Feature highlights

Demos

Watch these demos to quickly learn about Dataphin:

• Learn about Dataphin in five minutes

• A preview of Dataphin feature highlights

References

Platform features

• Introduction to the cross-tenant publishing feature in Dataphin

• Dataphin Publishing Center features: Publishing control

• The custom resource group feature is now available in Dataphin

• Use custom clusters in Dataphin to avoid cross-cluster data transfer (Supported in on-premises deployment mode only)

• Test Flink SQL nodes on session clusters to simulate production code logic (Supported in on-premises deployment mode only)

• Network solutions for multi-tenant data sources in Dataphin on the public cloud

• Create custom global roles to assign permissions by position

• Dataphin permission system (1): Introduction to the permission system and role permissions

• Dataphin permission system (2): Role permissions within a project

• Dataphin permission system (3): Introduction to permission audit capabilities

• Dataphin permission system (4): Introduction to table-level and field-level permissions

• Dataphin permission system (5): Custom project roles

• Dataphin permission system (6): Custom user groups

Data warehouse planning and standardized modeling

• Introduction to the Planning module in Dataphin 3.2

• A deep dive into conceptual modeling in Dataphin

Data integration

• API data sources and integration optimization

• Data integration best practices: Strategies for handling partitioned source tables

• Data integration: Optimization strategies for offline integration node timeouts

• Upgrades to the offline full-database migration feature

• Comprehensive feature optimization for OSS data integration scenarios

• New offline integration node list for quick filtering and batch operations

• Real-time computing node list for quick batch operations

• Dataphin integration nodes support custom FTP marker file content

• Use Dataphin to quickly sync data in batches from MaxCompute external tables to AnalyticDB for MySQL

Data development

• Real-time development with Flink in Dataphin

• Compute source and project configuration for open source Flink

• Use real-time code templates to implement multi-link disaster recovery

• How to perform Hologres development in Dataphin

• Dataphin real-time development practices: Building a real-time data dashboard for an E-commerce scenario

• Preference tags support custom aggregation methods for broader scenario coverage

• Trigger nodes simplify cross-system scheduling dependencies

• Troubleshooting issues with real-time integration of Oracle CDC in Dataphin

• Effectively manage third-party Python packages in Dataphin

Job O&M

• Dataphin runtime diagnostics: A powerful tool to improve the efficiency of runtime error analysis

• Intelligent baselines: Automated alerts to replace manual monitoring

• Data backfill upgrades in Dataphin: Filter by node type and perform batch backfills by entering or pasting node names

Asset Catalog

• Use Dataphin asset collections to build high-quality data assets

• Use Dataphin data profiling to quickly understand your data and identify potential threats earlier

Data Standard

• Use Data Standard to facilitate end-to-end data governance for your enterprise

• Flexible configuration of standard approvals for efficient organizational process management

• Intelligently recommend mappings to accelerate the implementation of data standards

• Automated encoding rules for standards eliminate manual encoding

• Subscribe to data standard changes to stay updated and ensure development quality

• Data Standard applications (1): Standard-to-asset mapping

• Data Standard applications (2): Monitoring standard-to-asset mapping - Part 1

• Data Standard applications (3): Monitoring standard-to-asset mapping - Part 2

Data Quality

• Support rapid business growth with data quality management

• Quality rules support custom properties for easier rule management

• Administration workbench upgrade: Manually enter quality issues for feedback and trackable rectification

• Data Quality best practices (1): Configure quality rules in batches to quickly increase quality coverage

• Data Quality best practices (2): Quickly locate quality issues by archiving and analyzing abnormal data

• Data Quality best practices (3): Track and handle quality issues using the Data Quality administration workbench

• Data Quality best practices (4): Support quality monitoring for ranges and multi-level partitions, and manage data quality by project and user

• Data Quality best practices (5): Use quality scores and leaderboards to improve enterprise data quality (Supported in on-premises deployment mode only)

Data security

• Data security best practices (1): Data security protection in the data development pipeline

• Data security best practices (2): Using data masking whitelists

• Data security best practices (3): Data integration encryption and decryption

• Data security best practices (4): How to use WHERE and JOIN clauses in data masking scenarios

• Data security best practices (5): Manually specifying sensitive data

• Data security best practices (6): Real-time detection and batch protection of sensitive data

Analysis Platform

• Dataphin introduces a new module: Analysis Platform

Tag Factory

• Create audience groups in the tag platform and query group data using APIs

• Preference tags support custom aggregation methods for broader scenario coverage

• Behavioral relationships support lookup tables to make tag processing and understanding more intuitive

• Flexible tag export: Meet diverse output data needs with one click

• Public calendar scheduling and data screening: A data output method better suited for business scenarios

DataService Studio

• Strategies for handling partitioned source tables

• Set a default collation for APIs to ensure data correctness

• Data security best practices (1): Data security protection in the data development pipeline

• Data security best practices (2): Using data masking whitelists

• Data security best practices (3): Data integration encryption and decryption

• Data security best practices (4): How to use WHERE and JOIN clauses in data masking scenarios

• Data security best practices (5): Manually specifying sensitive data

• Data security best practices (6): Real-time detection and batch protection of sensitive data

• Data security best practices (7): Building a business security system with multi-level security classification

OpenAPI (supported in on-premises deployment mode only)

• Manage Dataphin members using OpenAPI

• How to build an automated data backfill tool using APIs