Loading Klarna Data to Redshift 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. It 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. Amazon Redshift
is a fully managed, petabyte-scale data warehouse service in the cloud. It allows you to start with just a few hundred gigabytes of data and scale to a petabyte or more. Using the open-source Python library dlt
, you can efficiently load data from Klarna
to Redshift
. For further 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, 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. Read more: Adjust a schema docs. - Scalability via Iterators, Chunking, and Parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Read more about performance. - Implicit Extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and their transformations automatically. Read more about extraction DAGs. - Redshift Setup Guide: Comprehensive steps to install
dlt
with Redshift dependencies and set up a Redshift cluster for data loading. Read more about Redshift setup.
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 redshift 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 Redshift
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
dlt
pipeline using GitHub Actions for CI/CD automation. Read more - Deploy with Airflow and Google Composer: Follow this guide to deploy your
dlt
pipeline using Airflow and Google Composer. Read more - Deploy with Google Cloud Functions: Explore how to deploy your
dlt
pipeline using Google Cloud Functions for serverless execution. Read more - Other Deployment Methods: Check out additional methods to deploy your
dlt
pipeline, including various cloud and on-premises options. Read more
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline in production to ensure smooth operations. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified of any issues or important events in your
dlt
pipeline. Set up alerts - Set up tracing: Implement tracing to track the execution and performance of your
dlt
pipeline in detail. 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 Chess.com to Azure Synapse in python with dlt
- Load data from Microsoft SQL Server to YugabyteDB in python with dlt
- Load data from Soundcloud to CockroachDB in python with dlt
- Load data from Chargebee to EDB BigAnimal in python with dlt
- Load data from Imgur to Redshift in python with dlt
- Load data from Slack to Azure Synapse in python with dlt
- Load data from Fivetran to PostgreSQL in python with dlt
- Load data from SAP HANA to Timescale in python with dlt
- Load data from The Local Filesystem to AlloyDB in python with dlt
- Load data from Azure Cloud Storage to Neon Serverless Postgres in python with dlt