DataBuddy Term | English | Description |
Data Directory | Catalog | The top-level container in the three-level namespace (Catalog.Schema.Table), used to centrally manage metadata, permissions, and data assets across workspaces, serving as the core boundary for data governance and access control. |
Schema | Schema | A second-level namespace under the data catalog (Catalog), used to logically group and isolate objects such as tables, views, and functions, typically corresponding to a business domain, project, or environment. |
Tables | Table | Basic storage unit for structured data, containing explicit column definitions and data types. |
View | View | A virtual table defined by one or more SQL queries. It does not store data itself but computes results dynamically at query time, and is commonly used for data encapsulation, permission isolation, and logic reuse. |
Volume | Volume | Storage unit for unstructured and semi-structured data (such as images, audio/video, PDF, JSON, and Parquet files), located under Schema, managed under unified permissions, and directly accessible by Notebooks, tasks, and model training. |
Function | Function | User-defined reusable computing logic that can be written in SQL or Python, registered under Schema, and governed by permissions to extend the platform's data processing and business computing capabilities. |
Metric | Metric | A business-oriented measurable metric definition (such as GMV, retention rate, and number of active users), including its caliber, calculation logic, dimensions, and aggregation method, serving as a core semantic layer asset for unified consumption by BI, Agent, and downstream applications. |
Semantic Model | Semantic Model | A business semantic abstraction layer above the physical data layer, centrally defining metrics, dimensions, entities, and their relationships to provide a consistent semantic caliber for BI analysis, natural language querying, and AI applications. |
Notebook | Notebook | An interactive environment for data development and exploration that supports mixed-language authoring in SQL, Python, and other languages, with visualization output and version management capabilities. |
SQL | SQL | A structured query language script asset used for data querying, processing, and modeling. It can be saved independently, managed with version control, and participate in scheduling as a workflow node. |
Workflow | Workflow | An orchestration and scheduling unit for data tasks that connects nodes such as Notebook, SQL, data ingestion, and model training in the form of a DAG, supporting capabilities such as dependency management, scheduled triggering, and alarming. |
Real-time Ingestion | Realtime Ingestion | A low-latency data synchronization method based on technologies such as CDC (Change Data Capture), which synchronizes data changes from source systems to the data lake in real time to support real-time warehousing and real-time analytics scenarios. |
Offline Ingestion | Batch Ingestion | A method of synchronizing external data sources (such as databases, message queues, SaaS applications, and COS) to a data lake in scheduled batches, suitable for large-scale Data Integration scenarios that do not require high timeliness. |
Model | Model | A machine learning model asset that contains model files, dependencies, signatures, and metadata, supports version management, lineage tracking, and lifecycle management, and can be registered to a model repository for inference or retraining. |
Model Service | Model Service | The capability to deploy registered models as online or batch inference services, providing REST APIs, elastic scaling, traffic routing, and monitoring to integrate model capabilities into business systems. |
Model Experiment | Experiment | A tracking unit for machine learning training processes that records the parameters, metrics, code version, data version, and artifacts of each Run, used for experiment comparison, reproduction, and model selection. |
Dashboard | Dashboard | A data visualization dashboard for business users that displays metrics and analysis results through charts, filters, and interactive components, supporting scheduled refresh, subscription-based distribution, and embedding. |
Agent | Agent | An AI application unit with planning, tool invocation, and memory capabilities that can integrate with metrics, data, models, and external systems to complete complex business tasks and automated processes. |
Buddy | Buddy | An AI assistant embedded in the platform that covers scenarios such as data development, analysis, governance, and Ops, and assists users in writing SQL, explaining code, troubleshooting tasks, and generating documents through natural language. |
Buddy Analysis Space | Buddy analysis space | A work unit through which the Buddy assistant perceives business context, containing associated data assets, metrics, documents, and knowledge to enable AI to understand and respond to business questions more accurately. |
Workspace | Workspace | A top-level resource and permission isolation container of the platform that contains development files, tasks, clusters, members, and configurations, and is typically divided by team, project, or environment. |
File | File | Development asset files in a workspace, covering types such as Notebook, SQL, Python, and configuration, serving as the basic unit for code development and version management. |
Folder | Folder | A directory structure in a workspace for organizing files, supporting nesting, permission inheritance, and batch management. |
Git Folder | Git Folder | A special folder associated with a remote Git repository, supporting operations such as pulling, committing, and branch switching to enable code versioning and team collaboration. |
Backfill | Backfill | The capability to re-run tasks for historical time ranges, used to fix missing data, correct logic errors, or initialize historical partitions, and supporting batch backfill by business date. |
Governed Tag | Governed Tags | Controlled tags applied to objects such as catalogs, schemas, tables, and columns in the data Catalog, used for data classification, sensitive data identification, access policies, and compliance auditing. |
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