About Data Tanks

Treasure Data Data Tanks allow you to create a subset of your data so that you can perform interactive analytics and reporting on it.

Data Tanks provide easy access to your aggregated metrics through convenient, fully hosted data marts on the Treasure Data platform. They can be used to drive a variety of external business intelligence and visualization applications without having to host and maintain your own PostgreSQL instances.

Data Tanks are PostgreSQL databases that accelerate analytical queries. They are completely managed by Treasure Data, including creation, setup, monitoring, management, and troubleshooting, so you can focus on getting your job done.

Treasure Data can be considered an event data lake where disparate event data sources (and a few slow-moving dimensions) are aggregated and processed to create more compact and cleaner data packages for further processing, analysis, or visualization.

Given the size and scope of an event data lake, providing highly concurrent interactive access over trillions of data points while retaining schema flexibility is technologically challenging. As a way to work around this limitation, there is a design pattern called lakeshore data marts.

Image credit: Martin Fowler, DataLake

Data Tanks use the "data as water" metaphor to provide a mental model for how data pipelines for analytics work. They provide a convenient and accessible metric store to drive business intelligence and data visualization tools from Treasure Data without the burden of managing separate data marts.

Data Tank is available as row-oriented.

Data Tanks provide the following key benefits:

• Management UI — Console for user and database schema management.
• High availability — Option for critical business processes.
• Presto SQL queries — Query Data Tank data using Presto SQL, including the creation and deletion of tables.
• Flexible processing flows — Join Data Tank and core Treasure Data table data.

Treasure Data provides the following types of Data Tank users:

For accessing Data Tanks from Treasure Data components. For example, outputting results to Data Tanks or using the pg> operator from Treasure Workflow.

For accessing Data Tanks from client-side applications. You can connect to Data Tanks using standard PostgreSQL clients with this user.