'warehouse' 검색 결과

Connect MATLAB to Databricks via Spark (Databricks Connect) or JDBC (Database Toolbox). Use when setting up the MATLAB Interface for Databricks, configuring authentication (OauthU2M, OauthM2M, PAT), creating Spark sessions with getDatabricksSession(), reading Unity Catalog tables with server-side filtering, creating JDBC connections with databricks.JDBCConnection or StandaloneJDBCConnection, connecting to SQL Warehouses, selecting JDBC drivers (Simba/OSS), or writing data back to Databricks. Triggers on: Databricks Connect, Spark from MATLAB, getDatabricksSession, .databrickscfg, databricks.JDBCConnection, StandaloneJDBCConnection, SQLWarehouse, Databricks JDBC, Databricks cluster, large table server-side filtering.

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Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.

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Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.

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Run SQL against Snowflake — submit statements, poll async handles, fetch result partitions, cancel, and browse warehouses/databases/schemas/tables. Use this whenever the user wants to query Snowflake, ask "what tables are in this schema", check a warehouse's status, or mentions `snowflakecomputing.com`, `/api/v2/statements`, or a Snowflake account identifier (like `xy12345.us-east-1`). Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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应用从 AI for Everyone 蒸馏出的「统一数据仓库 — AI的数据基础设施」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to unified-data-warehouse.

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Use when a human-curated dbt model index must guide BigQuery SQL for a warehouse question. Not for discovering undocumented models or executing warehouse changes.

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Optimize and stress-test a logistics system end-to-end — chains route optimization, warehouse operations review, inventory demand forecasting, supply chain risk analysis, and peak-load testing. Use for delivery platforms, warehouse management systems, fleet routing, or supply chain software.

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Audit warehouse management system operations -- evaluate WMS platform architecture (Manhattan, Blue Yonder, SAP EWM, Korber, Infor), inbound receiving and ASN processing, putaway rules and cross-docking logic, picking strategy selection (discrete, batch, zone, wave, cluster.

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Audit warehouse flow design and material movement -- evaluate pick path optimization algorithms, ABC velocity-based slotting compliance, zone configuration and boundary balancing, conveyor merge point and sortation system capacity, wave planning logic.

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dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).

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Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

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Expert en data warehousing (Snowflake, BigQuery, Redshift, star schema, slowly changing dimensions)

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Extract structured fields from warehouse receipt documents.

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dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).

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Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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대량 데이터 수집, 과거 데이터 보충, 변환, 동기화의 지연을 단계별로 조사합니다. 처리 방식을 비교해 속도를 높이고 행 수, 파일 수, 시각의 일치 여부를 확인합니다.

• 대량 데이터 수집과 내보내기 가속
• 과거 데이터 보충 시간 단축
• 배치 크기와 병렬 작업 수 비교

Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.

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• Importing DNA sequences from FASTA files
• Auditing sample inventory at a storage location
• Exporting sequences for a specific schema to CSV

Use sales history and current stock to estimate stockout dates, reorder timing, quantities, and costs. Identify cash tied up in slow-moving inventory, and prepare purchase order and supplier email drafts for approval.

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• Deciding what to reorder this month
• Checking whether stock will last until delivery
• Finding cash tied up in slow-moving stock