「lakehouse」の検索結果

Expert guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.

日本語の概要は準備中です。原文の説明を表示しています。

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with Unity Catalog, Lakehouse/Lakeflow, Lakebase, AI Runtime/model serving, or external connectors, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

日本語の概要は準備中です。原文の説明を表示しています。

Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns

日本語の概要は準備中です。原文の説明を表示しています。

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

日本語の概要は準備中です。原文の説明を表示しています。

Convert construction data to/from Parquet format. Optimize storage, enable fast queries, and integrate with data lakehouses.

日本語の概要は準備中です。原文の説明を表示しています。

Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Routes AWS SDK for pandas (awswrangler) workflows across S3 lakehouse data, Athena/Glue catalogs, SQL connectors, runtime configuration, and AWS service integrations.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Data Engineer. Use when a task calls for Data Engineer judgment. Reasons from idempotent ELT, medallion bronze/silver/gold, Kimball grain and SCD2, CDC/Debezium and watermark incremental loads, dbt/GX quality gates, Airflow/Dagster orchestration, Iceberg/Delta lakehouse MERGE, data contracts and freshness SLIs while treating silent join drops, duplicate amplification, schema drift, and green-DAG-wrong-numbers as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Designs lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.

日本語の概要は準備中です。原文の説明を表示しています。

Airbyte is the leading open-source data integration platform providing 600+ pre-built connectors for ELT pipelines from APIs, databases, and files to data warehouses, lakes, and lakehouses. It supports both self-hosted and cloud deployments with a no-code connector builder.

日本語の概要は準備中です。原文の説明を表示しています。

S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).

日本語の概要は準備中です。原文の説明を表示しています。

Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en data lakes (S3, Delta Lake, Iceberg, schema evolution, governance)

日本語の概要は準備中です。原文の説明を表示しています。

S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).

日本語の概要は準備中です。原文の説明を表示しています。