers how to load data from Klarna
to Dremio
using the open-source Python library dlt
. The guide will walk you through the steps required to extract, transform, and load your Klarna
data efficiently into Dremio
. For further information about Klarna
, visit here.
dlt
Key Features
- Scalability via iterators, chunking, and parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Learn more - Implicit extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and their transformations automatically. Learn more - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Learn more - Schema evolution:
dlt
enables proactive governance by alerting users to schema changes, such as table or column alterations. Learn more - Automated maintenance: With schema inference, evolution, and alerts,
dlt
offers automated maintenance, making it simple to manage. Learn 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 dremio 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 Dremio
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: Automate your pipeline deployment using GitHub Actions, a free CI/CD runner. Learn more here.
- Deploy with Airflow: Use Google Composer or any Airflow instance to manage your pipeline. Follow the guide here.
- Deploy with Google Cloud Functions: Utilize Google Cloud Functions for serverless pipeline deployment. Detailed instructions are available here.
- Explore other deployment options: Find more ways to deploy your pipeline, including various cloud services and platforms, 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 smooth and reliable operation in production. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified about critical issues or changes in your
dlt
pipeline. Set up alerts - Set up tracing: Implement tracing to track the execution and performance of your
dlt
pipeline, making it easier to diagnose issues. 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 |
Additional pipeline guides
- Load data from Slack to BigQuery in python with dlt
- Load data from Airtable to Azure Cosmos DB in python with dlt
- Load data from Star Trek to DuckDB in python with dlt
- Load data from Slack to AlloyDB in python with dlt
- Load data from ClickHouse Cloud to Azure Cosmos DB in python with dlt
- Load data from Soundcloud to The Local Filesystem in python with dlt
- Load data from Zendesk to AlloyDB in python with dlt
- Load data from Klarna to CockroachDB in python with dlt
- Load data from Box Platform API to The Local Filesystem in python with dlt
- Load data from AWS S3 to DuckDB in python with dlt