Managed Data-as-a-Service

Fully-managed Data-as-a-Service platform that allows your data team to move faster. Get high-quality operational data and insights in ready-to-use formats.

Available Datasets

Our data engineering team constructs and maintains retail-focused data models using data from numerous Amazon APIs, the Amazon website, and Walmart.
This allows us to provide you with ready-to-use datasets.
When you access your DataHawk database, you'll notice that each theme is represented by a distinct schema.

Product - All your tracked products' data

All the tables included in the product schema will allow you to monitor and benchmark product listings and performance changes.

SEO - All your tracked keywords' data

Access all historical keywords' rank data and optimize products' organic and sponsored search performance. Also, access the historical keyword search volume of your tracked keywords.

Finance - Sales and profit & loss data

All your orders are in one place from all your marketplaces. One beautiful and meticulously unified model for Amazon and merchant-fulfilled orders.
In addition to orders and sales, access Profit & Loss and detailed financial events tables.


Financial Data

For this data to be available, you need to connect your Seller Central account to DataHawk. This will unveil 2 years of historical data for the accounts connected.

Advertising - Account, campaign, and product-level data

All your costs, sales, clicks, impressions, orders, units sold on a product, campaign, and account level data. Monitor and analyze your ads performance to optimize your ads cost.


Advertising Data

For this data to be available, you need to connect your Advertiser account to DataHawk. This will unveil 3 months of historical data for the accounts connected.

Raw - Consume data exactly as it was extracted from Amazon

Raw schemas contain data straight from Amazon's own services (e.g., Seller Central Reports), with minimal transformations and/or aggregations. This data is useful for performing more complex analyses or consuming data types whose schemas and intelligence are still under development by DataHawk. For now, Raw data contains:

  • Advertising Data - Shows all campaigns run in much more granular detail. This schema is currently available only upon request.
  • Inventory Data - Shows current and historical stock per SKU, as well as re-stocking recommendations generated by Amazon.

Reports - Smart way to join data sources to answer use cases

These tables join several themes (i.e., estimated sales and sales ranks) to answer a distinct use case.

Referential - Everything related to your DataHawk account and more!

This schema contains:

  1. DataHawk workspace-related information: tracked items, tags, Seller Central & Advertising accounts connected, projects.
  2. Repositories about marketplaces, browse node tree, and currency rate. These are useful to add extra information to other data sources, such as browse node details for sales rank data or currency rates to sales data.

Usage - Monitor your Connections credit usage

You'll have access to your remaining monthly query credits and the list of all queries made on your database.

Frequently asked questions

Why are you building the DataHawk Connections platform?

As Marketplaces and Marketplace Sellers grow, so will the complexity surrounding multiple APIs provided by Marketplace operators. Our platform helps your Data team move faster by giving a normalized and documented data foundation. We handle the API rate limits, evolving schemas, tests, transformations, data back-filling, and all other tedious tasks.

Who is the DataHawk Connections platform for?

Data teams from Amazon Sellers, Aggregators, or Agencies.

Can I use the datasets for ERP or CRM integrations?

Definitely! Our customers use DataHawk Connections datasets for Oracle NetSuite ERP, Salesforce Service Cloud CRM, and Marketing Cloud integrations.

How do you deliver the data?

We currently use Snowflake for data sharing. Google BigQuery, Amazon Redshift, and Databricks will soon be supported.

My data science team spends too much time on data preparation. Are the datasets ready for ML model building?

Yes. Connect your Jupyter or workbook to your data warehouse, and start building!

How to visualize and build reports on top of these datasets?

You can use any of the modern BI/Reporting tools. We’ve seen our customers using Tableau, Apache Superset, and Metabase. Many more are available, such as PowerBI, Qlik, Google Data Studio, Holistics, etc.