Load Backblaze data in Python using dltHub

Build a Backblaze-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Backblaze 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
@dlt.source
def backblaze_event_notifications_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://s3.<your-region>.backblazeb2.com/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            my-bucket-name/object-2, <your-bucket-name>/<your-key>
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='backblaze_event_notifications_pipeline',
        destination='duckdb',
        dataset_name='backblaze_event_notifications_data', 
    )
    # Load the data
    load_info = pipeline.run(backblaze_event_notifications_source())
    print(load_info)

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 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 backblaze_event_notifications’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

Object Operations: PUT/DELETE/GET operations for uploading, deleting, and retrieving objects from S3-compatible buckets
Bucket Operations: DELETE operations for removing buckets
Multipart Upload: POST operations for uploading file parts as part of multipart upload process
Batch Delete: POST operations with delete query parameters for bulk object deletion

You will then debug the Backblaze 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!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install dlt[workspace]
```
Initialize a dlt pipeline with Backblaze support.
```
dlt init dlthub:backblaze_event_notifications 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:

Prompt
Please generate a REST API Source for Backblaze API, as specified in @backblaze_event_notifications-docs.yaml 
Start with endpoint(s) my-bucket-name/object-2 and <your-bucket-name>/<your-key> and skip incremental loading for now. 
Place the code in backblaze_event_notifications_pipeline.py and name the pipeline backblaze_event_notifications_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 backblaze_event_notifications_pipeline.py and await further instructions.

🔒 Set up credentials

Webhook requests include HMAC SHA-256 signature verification. You must provide an hmacSha256SigningSecret when creating an Event Notification rule. The webhook request will include an X-Bz-Event-Notification-Signature HTTP header with format v1=<64-character lowercase hex signature>. To verify, compute HMAC-SHA256 of the request payload using hmacSha256SigningSecret as the key, then compare the resulting hex-encoded digest to the signature value in the header. Respond with 200 OK if signatures match, or 401 Unauthorized if they do not.

To get the appropriate API keys, please visit the original source at www.backblaze.com. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python backblaze_event_notifications_pipeline.py

If your pipeline runs correctly, you’ll see something like the following:


Pipeline backblaze_event_notifications load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset backblaze_event_notifications_data
The duckdb destination used duckdb:/backblaze_event_notifications.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

📈 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 backblaze_event_notifications_pipeline show 
```
🐍 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("backblaze_event_notifications_pipeline").dataset()
# get ["my-bucket-name/object-2"] table as Pandas frame
data.["my-bucket-name/object-2"].df().head()
```

Extra resources:

Learn more with our 1h LLM-assisted coding course!