Data Kiosk Deep Dive: Mastering NewAmazon Reporting API

Custom Amazon Reports Using SP-API’s Powerful Data Kiosk GraphQL Queries

Data Kiosk is a robust tool designed to assist Amazon selling partners in dynamically generating and managing custom reports through GraphQL queries.

This comprehensive guide provides an in-depth look at how to set up, use, and maximize the benefits of Data Kiosks in their current form.

Now, let’s dive into Data Kiosk!

Key Features and Benefits

GraphQL-Based Reporting

Data Kiosk leverages GraphQL, a powerful query language for APIs that allows for dynamic and efficient querying. Unlike traditional REST APIs, GraphQL enables clients to request the data they need in a single request, reducing the amount of data transferred over the network and improving performance. This makes it a superior choice for complex and detailed reporting needs.

Interactive Documentation

One of the standout features of Data Kiosk is its interactive documentation. Detailed schemas, data field definitions, and a user-friendly Schema Explorer are readily available. This tool simplifies the process of understanding data structures and building queries, making it accessible even for users who are new to GraphQL. The Schema Explorer enhances user experience and efficiency by providing real-time feedback and detailed descriptions.

Custom Report Generation

Data Kiosk simplifies the creation of custom reports tailored to specific business needs. Instead of calling multiple reports and manually combining data, users can generate comprehensive reports with a single query. This not only saves time but also reduces the complexity of data processing. Users can leverage advanced filtering and sorting options to create highly specific and useful reports.

Security and Role-Based Access

Ensuring data security is a top priority for Data Kiosk. It employs role-based access control, ensuring only authorized users can access sensitive data. Each field available through Data Kiosk requires specific roles, and queries are visible only to the requester, enhancing security. This level of control ensures that sensitive business data is protected and accessed appropriately.

JSONL Format

Data Kiosk provides results in JSON Lines (JSONL) format, simplifying the processing and parsing of large datasets. Each line in a JSONL file represents a separate JSON object, making it easier to handle streaming data and process records individually. This format is particularly useful for handling large volumes of data efficiently.

Pagination and Throttling

Data Kiosk supports pagination with the next tokens, allowing users to navigate large datasets efficiently. Additionally, it includes mechanisms to handle rate limits and prevent query backlogs, ensuring smooth and uninterrupted data access. These features are critical for maintaining performance and reliability, especially when dealing with extensive data sets.

Setting Up Data Kiosk

Initial Setup and Configuration

Setting up Data Kiosk involves subscribing to notifications using the Notifications API and Amazon Simple Queue Service (SQS). This allows users to receive updates when a query has finished processing. To subscribe, users need to configure a destination for the notifications and establish subscriptions as per the Notifications API v1 Use Case Guide. Assigning appropriate roles to users for accessing different datasets is crucial for maintaining data security.

Using the Schema Explorer

The Schema Explorer is a key tool for navigating Data Kiosk. It provides a user-friendly interface for visualizing data schemas and building queries. Users can utilize the dropdown selector to choose the schema they want to explore, use the search bar to find specific fields, and click on fields and data types to view detailed information. The Query Builder assists in constructing and testing queries, and schemas can be downloaded for offline use.

Creating and Managing Queries

Creating Queries

Creating queries in Data Kiosk involves understanding the structure of GraphQL queries and ensuring they are valid. A GraphQL query consists of fields, arguments, and return types. Users must ensure the query adheres to the schema and includes necessary fields. Handling quotation marks properly is essential to prevent syntax errors. For example, the following query fetches sales data by date:


POST https://sellingpartnerapi-na.amazon.com/dataKiosk/2023-11-15/queries
{
 “query”: “{analytics_salesAndTraffic_2023_11_15{salesAndTrafficByDate(startDate:\”2023–11–15\”)}}”
}

Processing and Monitoring Queries

After submitting a query, it is essential to monitor its progress and handle any errors that may occur. Users can submit queries using the `createQuery` operation, which will return a `queryId` if the request is successful. The progress of the query can be monitored using the `getQuery` operation, which will indicate when the query status is marked as `DONE`, `CANCELLED`, or `FATAL`. Handling errors involves differentiating between synchronous errors (occurring during query creation due to syntax issues) and asynchronous errors (occurring during processing and returned as error documents).

Retrieving and Using Data

Downloading Query Results

Once a query has been processed, the results can be retrieved using the `getDocument` operation. Users must provide the `dataDocumentId` or `errorDocumentId` to retrieve the document. The response includes a pre-signed URL for downloading the document, which expires after five minutes. Handling compressed data documents appropriately is crucial, as the `Content-Encoding` header will specify the compression method used.

Data Formats and Best Practices

Data Kiosk uses the JSONL format for its results, which offers several benefits for processing and streaming data. Each line in a JSONL file represents a separate JSON object, making it ideal for handling large datasets. JSONL simplifies streaming data processing, as each record can be processed individually. Optimizing data retrieval involves requesting only the necessary fields to minimize data transfer and improve performance and using filtering capabilities to narrow down query results based on specific criteria.

Advanced Features and Use Cases

Custom Reports and Analytics

Data Kiosk enables users to generate custom reports and perform detailed analytics. Users can tailor reports to specific business needs by selecting relevant fields and applying filters. Advanced GraphQL features can be utilized for complex data manipulations, such as sorting data based on specific criteria to gain valuable insights.

Real-World Use Cases

Data Kiosk can be used in various scenarios to optimize business operations. For sales and traffic analysis, users can generate reports to analyze sales trends and traffic metrics. An example query to fetch sales data by date or ASIN can provide detailed insights. Data Kiosk helps monitor inventory levels and manage stock efficiently for inventory management. Custom queries for performance metrics allow businesses to evaluate the effectiveness of marketing campaigns and other initiatives.

Troubleshooting and Support

Common Issues and Solutions

Addressing common issues in Data Kiosk involves understanding error messages and implementing solutions. Syntax errors can be identified and fixed by reviewing the query structure and parameters. Managing query concurrency limits is essential to avoid throttling errors, which can be achieved by implementing a request queue for handling sequential processing of requests.

Accessing Support

For additional help, users can access various developer resources and contact support teams. Comprehensive guides and tutorials are available in the documentation, and community forums provide a platform for discussing issues and sharing solutions. GitHub repositories offer sample applications and code examples. Users can also contact Amazon support teams for specific issues, providing detailed information about the problem to receive accurate assistance.

Security and Compliance

Data Security Measures
Data Kiosk prioritizes data security through various measures. Role-based access control ensures only authorized users can access sensitive data, with specific roles required for different datasets. Maintaining encryption at rest for all query result documents is crucial, and unencrypted query result document content should never be stored on disk.

Compliance with Policies
Adhering to Amazon’s API usage policies and data protection regulations is crucial for Data Kiosk users. Understanding and following Amazon’s API usage policies helps avoid potential issues, and regular reviews of these policies ensure compliance. Implementing the best data security and privacy practices is essential for safeguarding sensitive information.

Limitations of Data Kiosk

Despite its robust features and potential, Data Kiosk has several limitations that users should know. Understanding these limitations is crucial for setting the right expectations and planning accordingly.

Lack of Parity with Current Reporting API
One of the most significant limitations of Data Kiosk is that it does not yet offer full parity with the existing Reporting API. While Data Kiosk introduces a powerful new reporting framework, it does not yet support all the functionalities and data points available in the current Reporting API. Users relying heavily on the Reporting API's comprehensive capabilities may find the Data Kiosk lacking in some areas.

Primary Use Case: Exposure to a New Reporting Framework
Data Kiosk is primarily intended to expose users to a new reporting framework. It aims to glimpse the future of dynamic and flexible reporting via GraphQL, but it has not yet been designed to replace the existing reporting API. This transitional phase is essential for gathering user feedback and iterating on the features before achieving full parity and production readiness.

Limited Data Sets and Features

Currently, Data Kiosk supports a limited set of data types and features compared to the comprehensive offerings of the Reporting API. Users might encounter constraints in the types of data they can query and the complexity of the reports they generate. This limitation is expected to diminish over time as Amazon expands the capabilities of Data Kiosks.

Evolving Schema and API Changes

As Data Kiosk evolves, users should anticipate schema and API structure changes. These changes are part of the iterative improvement process but can lead to disruptions if users are not prepared to adapt their queries and integrations accordingly. Staying updated with the latest documentation and release notes is essential for minimizing potential issues.

No Support for Legacy Data Structures

Data Kiosk does not support some legacy data structures and formats that are available in the current Reporting API. This limitation can be a barrier for users who need access to historical data or specific report formats that have not yet been integrated into the Data Kiosk.

Not Production Ready

Given its current state, Data Kiosk should not be considered production-ready for critical business operations. While it can be used for testing, learning, and exploring new reporting capabilities, relying on it for production-level reporting might lead to gaps and inefficiencies. Users are advised to continue using the existing Reporting API for production needs until the Data Kiosk matures and achieves full feature parity.

While Data Kiosk is an exciting new option, it’s important to note that you can also access Amazon data through Openbridge’s automated connectors and integrations, as detailed in these articles:

Summary

While Data Kiosk presents an exciting opportunity to explore a new and dynamic reporting framework, it is important to recognize its current limitations.

The lack of full parity with the existing Reporting API and its status as not production-ready are critical user considerations. By understanding these constraints and planning accordingly, users can effectively leverage Data Kiosk for testing and learning while continuing to rely on the existing Reporting API for critical business operations.

As the Data Kiosk evolves, it is expected to overcome these limitations and offer a comprehensive, production-ready reporting solution.

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Data Kiosk Deep Dive: Mastering NewAmazon Reporting API was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



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