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To begin the process, you need to access data from your Amazon Seller account. First, ensure you have a registered developer account on Amazon MWS (Marketplace Web Service). Then, obtain your API credentials, which include the Access Key ID, Secret Access Key, and Seller ID. These credentials will allow you to programmatically access your Amazon Seller data.
Use the Amazon MWS API to extract the data you need. This could involve making HTTP requests to endpoints like `GetReport` to download sales reports or inventory data. Ensure you understand the specific API documentation and use libraries like `boto3` (for Python) to facilitate these requests. Handle authentication using the credentials obtained in Step 1.
Once you've retrieved the raw data from Amazon's API, parse it into a structured format. The data is often in XML or flat-file format, so you may need to convert it to JSON or CSV for ease of processing. This might involve using libraries such as `xml.etree.ElementTree` or `pandas` in Python to parse and transform the data into a tabular format suitable for BigQuery.
Before uploading, ensure that your data conforms to a schema compatible with BigQuery. Define the appropriate data types for each field (e.g., STRING, INTEGER, FLOAT, etc.) and handle any necessary data cleaning or transformation tasks. This might include handling missing values, normalizing data formats, or splitting and joining data fields.
If you haven’t already, set up the Google Cloud SDK on your local machine or server. Authenticate your Google Cloud account using `gcloud auth login`. Ensure you have the necessary permissions to create datasets and tables in BigQuery within your Google Cloud project.
Use the `bq` command-line tool to load your data into BigQuery. First, create a dataset using the command `bq mk dataset_name`. Then, load your data using a command such as `bq load --source_format=CSV dataset_name.table_name path_to_local_file.csv schema_file.json` where you specify the source format, dataset, table name, path to your CSV file, and the schema definition file.
To ensure the data is regularly updated, set up a cron job or a scheduled task to automate the retrieval, transformation, and loading process. Write a script that encompasses the above steps and schedule it to run at your desired frequency. This will ensure that your BigQuery data remains current with your Amazon Seller data.
By following these steps, you can effectively move data from Amazon Seller Partner to BigQuery without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Amazon Selling Partner’s API (SP-API) is a much-improved iteration of Amazon Marketplace Web Service (Amazon MWS) APIs. This next generation suite offers increased automation functionality, with many new features including state-of-the-art JSON-based REST API design standards and 0Auth2.0 selling partner authorization using Login with Amazon. With this generation of updates, Amazon Selling Partners continues to deliver reliable programmatic access to essential Amazon features, in the same tradition their customers have come to expect for over 10 years.
Amazon Seller Partner's API provides access to a wide range of data related to Amazon seller accounts. The API allows developers to retrieve data related to orders, products, inventory, and pricing. Here are the categories of data that the API provides access to:
1. Orders: The API provides access to order details such as order ID, order status, shipping address, payment information, and order items.
2. Products: The API provides access to product details such as product ID, product title, product description, product images, and product variations.
3. Inventory: The API provides access to inventory details such as inventory levels, inventory status, and inventory updates.
4. Pricing: The API provides access to pricing details such as product prices, discounts, and promotions.
5. Fulfillment: The API provides access to fulfillment details such as shipment tracking information, shipping labels, and fulfillment status.
6. Reports: The API provides access to various reports such as sales reports, inventory reports, and financial reports.
Overall, the Amazon Seller Partner's API provides a comprehensive set of data that can help sellers manage their Amazon business more effectively.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
