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Connect Amazon Seller to Snowflake

Connect Amazon Seller to Snowflake and export selected accounts, metrics, and dimensions to your Google BigQuery project.

Configure the historical window, destination table, and recurring schedule without maintaining a Amazon Seller API pipeline.

Used by the world's leading companies

Export Amazon Seller data to Snowflake

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Amazon Seller data in PostgreSQL

Bring all your Amazon Seller data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data freshness

Keep your Snowflake tables up to date

Automatically sync new data from Amazon Seller so every dashboard, model, and report works with fresh information.

Analysis

Unify Amazon Seller with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

Amazon Seller metrics and dimensions available in Snowflake

Access 50 metrics and 54 dimensions from Amazon Seller connectors including Ordered Product Sales Amount, Units Ordered, Sessions, Page Views, Buy Box Percentage, Refund Rate, Date, Product SKU, Product ASIN, Product Name, Sales Channel, Order Status and many more...

50

Metrics availables

54

Dimensions availables

Connect Amazon Seller to Snowflake in minutes

Move your data from Amazon Seller to Snowflake with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your Amazon Seller account

Authenticate your Amazon Seller account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Client A · Connected sources
5 sources ready
Amazon Seller Amazon Seller 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step two

Choose the Snowflake destination

Add the account host, user, and database schema. Use a password for compatible users or a PEM private key for service accounts and stricter policies.

Destination / Snowflake Required fields
Authentication Choose one
Password
Key pair
Host Required
xy12345.eu-west-1
Username Required
catchr_service
Database Required
MARKETING.RAW
Private key passphrase Optional
••••••••••••
Private key Required for key pair
-----BEGIN ENCRYPTED PRIVATE KEY----- MIIE6TAbBgkqhkiG9w0BBQMw... -----END ENCRYPTED PRIVATE KEY-----
Step three

Keep your Snowflake tables updated

Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.

Export job / PostgreSQL Example
Schema Destination
marketing_raw
Table Per job
linkedin_ads_daily
Recurring schedule Choose a cadence
Every 6 hours
Daily · 07:00
Weekly · Mon
First sync scheduledSelected fields · configured history
07:00 UTC

Review 30-day Amazon Seller sales performance in SQL.

This example summarizes revenue, orders, units sold, and average order value by sales channel.

Amazon Seller.sql
BigQuery SQL
30-day window
SELECT
  sales_channel,
  SUM(revenue) AS revenue,
  SUM(orders) AS orders,
  SUM(units_sold) AS units_sold,
  SAFE_DIVIDE(
    SUM(revenue),
    SUM(orders)
  ) AS average_order_value
FROM `your_project.marketing_raw.amazon_seller_orders`
WHERE order_date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY sales_channel
ORDER BY revenue DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Choose what to load into Snowflake.

Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.

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Before you schedule the job.

Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.

What do I need to export Amazon Seller data to Snowflake?

Register Amazon Seller, save the Snowflake connection details, and pair both systems in a datastream. The associated job previews and writes the selected columns to "customers".

What does an Amazon Seller table in Snowflake contain?

Select concrete Amazon Seller fields such as Average Sales Per Order Item Amount, Average Sales Per Order Item Currency, Average Sales Per Order Item B2B Amount, and Normalized Week of the year. You can rename the chosen columns, preview the result, and write them to Snowflake tables such as stores, customers, and products.

How do scheduled Snowflake jobs keep Amazon Seller data current?

The "customers" job can begin with a historical Amazon Seller fetch and continue on a recurring schedule. Available history and frequency depend on the source API and your plan.

What controls row replacement in an Amazon Seller Snowflake table?

Select a valid date field when configuring the Amazon Seller job for "customers". Catchr deletes and rewrites the scheduled window, limiting duplicate rows across overlapping runs.

How can I model Amazon Seller performance with Snowflake?

With Amazon Seller data, a Snowflake query can summarize revenue, orders, units sold, and average order value by sales channel. Turn Average Sales Per Order Item Amount and Average Sales Per Order Item Currency into a stable Snowflake view that analysts and reporting tools can query repeatedly.

Does my Snowflake database need to exist before I connect it?

Yes. Connect an existing Snowflake database first. The Catchr job creates and populates the destination table you configure inside that database.

Can I load several marketing sources into the same Snowflake database?

Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.

Still have a question ? 

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