How I Contributed to My First Open Source Data Pipeline

Hello, I'm Aman Gupta. Over the past eight years, I have navigated the structured world of civil engineering, but recently, I have found myself captivated by data engineering. Initially, I knew how to stack bricks and build structural pipelines. But this newfound interest has helped me build data pipelines, and most of all, it was sparked by a workshop hosted by dlt.

info

dlt (data loading tool) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets.

The dlt workshop took place in November 2022, co-hosted by Adrian Brudaru, my former mentor and co-founder of dlt.

An opportunity arose when another client needed data migration from FreshDesk to BigQuery. I crafted a basic pipeline version, initially designed to support my use case. Upon presenting my basic pipeline to the dlt team, Alena Astrakhatseva, a team member, generously offered to review it and refine it into a community-verified source.

My first iteration was straightforward—loading data in replace mode. While adequate for initial purposes, a verified source demanded features like pagination and incremental loading. To achieve this, I developed an API client tailored for the Freshdesk API, integrating rate limit handling and pagination:

class FreshdeskClient:
    """
    Client for making authenticated requests to the Freshdesk API. It incorporates API requests with
    rate limit and pagination.
    """
    
    def __init__(self, api_key: str, domain: str):
        # Contains stuff like domain, credentials and base URL.
        pass

    def _request_with_rate_limit(self, url: str, **kwargs: Any) -> requests.Response:
        # Handles rate limits in HTTP requests and ensures that the client doesn't exceed the limit set by the server.
        pass

    def paginated_response(
        self,
        endpoint: str,
        per_page: int,
        updated_at: Optional[str] = None,
    ) -> Iterable[TDataItem]:
        # Fetches a paginated response from a specified endpoint.
        pass

To further make the pipeline effective, I developed dlt resources that could handle incremental data loading. This involved creating resources that used dlt's incremental functionality to fetch only new or updated data:

def incremental_resource(
    endpoint: str,
    updated_at: Optional[Any] = dlt.sources.incremental(
        "updated_at", initial_value="2022-01-01T00:00:00Z"
    ),
) -> Generator[Dict[Any, Any], Any, None]:
    """
    Fetches and yields paginated data from a specified API endpoint.
    Each page of data is fetched based on the `updated_at` timestamp
    to ensure incremental loading.
    """

    # Retrieve the last updated timestamp to fetch only new or updated records.
    updated_at = updated_at.last_value

    # Use the FreshdeskClient instance to fetch paginated responses
    yield from freshdesk.paginated_response(
        endpoint=endpoint,
        per_page=per_page,
        updated_at=updated_at,
    )

With the steps defined above, I was able to load the data from Freshdesk to BigQuery and use the pipeline in production. Here’s a summary of the steps I followed:

1. Created a Freshdesk API token with sufficient privileges.
2. Created an API client to make requests to the Freshdesk API with rate limit and pagination.
3. Made incremental requests to this client based on the “updated_at” field in the response.
4. Ran the pipeline using the Python script.

While my journey from civil engineering to data engineering was initially intimidating, it has proved to be a profound learning experience. Writing a pipeline with dlt mirrors the simplicity of a GET request: you request data, yield it, and it flows from the source to its destination. Now, I help other clients integrate dlt to streamline their data workflows, which has been an invaluable part of my professional growth.

In conclusion, diving into data engineering has expanded my technical skill set and provided a new lens through which I view challenges and solutions. As for me, the lens view mainly was concrete and steel a couple of years back, which has now begun to notice the pipelines of the data world.

Data engineering has proved both challenging, satisfying, and a good career option for me till now. For those interested in the detailed workings of these pipelines, I encourage exploring dlt's GitHub repository or diving into the documentation.