Create a tag project

Prerequisites

• To perform standard modeling development, you must first create a data sector. Otherwise, only general development is available. For more information, see Create a data sector.

• Before you create a tag project, you must create a compute source. For more information, see Compute Source.

Limitations

• Only accounts with the super administrator, system administrator, or data sector architect role can create a tag project.

• Only accounts with the project super administrator, system administrator, or data sector architect role can request to enable or disable read and write permissions on data tables.

Step 1: Select the project type

1. On the Dataphin homepage, click Planning in the top navigation bar.

2. On the Data Warehouse Planning page, follow the instructions in the figure to open the Create Project dialog box.

   

Step 2: Configure data sector and basic information

1. In the Create Project dialog box, configure the Data sector affiliation, Basic information, and Business information parameters for the tag project.

   Note

   A tag project runs in the production (Prod) environment and is equivalent to a Prod project. Due to differences in the data development workflow, it is called a basic tag project. You can develop and commit data objects within the project. Committed changes are immediately applied to production scheduling and monitoring.

   Area	Parameter	Description
   Data sector affiliation	Data sector	A data sector is required for standard modeling development; otherwise, only general development is available. Note If the data sector uses the Dev-Prod mode, you cannot create standard modeling objects in a basic project that belongs to a Prod business sector. This prevents changes to the data standards of the common layer and avoids data production issues.
   Basic information	Project English name	Enter the English name for the project. The name must meet the following requirements: Must contain only letters, digits, and underscores (_). Cannot start with LD_. Cannot exceed 64 characters.
   	Project name	Enter the display name for the project. The name must meet the following requirements: Must contain only Chinese characters, letters, digits, underscores (_), and hyphens (-). Cannot exceed 64 characters.
   	Compute source type	Select the type of compute source, and then select a specific compute source. Important A compute source that is already bound to a project cannot be bound to another project. Dataphin integrates with Platform for AI (PAI) to provide basic algorithm scheduling capabilities. When you create a visual modeling workspace in the machine learning platform, you must select a MaxCompute-based resource group. For more information, see . If you enable machine learning, you must configure the following parameters. PAI region: Select the region where your Dataphin instance is located. AccessKey ID and AccessKey Secret: Enter the AccessKey ID and AccessKey Secret of the account that needs to access PAI. PAI project name: Select the PAI project. Recommendation: We recommend that the MaxCompute project bound to the current Dataphin project is the same as the MaxCompute project bound to PAI.
   	Description	Enter a brief description of the project.
   Business information	Space type	Specifies the characteristics of the project's development tasks and output data. The default value is Application layer. The available space types are: Intermediate layer: Used to store consistent, accurate, and clean data after processing. Staging layer: Used to store raw data integrated from business systems, which serves as the source for subsequent processing and development. Application layer: Used to define and generate personalized and diverse data metrics for various business scenarios. Common layer: Used to store common aggregated data, such as summary data for a specific dimension within a subject domain.
   Security settings	Global security settings	Security settings let you control data security and access at a fine-grained level, including whether to enable Spark tasks and which authentication mode to use. For more information, see Security settings.
   	Data result download (download approval)	Configure whether project-level data can be downloaded. Downloaded data is no longer under system control. You can add watermarks to remind users to handle data securely and avoid unauthorized sharing. For more information, see Data download configuration. Important Only users with roles other than Visitor can download data results to a local machine.
   	Data permission approval	Data permission approval policies let you specify different approval rules for different data sensitivity levels. Approvers can focus on highly sensitive data while bypassing approval for public data, which reduces their workload. For more information, see Data permission configuration.
   	Asset security policy	Use data security policies to protect sensitive data after installation. You can modify these policies in the Governance > Data Security > Project security policy module. For more information, see Project security policy.
   Commit settings	Code review	Disabled by default. If you enable it, you must also configure a Code reviewer. When enabled, all compute tasks in this project must be reviewed before they are committed. By default, the Project administrator is selected as the Code reviewer. You can also select Custom to assign multiple reviewers.
   Task parameter configuration	Default Flink task parameters	After you enable the real-time engine, enter Flink parameters in the text box. These parameters serve as the default configuration for Flink tasks in this project. Parameters must be in the key-value format: key:value. For example: taskmanager.numberOfTaskSlots:1.

2. Click OK to create the tag project.

Next steps

After you create the tag project, you can bind it to a corresponding mart when you create one. For more information, see Create an asset mart.