Principle:DataTalksClub Data engineering zoomcamp Dynamic URL Generation
| Page Metadata | |
|---|---|
| Knowledge Sources | dlt docs: dlt Documentation, NYC TLC: NYC TLC Trip Record Data |
| Domains | Data_Engineering, Data_Ingestion |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
Dynamic URL generation is the practice of programmatically constructing a set of data source addresses from parameterized ranges, enabling flexible and repeatable data discovery without hardcoded file references.
Description
Many public and enterprise datasets are published as collections of files that follow a predictable naming convention. For example, a dataset might be partitioned by year and month, with each partition stored as a separate file at a URL that encodes the partition key in its path. Rather than maintaining a static list of URLs that must be manually updated whenever new data is available, dynamic URL generation constructs the full set of source addresses algorithmically from a base URL template and a set of user-provided parameters.
This principle applies broadly to any scenario where data sources follow a consistent naming pattern:
- Temporal partitioning -- Files named by year-month, year-week, or date (e.g.,
data_2021-03.parquet) - Categorical partitioning -- Files named by category or type (e.g.,
green_tripdata,yellow_tripdata) - Combined partitioning -- Files that encode both category and time in the filename
The key advantages of dynamic generation over static lists include:
- Flexibility -- Users can request any date range without modifying code
- Completeness -- The algorithm systematically enumerates all combinations, eliminating the risk of accidentally omitting a partition
- Maintainability -- When the upstream URL pattern changes, only the template needs updating rather than every individual URL
- Reusability -- The same generation logic works across different categories and time ranges
Usage
Use dynamic URL generation when:
- Data sources are published at predictable, pattern-based URLs
- The set of files to process varies between pipeline runs (e.g., different date ranges)
- Users need to specify which subset of data to ingest at runtime
- The pipeline must be able to handle both small (single month) and large (multi-year) ranges without code changes
Theoretical Basis
The logic for generating URLs from parameterized ranges follows a Cartesian product enumeration:
FUNCTION generate_source_addresses(category, year_range, month_range):
template = base_url + "/{category}_data_{year}-{month}.format"
addresses = empty list
FOR EACH year IN year_range:
FOR EACH month IN month_range:
formatted_month = zero_pad(month, width=2)
address = interpolate(template, category, year, formatted_month)
addresses.append(address)
RETURN addresses
The nested iteration over years and months produces the Cartesian product of the two ranges. Each combination is interpolated into the URL template to produce a complete, valid address. The zero-padding of the month component ensures consistency with upstream naming conventions that use two-digit months.
An important consideration is validation: not every generated URL is guaranteed to point to an existing file. Some months may not have data, or the upstream publisher may not have released files for the most recent period. Robust pipelines should handle HTTP 404 or similar errors gracefully when iterating over the generated URLs.
The separation of URL generation from URL consumption (downloading or streaming) follows the producer-consumer pattern: the generator produces a list of work items, and downstream processing stages consume them independently.