Understanding Dlt: The Modern Python Library for Data Ingestion

Goal: Understand what dlt (data load tool) is, why it exists, and how its core Extract → Normalize → Load architecture solves one of the most tedious problems in data engineering: getting data from a source into a warehouse, reliably and repetably.

1. The Ingestion Problem

In the modern data stack, getting data into a warehouse is often the most unglamorous part of the job. You need to:

• Call an API, handle pagination, retries, and rate limits.
• Parse nested JSON and decide how to flatten it into relational tables.
• Infer and manage a schema that will inevitably evolve over time.
• Load data incrementally without duplicates.
• Do all of this again for every new data source.

Teams historically handled this by writing bespoke Python scripts, or paying for managed connectors (Fivetran, Airbyte). Both options have trade-offs: custom scripts are fragile and hard to maintain; managed connectors are expensive and limited in flexibility.

dlt is an open-source Python library that gives you the best of both worlds — the flexibility of custom Python code with the reliability and automation of a managed connector.

2. What dlt Actually Does

dlt is a Python-native data ingestion library. You write a Python function that yields data, and dlt takes care of everything else:

• Schema inference and evolution
• Data type normalization and coercion
• Flattening of nested structures (e.g., lists of objects inside JSON)
• Incremental loading (state management for cursor-based or timestamp-based extraction)
• Writing data to a destination (DuckDB, BigQuery, Snowflake, Redshift, and more)

Think of it as the glue between any data source and any data warehouse, written entirely in Python.

3. The Extract → Normalize → Load Architecture

At its core, a dlt pipeline has three distinct phases:

Phase 1: Extract

The extraction phase is your code. You write a Python function (called a Resource) that fetches data from a source — an API, a database, a file, or any other origin. This function yields data as dictionaries or lists.

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import dlt

@dlt.resource(name="books")
def get_books():
    # Fetch from Open Library API
    response = requests.get("https://openlibrary.org/search.json?q=python")
    for book in response.json()["docs"]:
        yield book

The @dlt.resource decorator tells dlt that this function is a data source. Multiple resources can be grouped together into a Source using @dlt.source.

Phase 2: Normalize

Once data is extracted, dlt normalizes it automatically. This is where the heavy lifting happens:

This normalization means you don’t have to write complex deserialization logic or manage schema migrations manually. dlt tracks the schema state and evolves it safely as your source data changes.

Phase 3: Load

Finally, dlt loads the normalized data into your chosen destination. You configure the destination once when you define the pipeline:

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pipeline = dlt.pipeline(
    pipeline_name="open_library_pipeline",
    destination="duckdb",
    dataset_name="library_data"
)

# Run it
load_info = pipeline.run(get_books())
print(load_info)

Supported destinations include DuckDB (great for local development), BigQuery, Snowflake, Redshift, Postgres, and many more.

4. Core Concepts at a Glance

5. Incremental Loading: The Key to Production-Ready Pipelines

One of dlt’s most powerful features is built-in incremental loading. Instead of re-fetching all data on every run, you can configure a cursor field (like a timestamp or ID) and dlt will automatically:

1. Track the highest value seen from the previous run (stored in its State).
2. Pass the cursor value to your resource on the next run.
3. Only fetch and load new or updated records.

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@dlt.resource(primary_key="id")
def get_books(
    updated_at=dlt.sources.incremental("last_modified", initial_value="2023-01-01")
):
    for book in fetch_books(since=updated_at.last_value):
        yield book

This makes pipelines idempotent and efficient — critical for production workloads where re-loading terabytes of data every day is not an option.

6. Why dlt + AI Is a Powerful Combination

Writing a dlt pipeline manually still requires knowing the API structure, designing the resource functions, and handling edge cases. This is where AI-assisted development becomes a force multiplier.

Since dlt follows consistent, well-documented patterns, an AI agent can:

• Read an API’s OpenAPI specification and generate the correct @dlt.resource functions.
• Choose the right incremental strategy based on the API’s available cursor fields.
• Configure the pipeline and destination with the correct parameters.
• Debug extraction errors by reading the raw API response.

The dlt MCP (Model Context Protocol) server takes this a step further by giving the AI agent direct access to dlt documentation, code examples, and even your pipeline’s metadata — enabling a fully conversational pipeline development experience.

In the next post, we’ll put all of this into practice: using an agentic IDE (Cursor), the dlt MCP server, and a single prompt to build a complete pipeline from the Open Library API to a local DuckDB database — in minutes.