Dataphin feature highlights-Dataphin(Dataphin)-阿里云帮助中心

Development

Feature

Demo

Description

Encryption and decryption for data transmission

数据传输的加解密

Security feature highlight - Encryption and decryption for data transmission

Encryption and decryption use cryptographic algorithms to convert data between plaintext and ciphertext. This process protects data from leakage and tampering during transmission and storage. The encryption and decryption feature in Dataphin data integration addresses security issues in data transmission through automated encryption and decryption flows and flexible configuration options.

Quality monitoring for the development pipeline

开发链路的质量监控

Quality feature highlight - Quality monitoring for the development pipeline

You can monitor data table quality automatically using offline integration pipeline monitoring or by applying unique value and NOT NULL constraints to fields in logical tables. If a quality risk is detected in a data table, the system sends an alert to the specified recipients. This helps you immediately understand the quality status of your data tables.

Governance

Feature		Demo	Description
Data Standard	Standardized field naming based on root words	Standard feature highlight - Standardized field naming based on root words	This demo shows how to use a standardized root word library and its smart recommendation feature. Standardized management and a smart recommendation mechanism help you resolve inconsistent field naming. This improves developer efficiency and data governance. The root word library is managed by dedicated personnel and provides duplicate-checking reminders. When you create a table, the system automatically performs tokenization, matches terms with the library, and recommends standard field names in real time. The library can also be used for standardizing the names and translations of business terms, physical tables, and fields. This supports unified asset management and data circulation, ensuring data accuracy and an efficient flow.
	Lookup table template library based on national standards	Standard feature highlight - Lookup table template library based on national standards	This demo shows how to create and use lookup tables from a template library. Lookup tables store standard codes and their names to standardize data value definitions and ensure consistency. In Dataphin, the main functions of lookup tables are to define field ranges and monitor data quality. This ensures data complies with predefined rules and maintains standardization and reliability. Dataphin also supports centralized management of lookup tables and provides built-in, industry-specific templates, such as templates for personal, economic, and regional attributes. This helps you create lookup tables quickly and improves data governance efficiency.
	Automatic standard encoding	Standard feature highlight - Automatic standard encoding	The automatic standard encoding feature allows the system to generate standard codes automatically based on custom rules. This improves the efficiency of standard configuration and ensures encoding consistency. This demo shows how to configure the following encoding rule: `CUST_<data_standard_set_code>_<specific_six-digit_incremental_code_value>`.
Data Quality	Automatic generation of quality monitoring based on standard definitions	Standard feature highlight - Automatic generation of quality monitoring based on standard definitions	Dataphin automates quality rules through data standards. A core challenge of implementing data standards is effectively monitoring data asset compliance and making continuous improvements. The link between the Dataphin Data Standard module and its quality monitoring feature saves you from creating rules manually and improves data governance efficiency. This demo shows how to generate quality rules with one click based on data standard content. It also shows how to reference existing data standards when you create quality rules.
	Standard-driven quality monitoring	Standard feature highlight - Standard-driven quality monitoring
	Flexible configuration of scheduling triggers for quality rules based on monitoring scenarios	Quality feature highlight - Flexible configuration of scheduling triggers for quality rules based on monitoring scenarios	Dataphin quality rules go beyond traditional timed scheduling. They offer multiple trigger mechanisms that better match your business processes. This improves the timeliness and accuracy of data quality monitoring. The main triggers are timed scheduling, data update triggers, and specific node triggers.
	Flexible quality monitoring based on custom SQL	Quality feature highlight - Flexible quality monitoring based on custom SQL	You can use the custom SQL template for quality rules to handle complex scenarios. These scenarios include statistical metrics, such as a month-over-month change greater than 20%, or multi-condition checks, such as abnormal order status transitions. Custom SQL lets you define business logic directly. This ensures that the checks match your requirements and helps you avoid the limitations of built-in templates. You can also use the batch import feature to quickly migrate existing offline SQL rules to the system. This feature supports duplicate checks and provides error prompts, which reduces manual configuration time.
	Quality score (including quality score dashboard and quality overview)	Quality feature highlight - Quality score	The quality score is a metric that quantifies the evaluation of projects, data sources, and individuals. It is calculated by applying weights to key dimensions based on predefined rules. You can use the quality score to quickly identify problems, optimize priorities, and measure the impact of improvements.
	Governance workbench	Quality feature highlight - Governance workbench	The governance workbench helps you view errors from data quality checks. You can then initiate governance operations for these issues, such as rectification, ignoring, or notification. This creates a complete Plan-Do-Check-Act (PDCA) process for asset quality, from planning to rectification, which effectively improves data quality. Dataphin categorizes and compiles statistics on issues by Asset Type, such as Dataphin tables and global tables, to simplify problem identification. It also provides a whitelist feature. You can add issues that do not currently require governance to the whitelist and set an effective period. During this period, no new governance items are generated, but the check records are retained.
Data Security	Automatic inheritance of security classification and levels based on lineage	Security feature highlight - Automatic inheritance of security classification and levels based on lineage	Downstream fields automatically inherit the sensitivity level and detection rules from their direct parent table fields based on field lineage. New data is automatically protected based on the configured default desensitization rules. This reduces manual configuration costs and helps ensure the consistency and relevance of detection results for associated data.
	Data classification template library	Security feature highlight - Data classification template library	Dataphin provides built-in data classification models based on various industry standards, such as classifications and levels for the finance, energy, and power industries. You can use these models to quickly create detection rules.
	Detection features (smart generation)	Security feature highlight - Detection features	Detection features describe data characteristics based on a field's data content and metadata properties using operational conditions such as regular expressions, inclusion, and exclusion. This process enables smart recommendations for relevant data classifications, levels, or data standards.
Metadata Center	Data exploration	Quality feature highlight - Data exploration	The Dataphin data exploration feature uses automated analysis to generate a comprehensive quality report for a data table with one click. This makes data quality checks faster and more efficient. You can configure both manual and automatic exploration. Manual exploration requires only a simple configuration to analyze and visualize key field metrics, such as the percentage of null values, unique values, and value distribution. This provides a complete assessment of data health. After you configure automatic exploration rules, the system periodically checks data quality.

Management Center

Feature

Demo

Description

Custom approval flows

Security feature highlight - Custom approval flows

An approval template is a predefined, standardized framework for an approval process. Use it to standardize the flow rules, approvers, and approval nodes for various permission requests, such as data standard approvals and data downloads. This achieves process automation, transparency, and compliance control.