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Product Data Lakehouse

Data managementAdvanced Level

A product data lakehouse is a data platform that stores raw and structured product data together, combining a data lake's cheap storage with a warehouse's structure.

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What is Product Data Lakehouse?

A product data lakehouse is a data platform that stores raw and structured product data in one place. It combines the cheap, flexible storage of a data lake with the tables, schemas and fast queries of a data warehouse, so the same data serves reports, analytics and AI models. Typical contents are supplier files, product exports, sales and search data, and image metadata. Platforms such as Databricks and Snowflake are common foundations. A lakehouse is not a PIM: it is built for analysis at scale, while a PIM is where teams maintain the approved product content that goes to channels. Many companies use both, with the PIM as the source of product content and the lakehouse combining it with sales and behavior data. Read what a PIM is for the content side.

Why Product Data Lakehouse matters for e-commerce

Large catalogs produce product data in many places: supplier feeds, the ERP, the PIM, the webshop, marketplaces and analytics tools. Answering questions such as which attributes sell better, or which suppliers deliver incomplete data, means combining all of those sources. A lakehouse gives data teams one place to do that without copying everything into a rigid warehouse first. For e-commerce teams the value is in the questions it answers. The product content itself still needs a maintained source, otherwise the lakehouse just stores the same errors at a larger scale.

Examples of Product Data Lakehouse

  • 1Source: Supplier CSV files, Stored as: raw files, Used for: checking supplier data completeness
  • 2Source: PIM product export, Stored as: structured table, Used for: joining content with sales
  • 3Source: Webshop orders and returns, Stored as: structured table, Used for: return rate per attribute value
  • 4Source: Site search logs, Stored as: semi-structured events, Used for: finding missing attributes shoppers search for
  • 5Source: Image metadata, Stored as: files plus a catalog table, Used for: finding products without enough images
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How WISEPIM Helps

  • Export delivers clean product data from WISEPIM as files for a lakehouse or warehouse.
  • Quality Guard rules catch incomplete or inconsistent product data before it is exported.
  • Attribute management keeps attribute names and allowed values consistent, which makes analysis easier.
  • Scheduled exports through automations deliver fresh product files hourly, daily, weekly or monthly.
  • Analytics gives e-commerce teams product and channel reports without building a data platform.

Common mistakes with Product Data Lakehouse

  • Using the lakehouse as the place where product content is edited instead of a PIM.
  • Loading raw files without any naming or ownership rules, which turns the lake into a swamp.
  • Building a lakehouse before the team knows which questions it should answer.
  • Ignoring data quality at the source and trying to fix everything downstream.

Tips for Product Data Lakehouse

  • Start with two or three business questions and load only the data they need.
  • Keep the PIM as the source of product content and export from it on a schedule.
  • Agree on stable product keys, such as SKU and EAN, across every source.
  • Assign an owner to each dataset in the lakehouse.

Trends around Product Data Lakehouse

  • Open table formats such as Apache Iceberg and Delta Lake making data portable between tools.
  • AI models trained or grounded on product and behavior data in the lakehouse.
  • Retailers combining product attributes with search and return data to find content gaps.
  • Cloud platforms offering lakehouse features as a managed service.

Tools for Product Data Lakehouse

  • Databricks
  • Snowflake
  • AWS Lake Formation
  • Azure Synapse Analytics
  • Apache Iceberg
  • WISEPIM (product content source and export)

Related Terms

Attribute CollisionCanonical Data ModelCircular Economy Data ModelCross-Border Trade (CBT) AttributesData ContractData Decay

Also Known As

Product data platformData lakehouse for product dataLakehouse architectureUnified product data platform

Frequently Asked Questions

It is a data platform that stores raw and structured product data together and lets you query it like a warehouse. Teams use it to combine product content with sales, search and return data for analysis and AI.

A data lake stores raw files cheaply but offers little structure or reliability for queries. A lakehouse adds tables, schemas and transactions on top of that storage, so the same data can be used for reliable reports.

No. A lakehouse is built for analysis across many data sources. A PIM is where teams maintain approved product content and send it to channels. In many companies the PIM feeds the lakehouse.

Usually when it has a data team and many sources it wants to analyze together, such as several marketplaces, stores and supplier feeds. Smaller teams often get further with built-in analytics and a clean PIM.

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