Data Tank is obsolete. We recommend to using DataTank2.0
Data Tanks allow you to create a subset of TD 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 Treasure Data’s core 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 are used to accelerate analytical queries. They are completely managed by Treasure Data including creation, setup, monitoring, management and troubleshooting so you can just get 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 impossible (at least affordably). As a way to work around this limitation, there is a design pattern called lakeshore data marts. For example:
image credit: Martin Fowler, DataLake
Our Data Tanks use the data as water metaphor to provide a mental model for how data pipelines for analytics works.
Data Tanks provide a convenient and accessible metric store to drive business intelligence and data visualization tools from Treasure Data without the burden of managing one or more separate data marts.
Data Tank is available as:
- Row-oriented
Use of Data Tanks provides the following key benefits:
Management UI console for user and database schema management
High availability option for critical business processes
Enables queries of Data Tank data using Presto SQL, includes the creation and deletion of tables.
Flexible processing flows by joining Data Tank and core TD table data.
TD provides the following types of users:
For accessing Data Tanks from Treasure Data components. For example, output result to Data Tanks, pg> operator from TD Workflow.
For accessing user side client. You can access Data Tanks, like PostgreSQL, with this user.