93 件 ・ 関連度順
概要と使いどころ
ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.
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。
数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N
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 running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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 ClickHouse (columnar OLAP, MergeTree, materialized views, real-time analytics)
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 running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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。
Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical 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。
Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.
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。
Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics
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针对包含Bitmap类型字段的快照表，利用位图差集函数计算每日新增和流失人数，忽略ID字段。
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。
Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical 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。
Suede-owned sales-enablement discipline. Use when creating pitch decks, one-pagers, objection guides, demo scripts, talk tracks, battle cards, proposal structures, buyer cards, or deal-level ROI framing. NOT FOR: setting price or terms (use suede-pricing), building the offer (use suede-offers), sending outreach (use suede-cold-email), or publishing unsupported proof.
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。
Suede-affiliated in-product monetization design for paywall screens, feature gates, trial-expiry states, usage-limit prompts, and free-to-paid upgrade moments. Use when the user needs trigger timing, message structure, plan presentation, or experiment design after users have experienced value. NOT FOR: public pricing pages (use suede-site-alchemy), tier architecture (use suede-pricing), or cancellation and save flows (use suede-churn-prevention).
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。
Suede AI umbrella router for the public Suede pack: selects the right specialist across copy, design, code review and grading, SEO and visibility, launch packaging, MCP QA, iOS and Android shipping, growth, and creator rights work. Use when a request crosses two or more Suede lanes, when the user wants the full public skill pack loaded from one installable path, when they ask which Suede skill fits a task, or when only this umbrella skill is installed. NOT FOR: a request that names one lane and can go straight to its specialist; a multi-file repo change that should be built and reviewed as one DAG (use suede-graph-flo-xr).
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。
Design, review, and visually QA web and app interfaces against explicit design laws so shipped screens look intentional instead of generic AI output.
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。
Suede-owned product-marketing context discipline. Use when a project needs a shared record of product, audience, ICP, positioning, objections, customer language, proof, and goals, or when that record needs updating. NOT FOR: writing a campaign (use suede-campaign-in-a-box), conducting new customer interviews (use suede-customer-research), or publishing brand claims.
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。
Suede Labs skill that turns messy creator materials into a local, offline rights-and-provenance transfer package: inventoried and hashed assets, a normalized suede-intake.json manifest, credits and splits, license notes, provenance, and a missing-information report, validated by a bundled stdlib script. Use when a creator needs to hand a song, release, or project to a collaborator, advisor, registry, marketplace, or label; when someone asks for a rights package, intake package, or handoff folder; or when a validated manifest is needed before licensing or royalty-routing review. Carries questions, not answers: building the package clears nothing and uploads nothing. NOT FOR: finding or investigating the rights gaps in the first place (use suede-rights-audit); linting a release folder's files and metadata (use suede-release-linter); a sync one-sheet (use suede-sync-packaging).
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。
Suede-owned prospecting and qualification discipline. Use when defining an ICP, sourcing and enriching a bounded lead list, finding early adopters or design partners, scoring account fit, or documenting disqualification evidence. NOT FOR: sending outreach (use suede-cold-email), changing CRM routing (use suede-revops), or profiling competitors instead of prospects (use suede-competitor-profiling).
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。
Suede-owned SEO and generative-search audit discipline. Runs a nine-lane evidence-based audit: access, intent, metadata, structure, supported schema, E-E-A-T, clusters, and exact rewrite fixes. Use when a page or site needs a deep standalone SEO/AI-visibility audit with scored lanes. NOT FOR: a fast A-F launch-appeal grade (use suede-visibility-grader); conversion-path rework after the audit (use suede-site-alchemy); getting cited by ChatGPT/Perplexity/AI Overviews (use suede-ai-seo).
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。
Suede-owned launch-appeal grader for public pages. Grades findability, first-screen clarity, CTA pull, proof quality, design signal, and AI citation readiness. Use when a public page needs a fast A-F visibility grade before or after launch. NOT FOR: the deep nine-lane SEO audit with rewrite fixes (use suede-seo-audit); implementing conversion changes (use suede-site-alchemy); getting cited by AI answer engines (use suede-ai-seo).
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。
Suede-owned marketing video planning and production discipline. Use when choosing a format, scripting, storyboarding, generating, editing, reverse-engineering pacing, building a repeatable video pipeline, or repurposing one source into multiple cuts. NOT FOR: deciding the social calendar (use suede-social), paid ad concepts (use suede-ad-creative), image-only assets (use suede-image), or publishing unapproved media.
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。
Context-aware line edit that strips generic AI writing patterns without flattening the author's voice, plus a findings-only audit mode that names issues instead of rewriting. Applies contextual rules rather than blanket bans on adverbs, passive voice, and punctuation, and locks facts, numbers, quotes, code, links, and the supplied house style before it touches a sentence. Use before copy, a README, an email, a social post, or a doc ships; after a long assisted-writing session; when the text sounds fine but feels generated; or when the user asks for a findings-only slop audit. NOT FOR: writing new copy from scratch; deciding whether a person or a model wrote text; changing or certifying facts, which must be checked against primary evidence before publication.
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。
Turns a website, PWA, dashboard, or marketplace into an iOS app. Use when the user has a live site or web app and asks to put it on the App Store, wrap it in an app, ship an iOS version, or convert a PWA to native. Covers URL audit, shell-vs-native strategy, App Store Guideline 4.2 wrapper-rejection risk, native value requirements, screenshots, metadata, privacy answers, and the release gate. Not for: building a native iOS app when no website exists; repairing or releasing an existing Capacitor shell; Android conversions; or keyword and listing audits on an app that already shipped.
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。
Suede-owned SMS and MMS marketing discipline. Use when evaluating SMS as a channel, designing consent-aware sequences, drafting messages, setting cadence, comparing number types or platforms, or defining measurement (TCPA consent, A2P 10DLC, quiet hours, cart-recovery and win-back texts. NOT FOR: email campaigns (use suede-emails), phone capture UX (use suede-site-alchemy), or legal certification) this skill does not give legal advice and never sends, schedules, imports, or registers.
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。
Suede-owned conversion-path discipline for pages. Turns a page into a conversion path: hero, friction, proof, CTA, pricing, A/B ideas, quick wins, and mobile CRO. Use when a live or drafted page needs conversion-rate work on its structure and persuasion path. NOT FOR: a scored SEO/AI-visibility audit (use suede-seo-audit); a fast launch-appeal grade (use suede-visibility-grader); writing the copy itself (use suede-copy).
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。