Load Lessonly data in Python using dltHub
Build a Lessonly-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.
In this guide, we'll set up a complete Lessonly data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:
Example code
Why use dltHub Workspace with LLM Context to generate Python pipelines?
- Accelerate pipeline development with AI-native context
- Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
- Build Python notebooks for end users of your data
- Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
- dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading loading to pythonic Iceberg with or without catalogs
What you’ll do
We’ll show you how to generate a readable and easily maintainable Python script that fetches data from lessonly’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:
- Users: Management and statistics of users in the Lessonly platform.
- Groups: Management of user groups and their assignments.
- Lessons: Access to lesson content and related data.
- Paths: Management of learning paths and their associated assignments.
You will then debug the Lessonly pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.
Setup & steps to follow
💡Before getting started, let's make sure Cursor is set up correctly:
- We suggest using a model like Claude 3.7 Sonnet or better
- Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
- Read our full steps on setting up Cursor
Now you're ready to get started!
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⚙️ Set up
dlt
WorkspaceInstall dlt with duckdb support:
pip install dlt[workspace]
Initialize a dlt pipeline with Lessonly support.
dlt init dlthub:lessonly duckdb
The
init
command will setup the necessary files and folders for the next step. -
🤠 Start LLM-assisted coding
Here’s a prompt to get you started:
PromptPlease generate a REST API Source for Lessonly API, as specified in @lessonly-docs.yaml Start with endpoints "users" and "groups" and skip incremental loading for now. Place the code in lessonly_pipeline.py and name the pipeline lessonly_pipeline. If the file exists, use it as a starting point. Do not add or modify any other files. Use @dlt rest api as a tutorial. After adding the endpoints, allow the user to run the pipeline with python lessonly_pipeline.py and await further instructions. -
🔒 Set up credentials
Authentication is performed via an API key that must be included in the request header as 'Authorization'.
To get the appropriate API keys, please visit the original source at https://www.lessonly.com/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.
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🏃♀️ Run the pipeline in the Python terminal in Cursor
python lessonly_pipeline.py
If your pipeline runs correctly, you’ll see something like the following:
Pipeline lessonly load step completed in 0.26 seconds 1 load package(s) were loaded to destination duckdb and into dataset lessonly_data The duckdb destination used duckdb:/lessonly.duckdb location to store data Load package 1749667187.541553 is LOADED and contains no failed jobs
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📈 Debug your pipeline and data with the Pipeline Dashboard
Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:
- Pipeline overview: State, load metrics
- Data’s schema: tables, columns, types, hints
- You can query the data itself
dlt pipeline lessonly_pipeline show --dashboard
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🐍 Build a Notebook with data explorations and reports
With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.
import dlt data = dlt.pipeline("lessonly_pipeline").dataset() # get "users" table as Pandas frame data.users.df().head()
Running into errors?
Lessonly imposes a rate limit of 500 requests per minute. Exceeding this limit will result in a '403 Forbidden (Rate Limit Exceeded)' response. It is advisable to implement pauses in scripts to avoid hitting this limit. Additionally, ensure that all required parameters are formatted correctly to prevent errors.