Load Majestic data in Python using dltHub

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

In this guide, we'll set up a complete Majestic 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 majestic_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://developer.majestic.com/api/json?app_api_key=API_KEY&cmd=AnalyseIndexItem&items=1&item0=http:/www.majestic.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "comparedomainbacklinkhistory.php&DebugForceQueue=1", "comparedomainbacklinkhistory.php"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • GetIndexItemInfo: Retrieves detailed information about a specific indexed item.
  • DeleteDownloads: Deletes a download job by its ID.
  • DownloadBackLinks: Initiates a download of backlinks for a specified query.
  • GetDownloadsList: Lists all current downloads or a specific download by ID.
  • DownloadRefDomainBackLinks: Downloads backlinks from referring domains for a specific item.
  • GetAnchorText: Fetches anchor text data related to a specified item, with options for filtering and mode selection.
  • GetBackLinkData: Retrieves backlink data for a specific item with options to limit the count.
  • GetBackLinksHistory: Provides historical backlink data for two specified items.
  • GetHostedDomains: Lists hosted domains for a given IP address or domain name.
  • GetKeywordInfo: Returns information about specified keywords.
  • GetLinkProfile: Fetches the link profile for a specific item.
  • GetNewLostBackLinks: Retrieves new and lost backlinks for a specified item within a defined count.
  • GetNewLostBackLinkCalendar: Provides a calendar view of new and lost backlinks for a specified item.
  • GetPrefixQueryEstimate: Estimates the prefix query results for a specified endpoint.

You will then debug the Majestic 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:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install "dlt[workspace]"

    Initialize a dlt pipeline with Majestic support.

    dlt init dlthub:majestic duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Majestic API, as specified in @majestic-docs.yaml 
Start with endpoints comparedomainbacklinkhistory.php&DebugForceQueue=1 and comparedomainbacklinkhistory.php and skip incremental loading for now. 
Place the code in majestic_pipeline.py and name the pipeline majestic_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 majestic_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    To authenticate requests, you need an API key, which must be maintained securely by the client and can be enabled by selecting the Enable API option from the API tab at the Majestic website; additionally, for the OpenApp authentication mechanism, you will require an OpenApp private key and an access token.

    To get the appropriate API keys, please visit the original source at https://developer-support.majestic.com/api/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python majestic_pipeline.py

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

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

  5. 📈 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 majestic_pipeline show

  6. 🐍 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("majestic_pipeline").dataset()
# get "comparedomainbacklinkhistory.php&DebugForceQueue=1" table as Pandas frame
data."comparedomainbacklinkhistory.php&DebugForceQueue=1".df().head()

Extra resources:

Next steps