Loading Klarna
Data to Azure Cloud Storage
Using dlt
in Python
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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. This documentation explains how to load data from Klarna
to Azure Cloud Storage
using the open-source Python library dlt
. The Azure Cloud Storage
destination stores data on Microsoft Azure, allowing you to easily create data lakes. Data can be uploaded in JSONL, Parquet, or CSV formats. For more information on Klarna
, visit Klarna's website.
dlt
Key Features
- Scalability via iterators, chunking, and parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn more - Implicit extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Learn more - Environment and TOML providers:
dlt
provides flexibility in managing credentials and configurations through environment variables and TOML files. Learn more - Filesystem & buckets:
dlt
supports storing data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. Learn more - Setup Guide: A step-by-step guide to initializing and setting up a new
dlt
project, making it easy to get started. 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 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 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 Cloud Storage
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: Use GitHub Actions to automate your pipeline deployment. Follow the guide here.
- Deploy with Airflow: Leverage Airflow and Google Composer for deploying your pipeline. Detailed instructions can be found here.
- Deploy with Google Cloud Functions: Utilize Google Cloud Functions for serverless deployment of your pipeline. Check out the guide here.
- Explore other deployment options: Discover various other methods to deploy your pipeline here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipelines to ensure they are running smoothly and to catch any potential issues early. How to Monitor your pipeline - Set up alerts: Configure alerts to notify you of any critical issues or changes in your
dlt
pipelines, so you can take immediate action. Set up alerts - And set up tracing: Implement tracing to get detailed insights into the execution of your
dlt
pipelines, including timing information and configuration details. 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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