Skip to main content

Loading Klarna Data to Microsoft SQL Server Using python's dlt

Need help deploying these pipelines, or figuring out how to run them in your data stack?

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. Microsoft SQL Server is a relational database management system (RDBMS). Applications and tools connect to a SQL Server instance or database and communicate using Transact-SQL. This documentation will guide you through the process of loading data from Klarna to Microsoft SQL Server using the open-source python library dlt. For more information about Klarna, visit their website.

dlt Key Features​

  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
  • Run it where Python runs: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Explore further
  • User-friendly, declarative interface: Removes knowledge obstacles for beginners while empowering senior professionals. Read more
  • Scalability via iterators, chunking, and parallelization: Efficient processing of large datasets by breaking them down into manageable chunks. Detailed information
  • Implicit extraction DAGs: Automatically handles dependencies between data sources and their transformations. Find out more

Getting started with your pipeline locally​

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

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​

info

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.

  • If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
  • OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

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​

Further help setting up your source and destinations

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 mssql and supply the credentials as outlined in the destination doc linked below.

  • Read more about setting up the rest_api source in our docs.
  • Read more about setting up the Microsoft SQL Server destination in our docs.

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 Microsoft SQL Server 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 your pipeline using GitHub Actions, a CI/CD runner that you can use for free. Read more.
  • Deploy with Airflow and Google Composer: Deploy your pipeline with Airflow, a platform to programmatically author, schedule, and monitor workflows. Read more.
  • Deploy with Google Cloud Functions: Explore how to deploy your pipeline using Google Cloud Functions, a serverless execution environment. Read more.
  • Other Deployment Methods: Discover various other methods to deploy your pipeline, including detailed guides and walkthroughs. 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 smooth and efficient operations. How to Monitor your pipeline
  • Set up alerts: Implement alerting mechanisms to stay informed about the status and health of your dlt pipelines. Set up alerts
  • Set up tracing: Utilize tracing to gain insights into the performance and execution of your dlt pipelines. 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 NameWrite DispositionDescription
session_readappendSession data including user interactions and payment sessions

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.