Load Bitcoin Information data in Python using dltHub

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

In this guide, we'll set up a complete Bitcoin Information 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 bitcoin_information_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8332/rest/block/notxdetails",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.json", "00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.hex"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='bitcoin_information_pipeline',
        destination='duckdb',
        dataset_name='bitcoin_information_data', 
    )
    # Load the data
    load_info = pipeline.run(bitcoin_information_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 bitcoin_information’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

Mempool Endpoints: These endpoints provide information about the current state of the mempool, including its contents and general info.
- /rest/mempool/contents.json: Returns the contents of the mempool.
- /rest/mempool/info.json: Provides general information about the mempool.
Block Endpoints: These endpoints give details regarding specific blocks in the blockchain.
- /rest/block/{blockhash}.json: Retrieves data for a specific block by its hash.
- /rest/block/notxdetails/{blockhash}.json: Returns block data without transaction details.
- /rest/block/{blockhash}.hex: Provides the block data in hexadecimal format.
Transaction Endpoints: These endpoints focus on specific transactions within the blockchain.
- /rest/tx/{txid}.json: Fetches details for a specific transaction by its ID.
- /rest/tx/{txid}.hex: Returns the transaction data in hexadecimal format.
Chain Info Endpoint: This endpoint offers high-level information about the blockchain's status.
- /rest/chaininfo.json: Provides general information about the current state of the blockchain.
Headers Endpoints: These endpoints deal with the block headers in the blockchain.
- /rest/headers/{count}/{startblockhash}.json: Retrieves a specified number of block headers starting from a particular block hash.
- /rest/headers/{count}/{startblockhash}.hex: Provides the block headers in hexadecimal format.
UTXO Endpoints: These endpoints check unspent transaction outputs (UTXOs) against the mempool.
- /rest/getutxos/checkmempool/{utxo}.json: Checks UTXOs against the mempool and returns the result in JSON format.
- /rest/getutxos/checkmempool/{utxo}.hex: Returns the same check in hexadecimal format.

You will then debug the Bitcoin Information 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 Bitcoin Information support.
```
dlt init dlthub:bitcoin_information 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 Bitcoin Information API, as specified in @bitcoin_information-docs.yaml 
Start with endpoints 00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.json and 00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.hex and skip incremental loading for now. 
Place the code in bitcoin_information_pipeline.py and name the pipeline bitcoin_information_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 bitcoin_information_pipeline.py and await further instructions.

🔒 Set up credentials

Auth information not found.

To get the appropriate API keys, please visit the original source at https://btcinformation.org/en/developer-reference. 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 bitcoin_information_pipeline.py

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


Pipeline bitcoin_information load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bitcoin_information_data
The duckdb destination used duckdb:/bitcoin_information.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 bitcoin_information_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("bitcoin_information_pipeline").dataset()
# get "00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.json" table as Pandas frame
data."00000000839a8e6886ab5951d76f411475428afc90947ee320161bbf18eb6048.json".df().head()
```

Extra resources:

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