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Configuring Amazon Selling Partner as a Source

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1. Add account access

• Region: Select the appropriate region for your marketplace:
  • North America (US, Canada, Mexico, Brazil)
  • Europe (UK, France, Germany, Italy, Spain, Netherlands, etc.)
  • Far East (Japan, Australia, Singapore, India)
• Marketplace IDs: The Amazon marketplace IDs you want to sync data from (e.g., ATVPDKIKX0DER for Amazon US, A2Q3Y263D00KWC for Amazon Brazil).

• Start Date: The earliest date from which records will be synced. If not provided, the connector defaults to the last 30 days of data.

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2. Select streams

Tip: The stream can be found more easily by typing its name.

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3. Configure data streams

• Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
• Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
• Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
• Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
  • Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
  • Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

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4. Configure data source

• Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
• Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

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5. Check your new source

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Streams and Fields

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Use Cases for Data Analysis

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1. Sales Performance Overview

• Track revenue trends over time
• Monitor refund rates to identify product quality issues
• Compare B2B vs. B2C sales performance

SELECT
   date,
   unitsOrdered,
   orderedProductSalesAmount,
   orderedProductSalesCurrencyCode,
   unitsRefunded,
   refundRate,
   unitsShipped,
   ordersShipped,
   CASE
      WHEN unitsOrdered > 0 THEN ROUND(unitsRefunded * 100.0 / unitsOrdered, 2)
      ELSE 0
   END AS calculated_refund_pct
FROM
   nekt_raw.amazon_sp_sales_and_traffic_report
WHERE
   date >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY
   date DESC

SELECT
   date,
   unitsOrdered,
   orderedProductSalesAmount,
   orderedProductSalesCurrencyCode,
   unitsRefunded,
   refundRate,
   unitsShipped,
   ordersShipped,
   CASE
      WHEN unitsOrdered > 0 THEN ROUND(SAFE_DIVIDE(unitsRefunded * 100.0, unitsOrdered), 2)
      ELSE 0
   END AS calculated_refund_pct
FROM
   `nekt_raw.amazon_sp_sales_and_traffic_report`
WHERE
   date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY
   date DESC

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2. Order Fulfillment Analysis

• Monitor order processing efficiency across FBA and merchant-fulfilled channels
• Identify bottlenecks in fulfillment
• Track cancellation rates by channel

WITH order_stats AS (
   SELECT
      fulfillmentChannel,
      orderStatus,
      COUNT(*) AS order_count,
      SUM(CAST(itemPrice AS DOUBLE)) AS total_revenue
   FROM
      nekt_raw.amazon_sp_orders_report
   WHERE
      purchaseDate >= CURRENT_DATE - INTERVAL '30' DAY
   GROUP BY
      fulfillmentChannel, orderStatus
),
total AS (
   SELECT SUM(order_count) AS total_orders FROM order_stats
)
SELECT
   os.fulfillmentChannel,
   os.orderStatus,
   os.order_count,
   ROUND(os.order_count * 100.0 / t.total_orders, 2) AS percentage,
   ROUND(os.total_revenue, 2) AS total_revenue
FROM
   order_stats os, total t
ORDER BY
   os.order_count DESC

WITH order_stats AS (
   SELECT
      fulfillmentChannel,
      orderStatus,
      COUNT(*) AS order_count,
      SUM(CAST(itemPrice AS FLOAT64)) AS total_revenue
   FROM
      `nekt_raw.amazon_sp_orders_report`
   WHERE
      purchaseDate >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
   GROUP BY
      fulfillmentChannel, orderStatus
),
total AS (
   SELECT SUM(order_count) AS total_orders FROM order_stats
)
SELECT
   os.fulfillmentChannel,
   os.orderStatus,
   os.order_count,
   ROUND(SAFE_DIVIDE(os.order_count * 100.0, t.total_orders), 2) AS percentage,
   ROUND(os.total_revenue, 2) AS total_revenue
FROM
   order_stats os, total t
ORDER BY
   os.order_count DESC

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3. Traffic-to-Sales Conversion

• Identify trends in traffic and conversion rates
• Compare browser vs. mobile app performance
• Optimize listing strategies based on Buy Box percentage

SELECT
   date,
   sessions,
   browserSessions,
   mobileAppSessions,
   pageViews,
   unitsOrdered,
   unitSessionPercentage AS conversion_rate,
   buyBoxPercentage,
   CASE
      WHEN sessions > 0 THEN ROUND(pageViews * 1.0 / sessions, 2)
      ELSE 0
   END AS pages_per_session
FROM
   nekt_raw.amazon_sp_sales_and_traffic_report
WHERE
   date >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY
   date DESC

SELECT
   date,
   sessions,
   browserSessions,
   mobileAppSessions,
   pageViews,
   unitsOrdered,
   unitSessionPercentage AS conversion_rate,
   buyBoxPercentage,
   CASE
      WHEN sessions > 0 THEN ROUND(SAFE_DIVIDE(pageViews * 1.0, sessions), 2)
      ELSE 0
   END AS pages_per_session
FROM
   `nekt_raw.amazon_sp_sales_and_traffic_report`
WHERE
   date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY
   date DESC

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Implementation Notes