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Version: 1.0.0 (latest)

How dlt works

dlt automatically turns JSON returned by any source (e.g. an API) into a live dataset stored in the destination of your choice (e.g. Google BigQuery). It does this by first extracting the JSON data, then normalizing it to a schema, and finally loading it to the location where you will store it.

architecture-diagram

Extract

The Python script requests data from an API or a similar source. Once this data is received, the script parses the JSON and provides it to dlt as input, which then normalizes that data.

Normalize

The configurable normalization engine in dlt recursively unpacks this nested structure into relational tables (i.e. inferring data types, linking tables to create nested relationships, etc.), making it ready to be loaded. This creates a schema, which will automatically evolve to any future source data changes (e.g. new fields or tables).

Load

The data is then loaded into your chosen destination. dlt uses configurable, idempotent, atomic loads that ensure data safely ends up there. For example, you don't need to worry about the size of the data you are loading and if the process is interrupted, it is safe to retry without creating errors.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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