Adevinta Thinks Local While Taking Their Data Global

Note: This story was originally published December 2022.

Challenge

Adevinta operates a network of local and global marketplaces, each with its own tech stack and data tools. This autonomy, while empowering individual teams, created a fragmented data landscape and made it difficult to discover and share data company-wide.

“The dynamic between local and global marketplaces led to the proliferation of different platforms over time, creating a divergence in terms of the tools and platforms used in the company.”

—Oscar Ompre, Product Manager, Adevinta

Without a shared discovery layer, teams struggled to locate relevant datasets. Data remained siloed, duplicated work became common, and collaboration suffered.

Solution

To bridge the gap, Adevinta built a centralized data catalog using DataHub Cloud. The team leveraged DataHub’s integrations to ingest metadata from across their stack, including Kafka, S3, Athena, Glue, Hive, Databricks, BigQuery, Redshift, dbt, Snowflake, Looker, Tableau and Datadog, among many others.

They enriched this catalog with detailed metadata: ownership, schema, update history, and lineage. To make it accessible across technical and non-technical users alike, they developed a custom UI with intuitive search, filters, and navigation paths tailored to marketplace and team-specific needs.

Key features included:

• Hourly metadata ingestion across platforms
• Lineage visualization to show data flow and dependencies
• Search filters for dataset name and description
• Marketplace-specific paths to simplify navigation

Impact

With DataHub, Adevinta replaced a fragmented discovery experience with a scalable, enterprise-grade data marketplace. 

Key outcomes included:

• One unified interface for global and local teams to discover and access data
• 65,000+ data entities indexed and made searchable
• Simplified access paths for users to discover, evaluate, and manage data
• Improved cross-team collaboration between data producers and consumers
• Enhanced context through metadata enrichment and lineage

“Once consumers have the necessary permissions to access the data, they can make use of other tools in DataHub to share this data from one place to another, manage it, transform it, create ML models, analyse it and do so much more in order to extract value from the data.”

—Oscar Ompre, Product Manager, Adevinta