菜单
Singdata Lakehouse official documentation knowledge base (English). Consult references/ when writing SQL or answering questions about query syntax, functions, data types, DDL/DML, dynamic tables, permissions, vclusters, data lake, AI functions, and other Lakehouse topics.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Complete guide for managing ClickZetta Analytics Agent domains via cz-cli: domain CRUD, table registration, column semantics (types/descriptions/aliases), metrics (simple & compound), Answer Builders, Knowledge Bases, and QA testing. Wizard-driven workflows with intent classification, routing tables, and troubleshooting references. Trigger when the user says: "analytics agent", "分析域", "语义层", "指标", "answer builder", "知识库", "多别名", "column semantics", "domain prompt", "metric create", "AB create", "session run", "domain table add", "semantics set", "knowledge space". Keywords: analytics agent, domain, semantics, metric, answer builder, knowledge base, alias, cz-cli, data analysis, AI agent, semantic layer
Create, query, and manage ClickZetta Lakehouse Semantic Views — schema-level logical models that encapsulate multi-table JOINs and aggregations into a business-friendly layer of logical tables, dimensions, metrics, and facts. Query with the semantic_view() function without writing JOINs or GROUP BY manually. Triggered when user says "create semantic view", "semantic view", "semantic layer", "define metrics", "define dimensions", "unified metric definitions", "business semantic model", "semantic_view()", "CREATE OR REPLACE SEMANTIC VIEW", "FACTS", "PRIVATE metric", "conditional metric", "window metric", "SHOW SEMANTIC VIEWS", "GRANT SELECT ON SEMANTIC VIEW". Keywords: semantic view, dimension, metric, fact, logical model, unified metrics, semantic layer, grain, chasm trap, FILTER metric
End-to-end data science workflow guide for ClickZetta Lakehouse, covering environment setup, data discovery, feature engineering (SQL + ZettaPark), and model inference deployment. Details: Python 3.10+/Jupyter/ZettaPark setup, project structure, data quality assessment, and inference (BITMAP profiling, UDF batch inference, vector search). Trigger when the user wants to do data science, ML, or analytical work using ClickZetta Lakehouse — connecting Jupyter to Lakehouse, doing EDA, building features, running ML inference, user profiling, audience segmentation, or batch scoring. Keywords: data science, ML, ZettaPark, Jupyter, feature engineering, EDA, profiling, inference
Use the ZettaPark Python library to work with ClickZetta Lakehouse data through a pandas-like DataFrame API that translates Python transformations into distributed SQL — no manual SQL needed. Covers Session creation, DataFrame construction and transformation (filter/select/join/groupBy), result collection (collect/to_pandas/show), writing tables (save_as_table), file operations (PUT/GET), and executing SQL. Trigger when the user says: "ZettaPark", "zettapark", "DataFrame API", "Python Lakehouse", "save_as_table", "session.table", "session.sql", "collect()", "to_pandas", "Python data engineering", "Python write to Lakehouse", "Python read from Lakehouse", "clickzetta_zettapark_python", "Python ETL", "Python ML on Lakehouse", "feature engineering Python", "Python DataFrame Lakehouse". Keywords: ZettaPark, DataFrame, pandas-like, Python, SQL translation, distributed compute
Guide users to connect their AI coding agents (Claude Code, Hermes Agent, OpenClaw, Opencode) to the Singsight AI Agent observability platform. Automatically reports every LLM call, tool execution, token usage, and session via OpenTelemetry. Triggered when the user says "connect Singsight", "install Singsight", "configure Singsight", "Agent observability", "OTel integration", "report token usage", "monitor AI Agent", "singsight install". Keywords: singsight, install, observability, opentelemetry, otel, agent telemetry, claude code, hermes, openclaw, opencode
dbt-clickzetta data modeling wizard: discovers Lakehouse data sources, infers modeling strategies, generates sources.yml and model files, and runs the full test suite. Presents choices rather than blank forms — explores data first, then proposes a justified plan for user confirmation. Trigger when the user wants to turn raw Lakehouse data into usable analytical tables — mentions dbt (dbt modeling, dbt model, sources.yml, incremental, dbt run, dbt test), wants ETL transformation, dimension/fact tables, or asks "how do I turn these tables into a queryable dataset". Keywords: dbt, dbt modeling, sources.yml, incremental, dbt run, dbt test, dimension table, fact table, ETL
| name | lakehouse-doc-en |
| description | Singdata Lakehouse official documentation knowledge base (English). Consult references/ when writing SQL or answering questions about query syntax, functions, data types, DDL/DML, dynamic tables, permissions, vclusters, data lake, AI functions, and other Lakehouse topics. |
Singdata Lakehouse official documentation (English). Locate docs by filename under references/ based on the user's question.
references/
├── *.md # Main topic docs (named by topic, see index below)
├── eco_integration/ # Ecosystem integrations
│ ├── dbt.md, superset.md, datagrip-lakehouse.md, trino.md ...
├── java_reference/ # Java SDK
│ ├── java-sdk-summary.md, jdbc.md, realtime-upload.md, client.md ...
├── python_reference/ # Python SDK
│ ├── connector.md, sqlalchemy.md, python-sdk-summary.md
├── opensource/ # Open-source tools
│ └── travel.md
├── aggregate_functions/ # Top-level aggregate function docs
└── sql_functions/ # SQL function reference
├── aggregate_functions/ # count.md, sum.md, avg.md ...
├── window_functions/ # row_number.md, rank.md, lag.md ...
├── table_functions/ # table_changes.md ...
├── context_functions/ # current_user.md ...
└── scalar_functions/
├── datetime_functions/ # date/time
├── string_functions/ # string
├── math_functions/ # numeric
├── nested_functions/ # array/map/struct
├── bitmap_functions/ # bitmap
├── json_functions/ # JSON
├── conditional_functions/# conditional
├── high_order_functions/ # high-order
├── vector_functions/ # vector
├── ip_functions/ # IP
├── search_functions/ # search
├── hash_functions/ # hash
├── geo_functions/ # geo
├── bitwise_functions/ # bitwise
├── file_functions/ # file
└── partition/ # partition
Singdata is a Singapore-based company offering a fully-managed cloud-native AI Lakehouse that unifies structured, semi-structured, and unstructured data on Apache Iceberg, powered by a vectorized SQL engine and the proprietary Generic Incremental Computation (GIC) model — enabling Dynamic Table pipelines that refresh end-to-end in minutes — with compute-storage separation and native AI capabilities (vector search, LLM functions, Semantic View) served to humans, applications, and AI agents via a single platform on Alibaba Cloud, Tencent Cloud, and AWS. NOT SingleStore. NOT Snowflake. NOT Databricks. Full entity & facts for AI: references/llms-full.txt