Features

Data Integration

Feature set	Feature	Description	References
Data Integration	Data source management	DataWorks Data Integration supports dozens of data sources such as MySQL, MaxCompute, Hologres, OSS, and Kafka as input and output data sources for data integration tasks. You can manage these data sources in the Data Integration module.	Create a data source Configure a data source by using RAM role-based authorization Network connectivity configuration
	Single-table batch synchronization task management	Allows you to configure and manage single-table batch synchronization tasks. The data channel for single-table batch synchronization defines source and destination data sources and datasets, provides a set of abstracted data extraction plugins (Reader) and data writing plugins (Writer), and designs a simplified intermediate data transfer format based on this framework to enable data transfer between any structured or semi-structured data sources.	Configure a batch synchronization task by using the codeless UI Configure a batch synchronization task by using the code editor
	Single-table real-time synchronization task management	Allows you to configure and manage real-time synchronization tasks that synchronize data changes from a single table in the source database to the destination database in real time, ensuring that the destination table maintains real-time data consistency with the source table.	Single-table real-time synchronization capabilities Configure a single-table real-time synchronization task
	Full-database batch synchronization task management	Supports incremental data synchronization for entire databases, allows you to synchronize change logs of an entire database to the destination, and supports one-time configuration of multiple tables across multiple databases under a single instance.	Full-database batch synchronization task capabilities Configure a full-database batch synchronization task
	Full-database real-time synchronization task management	The full-database real-time synchronization feature combines one-time full synchronization with continuous incremental capture to synchronize an entire source database (such as MySQL or Oracle) to the destination system with low latency.	Full-database real-time synchronization task capabilities Configure a full-database real-time synchronization task
	Synchronization task O&M	Allows you to manage synchronization tasks, monitor task running status, view task running metrics, modify synchronization resource groups, and view task runtime logs.	Batch synchronization task O&M Real-time synchronization task O&M Full-database synchronization task O&M
Data Upload & Download	Data upload	Supports uploading data files in multiple formats such as local files (CSV, Excel), OSS, and spreadsheets to MaxCompute, Hologres, and EMR Hive.	Data upload
Data Upload & Download	Query result download	Supports saving CSV and XLSX files downloaded from Data Studio, Data Analysis, and Security Center modules to the data download list. You can re-download historical files to your local environment and trace the operation details of historical download records.	Data download

Data Modeling

Feature set	Feature	Description	References
Data warehouse planning	Layering and domain division	Allows data warehouse architects or modeling team members to design data layers, business categories, data domains, business processes, data marts, and subject domains on the data warehouse planning page. After the design is complete, model designers can manage the models they create by using the data layers, business categories, data domains, and business processes defined in the data warehouse planning.	Planning overview Business planning Data warehouse layering
	Modeling workspace	Supports reusing the same data warehouse planning across workspaces and managing data models across workspaces.	Modeling workspace
	System management	Allows you to customize physical table creation governance policies, code generation rules, and other settings required during data warehouse model design.	System management
Data standard	Column standard	Allows you to define value ranges, measurement units, and other properties of columns for unified and standardized management of data that has the same meaning but different column names.	Column standard
	Standard code	Allows you to configure the available data content and range for a column standard in standard codes, which is used to determine the value range of the column standard.	Standard code
	Measurement unit	Allows you to create custom measurement units to specify the quantity units of columns (such as pieces and centimeters). Pre-built measurement units are also provided.	Measurement unit
	Naming dictionary	Allows you to customize a business dictionary for table name generation, column name translation, and other features.	Naming dictionary
Dimensional modeling	Reverse modeling	Allows you to batch-generate models from existing physical tables in a physical engine and manage all models in the Data Modeling module.	Reverse modeling: Generate models from physical tables
Dimensional modeling	Dimensional modeling	Based on Kimball dimensional modeling theory, supports managing source tables, creating dimensions, dimension tables, detail tables, aggregate tables, and application tables, and deploying models to corresponding compute engines. Also provides DDL code for models and MaxCompute ETL code.	Create a conceptual model: Dimension Create a logical model: Dimension table Create a logical model: Detail table Create a logical model: Aggregate table Create a logical model: Application table Code-based modeling Import-based modeling Materialize a logical model Generate ETL code based on a model
Data metric	Atomic metric	Atomic metrics are used to define business statistical criteria and calculation logic. They are created based on business activities (business processes) and are used to measure a specific business condition within the business activity.	Atomic metric
	Derived metric	Derived metrics are composed of atomic metrics, time periods, and modifiers, and are used to reflect the business status of a specific business activity within a specified time period and target scope.	Derived metric
	Composite metric	Provides composite metrics that are calculated from derived metrics through computation rules, enabling more flexible and fine-grained business metric definitions.	Composite metric
	Modifier	Modifiers are created based on selected data domains. They are dimensional qualifiers for data within a data domain and are used to limit the business scope of statistical data.	Modifier
	Time period	Time periods define the time range or time window for data statistics, such as the last 1 day or the last 1 natural week. They are used to limit the time range of business statistics when calculating derived metrics.	Time period
General tools	Model import	Allows you to edit model objects based on templates for data models, data standards, and other object types, and import them into the Data Modeling module.	Import
Relationship diagram	Relationship diagram	Allows you to quickly build data warehouse model architecture diagrams that visually display the relationships between models such as dimensions, dimension tables, detail tables, aggregate tables, and application tables. A relationship diagram represents one data warehouse model, and you can create multiple relationship diagrams per account.	Relationship diagram
Industry model template	Industry model template	Provides industry model best practices for multiple domains such as retail and e-commerce, finance, and manufacturing. You can use these templates to quickly build your data warehouse.	-

Data development and O&M

Feature set	Feature	Description	References
Data Studio (legacy)	Task and code management	The DataWorks Data Studio module is used to define the development and scheduling properties of scheduled tasks. It works with Operation Center to provide a visual development interface for various engines (MaxCompute, Hologres, EMR, etc.), supporting intelligent code development, multi-engine hybrid workflows, and standardized task deployment, helping you efficiently build offline data warehouses, real-time data warehouses, and ad hoc analysis systems with stable data production.	Data Studio (legacy)
	Code editor	Provides online SQL development capabilities, including keyword suggestions, code auto-completion, real-time syntax validation, and permission validation to improve SQL development efficiency.	Editor productivity features
	Node development	DataWorks encapsulates different types of engine tasks as different nodes. You create nodes to generate data development tasks. Data Studio also supports developing complex tasks using resources, functions, and various logic processing nodes.	Node development Supported node types
	Node orchestration and scheduling	Provides visual task orchestration capabilities. If a task needs to run on a periodic schedule, you need to define its scheduling properties including schedule, dependencies, and scheduling parameters.	Node schedule settings Create a workflow Create a manual task
	Node debugging and testing	After code development is complete, you can use features such as Run, Run with Parameters, and Quick Run to debug complete code or code snippets, and view the results after debugging is complete.	Task debugging process Run smoke testing
	Table management	The table management feature is used to define identifiers for partition columns, temporary tables, and imported tables in DataWorks, allowing you to determine the category of a table by its name. It also supports defining table topics and physical levels, enabling you to group tables of the same type under the same topic or level for unified management based on function, type, and other dimensions.	Table management View public tables External tables
	Resource management	You can use the MaxCompute resource panel to view resources in the MaxCompute compute engine, view resource change history, and add resource files to workflows in the Data Studio panel with one click.	MaxCompute resource management Create and use EMR resources
	Function management	You can use the MaxCompute function panel to view functions in the MaxCompute compute engine, view function change history, and add functions to workflows in the Data Studio panel with one click.	Create and use custom functions Use built-in functions MaxCompute function management Create an EMR function
	Operation management	In the Data Studio interface, you can filter by operation type, operator, and operation time on the operation history page to view operation records in the current workspace.	View operation records View node versions
	Custom data development process	DataWorks provides end-to-end data development governance capabilities with a unified development governance process. You can also define custom governance controls at key process points based on your business requirements.	Development process governance
Data Studio (new)	Directory management	Manages code files, task nodes, workflows, and other objects, including the project directory and personal directory.	Project directory Personal directory
	Node development	Provides various node types to meet different data processing needs: Data Integration nodes for synchronization, engine compute nodes (such as MaxCompute SQL, Hologres SQL, and EMR Hive) for data cleansing, and general nodes (such as virtual nodes and do-while nodes) for complex logic processing.	Node development
	Workflow	Workflow is an automated data processing workflow management tool that integrates multiple types of sub-task nodes through visual drag-and-drop, making it easy to establish task dependencies, accelerate data processing workflow construction, and improve task development efficiency.	Scheduled workflow Manual workflow
	Notebook	Notebook provides an interactive, flexible, and reusable data processing and analysis environment with enhanced intuitiveness, modularity, and interactivity, helping you perform data processing, exploration, visualization, and model building more easily.	Notebook
	Schedule settings	Nodes and workflows in the project directory are typically scheduled periodically. You can configure scheduling properties in the schedule settings panel of a node or workflow, including the schedule, dependencies, and scheduling parameters.	Node scheduling Scheduling parameters Scheduling policies Scheduling time Scheduling dependencies
	Node/workflow deployment	After you complete data development and scheduling dependency configuration in Data Studio, you can deploy tasks to the development environment and production environment so that they can run in these environments.	Node/workflow deployment
	Intelligent assistant	Allows you to use DataWorks Copilot during data development.	Intelligent coding assistant
	Component management	Components are used to abstract SQL processes into SQL templates to enable SQL code reuse.	Component management
	Data Catalog	Supports the OpenLake lakehouse architecture for unified metadata management, diversified table creation methods, and intelligent table creation assistance, improving data development efficiency and meeting diverse metadata creation and management needs of different user types.	Data Catalog
	Resource management	Allows you to reference custom resources and functions in your data development code (supporting MaxCompute, EMR, CDH, and Flink). You can create or upload resources and functions to the target workspace. They can only be used in tasks within that workspace after being uploaded.	Resource management MaxCompute resources and functions EMR resources and functions CDH resources and functions Flink resources and functions
	Operation check	DataWorks provides end-to-end data development governance capabilities with a unified development governance process. You can define custom governance controls at key process points based on your business requirements.	Operation check
	Recycle bin	Stores all deleted task nodes, workflows, tables, and resources in the current workspace. You can restore or permanently delete nodes.	Recycle bin
	Run history	The run history panel displays your code execution records in the Data Studio interface from the last three days.	Run history
	Personal development environment	The personal development environment supports connecting to NAS, Git, Python programming, and Notebook.	Personal development environment Access the Internet from a personal development environment through a dedicated Internet gateway Connect a personal development environment to a Git repository
	Settings	Scheduling settings, security settings, and other related settings.	System settings
	More operations	Code review, smoke testing, batch operations, code search, and more.	Code review Smoke testing Code search
Code review	Node code review	Provides code review capabilities. After a task is submitted, you can specify a code reviewer. The task can only be deployed to the scheduling system after the review is approved.	Code review
Deployment Center	Node deployment management	Provides node deployment capabilities, allowing you to deploy code to the production scheduling system.	Deployment Center Deploy within the same workspace Deploy across workspaces Deploy across clouds
Operation Center	O&M dashboard	The O&M dashboard displays workspace O&M stability assessments, key O&M metrics, scheduling resource usage, scheduled task running status, and Data Integration synchronization task running details, helping you quickly understand the overall task status from a macro perspective, identify and handle abnormal tasks in a timely manner, and improve O&M efficiency.	View the O&M dashboard
	Scheduled task O&M	Scheduled tasks are tasks that are automatically run on a periodic basis by the scheduling system based on schedule settings. You can view scheduled tasks in a workspace on the Scheduled Tasks page in Operation Center and perform task operations, including automatically scheduling and manually running scheduled tasks, viewing task details, pausing tasks, and undeploying tasks.	Basic O&M operations for scheduled tasks Manage scheduled tasks View scheduled instances Backfill data and view backfill instances (new) Run tests and view test instances Task running diagnostics
	Real-time task O&M	Provides real-time task O&M capabilities, displaying the running status of real-time computing and real-time synchronization tasks.	Run and manage real-time computing tasks Run and manage real-time synchronization tasks
	Manual task O&M	Provides manual task O&M capabilities, displaying manual task definitions and manual task instances, and providing basic O&M operations for tasks and instances.	Run and manage manual tasks View and manage manual instances
	Intelligent baseline	Intelligent baselines can promptly detect anomalies that prevent tasks on a baseline from completing on time and provide early warnings, ensuring that important data in complex dependency scenarios is delivered within the expected time. This helps you reduce configuration costs, avoid unnecessary alerts, and automatically monitor all important tasks.	Baseline management Baseline instances Event management
	Monitoring and alerting	You can use the alert information feature to view all alerts generated by the intelligent monitoring module. This includes baseline early warning information and event alerts from intelligent baselines, alerts generated by custom rules, and alerts generated by global rules.	Rule management Alert information Duty roster
	Engine O&M	In DataWorks instance tasks, E-MapReduce compute engine tasks are split into multiple jobs that run in sequence. You can use the engine O&M feature of DataWorks to view detailed information about each E-MapReduce job, identify and clean up faulty jobs in a timely manner, and prevent such jobs from blocking downstream tasks and affecting normal instance task execution.	Engine O&M
	Scheduling settings	Provides global scheduling settings where you can configure scheduling calendars, workspace parameters, and other advanced scheduling configurations.	Scheduling settings
	Intelligent diagnostics	You can use the intelligent diagnostics feature to perform end-to-end analysis on tasks. When a task does not run as expected, you can use this feature to quickly locate the issue.	Intelligent diagnostics
	Operation history	The operation history page displays your operation records on various pages in Operation Center. You can use this feature to trace historical operations and view operation details.	View Operation Center operation records

Data Governance

Feature set	Feature	Description	References
Data Map	Metadata collection	Imports metadata from different data sources into Data Map for unified management through the metadata collection feature. After collection is complete, you can search for and view the metadata information of each data source in Data Map.	Metadata collection
	Metadata search	Allows you to quickly search for metadata objects such as engine tables by keywords and use various filter conditions for combined search.	General data query and management
	Metadata access control	Supports metadata permission control at three levels in Data Map: feature module, project, and table. You can configure view permissions for metadata.	Appendix: Overview of Data Map permission control capabilities
	Data categories and collections	Allows you to configure category navigation on the Data Map configuration management page so that users can search for metadata from a business perspective. Also supports viewing metadata details.	Configuration management Business perspective management: Data Collection My data
	Data lineage	Supports lineage relationship display for different metadata objects, with a focus on table-to-table and column-to-column data flow lineage.	View lineage
	Data management	Allows you to modify the lifecycle, owner, and descriptions of tables and columns. You can also bookmark and view tables.	MaxCompute table data Lindorm table data
	Metadata details	Allows you to view detailed information about data objects in Data Map, including multi-dimensional technical metadata and business metadata information.	MaxCompute table data Lindorm table data Data Service API View workspace information
Data Quality	Quality dashboard	Displays key data quality overview metrics, the trend and distribution of quality rule validation status triggered after instance execution, top quality issue tables and issue owners, and quality rule coverage information for the current workspace.	Access the Data Quality dashboard
	Quality rule configuration	Supports quality rule configuration by single table within a data source type or by rule template. Configured rules support subscription management, modification, enabling/disabling, scheduling association, and strong/weak rule type settings. Supports multiple validation methods including fixed values, period-over-period comparison, 1-day, 7-day, and 30-day fluctuation rates, and dynamic threshold validation.	Data quality rule configuration process Configure rules: By table (single table) Configure rules: By template (batch)
	Quality validation task query	Allows you to view the list of completed quality validation tasks and task details through the quality validation task list. You can also view historical results and issue data, and record the issue handling process.	Quality monitoring list View quality monitoring execution details
	Quality alert subscription	Supports multiple quality alert subscription channels, including email, SMS, DingTalk group bots, WeCom bots, and Lark group bots.	Configure rules: By table (single table) Configure rules: By template (batch)
	Quality rule template library	Supports building a custom rule template library through unified management of custom rules to improve rule configuration efficiency.	View built-in rule templates Create and manage custom rule templates
	Quality report	The quality report management page supports dynamically configuring quality metrics for report templates, and generating and sending reports on a scheduled basis based on the configured templates.	Configure monitoring report templates
Security Center	Security overview	Provides user to-do task guidance, security risk event trends, and security status statistics for data assets.	Security Center
	Data access control permissions	Users can request permissions on databases, tables, and columns through DataWorks (supporting MaxCompute, DLF 1.0, Hologres, StarRocks, and Hive). Access is granted after administrator approval. Supports viewing request records, administrator approval records, and permission audits.	MaxCompute data access control DLF data access control Hologres data access control
	High-risk behavior control	Controls high-risk behaviors (such as download) with processing methods including rejection, alerting, and triggering an approval workflow. You can customize high-risk behavior controls through Extensions.	Risk identification rules
	Risk detection	Identifies and determines potential data leakage risks based on a series of user operation behaviors, and marks them as risk events. Supports enabling/disabling system-preset risk detection items. Supports custom risk detection items with configurable detection and notification rules.	Security risks
	Data Classification	Allows you to set up data classification templates and customize classification structures and classification rules. Allows you to configure identification rules for each sensitive data type.	Data Classification
	Sensitive data identification	Allows you to create sensitive data identification tasks to identify sensitive data within a specified scope of data assets. Supports periodic tasks and one-time tasks. Allows you to view identification result details and sensitive data distribution. Allows you to adjust sensitive data identification results through revisions.	Data Classification
	Sensitive data protection	Provides three types of masking solutions: DataWorks dynamic masking, DataWorks static masking, and engine-level masking. DataWorks dynamic masking: When users access sensitive data in DataWorks Data Map, Data Analysis, or Data Studio, they can only view masked data. The original data remains unmasked. DataWorks static masking: Sensitive data is masked during DataWorks Data Integration before storage. Engine-level masking: Uses the masking policies of the engine directly (such as MaxCompute masking policies).	Data masking
	Data watermark tracing	Supports tracing leaked data to identify possible leakage behaviors. Provides information such as the time of the leakage behavior, operator, and executed SQL details to help administrators trace data leakage events.	Data tracing
	Sensitive data access audit	Supports auditing user access to sensitive data for up to one month. Provides information such as the time of access, operator, accessed data details, and executed SQL details.	Sensitive data access
	Data Analysis and result control	Authorizes RAM users to access data sources using other identities in DataWorks Data Analysis. Controls RAM user behaviors such as downloading, exporting, and sharing Data Analysis results.	Data query and analysis control
	Entity transfer	Supports setting tenant-level transfer rules and workspace transfer rules. Supports manually triggering entity transfers. Supports automatically triggering entity transfers, for example, when an account is deleted or removed from a workspace.	Entity transfer
	Identity credential management	Supports mapping Alibaba Cloud RAM accounts to the native users of a data source, enabling RAM accounts to access database/table/column data in the data source using a specified user identity.	Identity credentials
	Ranger management	Integrates with Ranger to enable users to request and approve data access control permissions for StarRocks and Hive in DataWorks.	Add a Ranger configuration
	Platform security baseline check	Supports security monitoring across multiple aspects such as data collection, transmission, storage, computing, and sharing, and provides improvement suggestions.	Platform security diagnostics
Data Security Guard	Data classification	1. Allows you to add classification rules based on data content and business attributes to Data Security Guard. 2. Allows you to add classification level rules based on data value and leakage impact to Data Security Guard, including definitions for top secret, confidential, and secret data.	Configure sensitive data classification
	Sensitive data identification	1. Automatically identifies and locates sensitive data based on user-defined sensitive type identification rules, and clarifies the distribution of sensitive data on the data resource platform. 2. Supports identification within the project scope specified in system configuration. 3. Visually displays the running status of sensitive data identification tasks, distribution of sensitive data, classification details, and column details, and supports filtering by data engine, project, sensitive data type, and classification level.	Configure data identification rules and run identification tasks Identify through custom models Identify through sample libraries
	Sensitive data protection	When users access data, returns real-time, transparent, and seamless masked data based on user roles, permissions, the page being accessed, and the sensitive data type. Commonly used in application dynamic masking and O&M dynamic masking scenarios.	Create a data masking scenario Create a masking rule
	Sensitive data access audit	1. Automatically audits sensitive data access behaviors for up to one month. 2. Visual display: provides chart-based visualization with access details including SQL details, data details, and user information, and supports filtering by data engine, project, sensitive data type, classification level, access user, access data volume range, and access time.	Sensitive data overview Sensitive data access and export
	Data risk identification	1. Automatic data risk identification: automatically identifies data risks based on configured risk identification rules for up to one month. 2. Data risk details display: shows risk disposition results and risk details (such as matched risk rules and matched risk behavior information), and supports filtering by risk status, data engine, project, sensitive data type, classification level, export behavior, user account, data volume range, and access time.	Risk identification management (new)
	Data tracing	Data tracing: After obtaining leaked data, you can upload the leaked data to trace possible leakage source behaviors.	Sensitive data tracing
Data Asset Governance	Governance health score assessment	Displays the overall governance health score, governance assessment level, detailed health scores across five governance dimensions (storage, computing, quality, development, and security), governance effectiveness, governance issue trends at the tenant, workspace, and personal levels, and provides a governance leaderboard showing rankings of governance items and check item events.	Data Asset Governance
	Asset governance optimization	From the tenant, workspace, and personal perspectives, displays the detailed list of governance issues across five governance dimensions (storage, computing, quality, development, and security) that require attention, along with corresponding processing operations.	Asset health and business assets Handle governance work items Handle check item events Add invalid issues to the allowlist
	Asset inventory analysis	Provides DataWorks and MaxCompute resource usage overview statistics, usage rankings, and anomaly analysis.	Usage overview
	Governance knowledge base	Provides descriptions, handling methods, and precautions for each governance item and check item.	Knowledge base
	Asset governance configuration	Provides configuration of governance items and enable/disable controls for check items.	Configure check items Configure governance items Configure governance units
	Data asset management	Allows asset administrators to build custom enterprise data asset directories and manage asset directory views for better enterprise data asset governance, optimization, and improving the ability of business users to find the data assets they need.	Resource type perspective Development pipeline perspective
	Automated governance	Provides workflow-based governance processing mechanisms to help you perform automated governance in scenarios such as coordinated multi-object undeployment.	Graceful undeploy Materialized view
Asset tags	Tag management	Allows you to customize asset tags defined by tag keys and values, and associate tags with related assets (tag assets). Supports asset query and management based on tag associations.	Tag management

Data Analysis

Feature set	Feature	Description	References
Data Analysis	SQL query	Allows you to query and analyze data from data sources such as MaxCompute, EMR Hive, and Hologres using SQL statements. Query results can be saved as enhanced analysis cards and reports, saved as spreadsheets, or downloaded to local files.	SQL data analysis query (legacy) SQL data analysis query (new)
	Spreadsheet	Allows you to view SQL query results or manually edit data in spreadsheets, and supports online sharing of spreadsheet data.	Spreadsheet
	Visual cards and reports	Allows you to save data query results as data cards and reports online, supports scheduled card data refresh, and makes it easy to create personalized visualizations to tell data stories and express data insights.	Visual cards and reports
	Notebook	Notebook provides an interactive, flexible, and reusable data processing and analysis environment with enhanced intuitiveness, modularity, and interactivity, helping you perform data processing, exploration, visualization, and model building more easily.	Notebook
	Data Catalog	Supports the OpenLake lakehouse architecture for unified metadata management, diversified table creation methods, and intelligent table creation assistance, improving data development efficiency and meeting diverse metadata creation and management needs of different user types.	Data Catalog
	Dimension table	Allows you to create MaxCompute tables in the production environment and import local data through a visual interface. You can also directly modify data in MaxCompute tables within the table editor.	Dimension table creation and management

Data Service

Feature set	Feature	Description	References
Data Service	API development	Supports generating APIs by using the codeless UI or the code editor. The codeless UI supports visual API configuration. In the code editor, you can write custom query SQL for APIs, supporting multi-table joins, complex queries, and aggregate functions.	Generate an API by using the codeless UI Generate an API by using the code editor
	API management	Provides lists of deployed APIs, authorized APIs, and APIs with granted access, and supports operations such as undeploying, testing, granting access, protocol changes, and viewing details for APIs.	Register an API Deploy an API
	API functions and filters	Supports writing Aviator functions to set pre-filters and post-filters for APIs, enabling preprocessing of API request parameters or secondary development of API return results to enhance API logic and adapt to various scenarios.	Create an Aviator function Manage functions Test, deploy, and use functions
	API testing	When generating an API, if you need to deploy the API to API Gateway for hosting, you need to test the API to verify that the request parameters and return results meet expectations. You can also test deployed APIs.	Test an API
	API invocation and authentication	Supports two authentication methods for API invocation: simple identity authentication through AppCode, and encrypted signature identity authentication through AppKey and AppSecret, greatly ensuring the security of your data sharing.	Grant access to an API Call an API
	API metering	Supports metering for APIs deployed to API Gateway, providing cumulative total invocation count and total invocation duration over the last 7 days, as well as detailed metering data for each API.	View the metering dashboard View metering details
	API acceleration service	Provides acceleration capabilities for queries on certain data source tables, including acceleration provided by DataWorks Data Service and the acceleration solution (MCQA) natively supported by MaxCompute.	Acceleration service
	Data table	Allows you to preview the target table structure by filtering by data source type and table name.	Data Push
	Data Push and subscription	Supports configuring data query result push and content subscription.	Data Push
	Data Push management	Provides a list of deployed Data Push tasks and supports viewing and managing running instances of Data Push tasks.	Data Push
	Data Push testing	Supports testing undeployed or deployed Data Push tasks.	Data Push

Intelligent assistant

Feature set	Feature	Description	References
Copilot	Code completion	When you write SQL code, DataWorks Copilot provides intelligent code completion for the SQL being written based on the current context information.	Intelligent assistant (DataWorks Copilot)
	Code generation	DataWorks Copilot supports automatically generating SQL/Python statements based on natural language input.	Intelligent assistant (DataWorks Copilot)
	Code rewriting	DataWorks Copilot supports rewriting specified code snippets based on natural language input.	Intelligent assistant (DataWorks Copilot)
	Comment generation	DataWorks Copilot supports generating comments for specified code to improve SQL readability.	Intelligent assistant (DataWorks Copilot)
	Code explanation	DataWorks Copilot supports explaining specified code to improve SQL readability.	Intelligent assistant (DataWorks Copilot)
	Code correction	DataWorks Copilot supports finding and fixing errors in specified code snippets, and also supports one-click invocation of Copilot Chat to fix syntax errors in the editor, with one-click navigation to the error location.	Intelligent assistant (DataWorks Copilot)
	Code optimization	DataWorks Copilot supports optimizing selected code to help you simplify code logic, improve code execution efficiency, and reduce database load.	Intelligent assistant (DataWorks Copilot)
	Code testing	DataWorks Copilot supports providing test plans for selected code and generating test code to help you verify whether each part of the task code works as expected.	Intelligent assistant (DataWorks Copilot)
	Code Q&A	DataWorks Copilot supports providing explanations and usage examples for SQL syntax or MaxCompute functions you ask about, helping you deepen your understanding of SQL syntax and functions.	Intelligent assistant (DataWorks Copilot)
Agent	Agent	DataWorks Agent is based on the MCP (Model Context Protocol) and integrates with DataWorks MCP Server and other big data MCP Servers (such as Hologres MCP Server). It enables data development, task operations, and data integration capabilities in DataWorks through natural language interaction.	DataWorks Agent

Approval Center

Feature set	Feature	Description	References
Approval Center	Approval policy management	Defines governance processes for key data resources and sensitive behaviors by specifying the scope of approval objects and defining approval workflows. Also supports sending notifications through SMS, email, and DingTalk.	Compute engine approval policy Data Service approval policy Extensions approval policy
	Request records	Supports querying previously submitted requests and previously approved requests.	View and handle approval items
	Pending my approval	View all pending approval requests.	View and handle approval items
	Approval records	View all completed approval records under the current account.	View and handle approval items

Migration Assistant

Feature set	Feature	Description	References
Migration Assistant	Task migration to the cloud	Provides migration capabilities for open-source scheduling engine tasks, supporting migration of jobs from Oozie, Azkaban, Airflow, DolphinScheduler, and other open-source scheduling engines to DataWorks.	Task migration to the cloud and cross-platform migration
Migration Assistant	DataWorks migration	Provides the ability to migrate DataWorks development artifacts, supporting migration of tasks, resources, functions, table DDL, data sources, Data Service APIs, data quality rules, and other development artifacts.	Create and view DataWorks export tasks Create and view DataWorks import tasks

Open Platform

Feature set	Feature	Description	References
Open Platform	Developer console	Allows you to view all feature modules of Open Platform, switch the region used by Open Platform, or view typical application scenarios of Open Platform.	Open Platform

Management and configuration

Feature set	Feature	Description	References
DataWorks console	Workspace list	Allows you to create, delete, and disable workspaces, and manage and configure the properties of a specified workspace. A workspace is the basic unit for task development and member permission management in DataWorks.	Workspace management
DataWorks console	Resource group management	Allows you to create and manage resource groups that support different use cases including task scheduling, Data Integration, and Data Service. Supports specification changes and scaling.	Resource group management
Administration	Workspace configuration	Allows you to manage workspace basic properties, scheduling properties (whether to enable scheduling, error retry count), security settings (limit query result count, associate sandbox allowlist), and associated compute engines.	Manage workspaces
	Data source management	Provides a data source management page where you can create databases or data warehouses as DataWorks data sources and associate them with corresponding DataWorks feature modules.	Create and manage data sources
	Compute resource	Provides a compute resource management page where you can associate data warehouses, compute engines, and real-time analysis engines as compute resources. Only successfully associated compute resources can be used within the workspace.	Associate compute resources
	Extensions management	Allows you to register service programs as DataWorks Extensions that intercept and respond to subscribed event messages, enabling message notification and process governance for specific events through Extensions.	Develop and deploy Extensions: Self-hosted service method Develop and deploy Extensions: Function Compute method Apply Extensions Advanced usage: Extensions parameter configuration Advanced usage: Extensions option configuration
	Alert settings	Allows you to view alert resources in the specified region for the current account on the alert resources page, and set the daily upper limit for alert SMS and phone calls.	View and configure alert resources View and configure alert contacts
	Tenant member and role management	Allows you to add, remove, or modify members in the workspace.	Appendix: Preset role permission list (workspace level)

Basic capability billing

Feature set	Feature	Description	References
Resource fees	DataWorks general resource group	Supports general resource groups.	Serverless resource group billing