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Load Lazada data to DuckDB

Build a Lazada to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Lazada API base URL, auth, endpoints, and incremental loading.

SourceLazadaLazada API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Lazada Open Platform provides RESTful APIs for managing seller business data including products, orders, and logistics. Everything needed to build a working Lazada → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.


Build your Lazada to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from Lazada to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Lazada API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.


Lazada API at a glance

Base URLhttps://api.lazada.sg/rest
Example endpointGET /orders/get
Records found atorders
Authenticationrequests require an HMAC-SHA256 signature generated using an App Secret, with access tokens passed as URL parameters where required for seller authorization — sent in the Authorization header, prefixed Bearer
PaginationOffset-based
API referencehttps://open.lazada.com/apps/doc/api

These values come from the Lazada API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Lazada API?

Requests require a cryptographic signature (HMAC-SHA256) calculated from the request parameters and an App Secret, passed as a 'sign' parameter in the query string. Access tokens, when required, are passed as an 'access_token' parameter.

1. Get your credentials

  1. Navigate to the Lazada Open Platform (https://open.lazada.com/) and sign in to your developer account. 2. Access the 'App Console' or 'App Management' section. 3. Register a new application if one does not already exist. 4. Once registered, your 'App Key' (Client ID) and 'App Secret' (Client Secret) will be generated and visible on the application overview page. 5. In the 'App Overview' or 'Basic Information' section, you can view the App Key directly and click 'View' to reveal the App Secret. Secure these credentials immediately.

2. Add them to .dlt/secrets.toml

[sources.lazada_source] app_key = "your_app_key_here" app_secret = "your_app_secret_here" redirect_uri = "your_registered_callback_url"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.


What Lazada data can I load into DuckDB?

These are the Lazada endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
orders/orders/getGETordersRetrieve a list of orders. Supports pagination using limit and offset.
products/products/getGETproductsRetrieve a list of products. Supports pagination using limit and offset.
brands/category/brands/queryGETbrandsRetrieve all product brands.
categories/category/tree/getGETcategory_treeRetrieve the list of all product categories.
seller_metrics/seller/metrics/getGETGet seller performance data.

How do I load only new Lazada records?

The Lazada API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "orders", "endpoint": { "path": "/orders/get", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.


What does the generated Lazada pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /auth/token/create and /auth/token/refresh from the Lazada API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def lazada_source(access_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.lazada.sg/rest", "auth": {"type": "bearer", "token": access_token}, }, "resources": [ {"name": "orders", "endpoint": {"path": "/orders/get", "data_selector": "orders"}}, {"name": "products", "endpoint": {"path": "/products/get", "data_selector": "products"}} ], } yield from rest_api_resources(config) def load_lazada_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="lazada_pipeline", destination="duckdb", dataset_name="lazada_data", ) load_info = pipeline.run(lazada_source()) print(load_info) if __name__ == "__main__": load_lazada_to_duckdb()

Run it with python lazada_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query Lazada data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

import dlt data = dlt.pipeline("lazada_pipeline").dataset() df = data.orders.df() print(df.head())

SQL:

SELECT * FROM lazada_data.orders LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.


How do I deploy the Lazada to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

  • Deploy & schedule — run the pipeline as a managed job with automatic retries.
  • Monitor — observable job queues, alerting, and load metrics for every run.
  • Transform — promote raw Lazada loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load Lazada data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.


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