SQLMesh data transformation framework — model kinds (FULL/INCREMENTAL_BY_TIME_RANGE/INCREMENTAL_BY_UNIQUE_KEY/SCD_TYPE_2/VIEW/SEED), plan/apply workflow, virtual environments, state-aware deploys, audits, unit tests, CI/CD, dbt migration
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 writing, optimizing, or maintaining Apache Iceberg tables with Trino — covering table DDL, all ALTER TABLE operations, partition transforms, bucketing, sorted tables, DML (INSERT/UPDATE/DELETE/MERGE), EXPLAIN plan reading, join optimization, statistics with ANALYZE, table maintenance (optimize/expire_snapshots/remove_orphan_files), schema evolution, time travel, and metadata table diagnostics.
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。
Prefect 3.x workflows — flows, tasks, deployments, work pools, event-driven triggers, caching, retries, state hooks, Prefect Cloud/server, Python SDK, Docker/K8s infrastructure
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 Contracts — datacontract.com YAML specification (schema, quality, SLA, ownership, servers, changelog), Data Contract CLI (init/test/lint/diff/breaking/publish/export), SodaCL-style quality checks embedded in contract, breaking change detection (column removal/type change/constraint tightening), CI/CD GitHub Actions PR enforcement, Kafka/S3/database server definitions, contract-driven development (producer-first), semver versioning and deprecation, DataHub and OpenMetadata catalog publishing
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 or implementing Data Vault 2.0 data models — Hubs, Links, Satellites, Reference Tables, Same-As Links, Multi-Active Satellites, Effectivity Satellites, Point-in-Time tables, Bridge tables, Business Vault patterns, hash key generation, DDL for all entity types, insert-only DML load patterns, pipeline architecture, and constructing Information Marts from the vault.
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 writing, configuring, or optimizing dbt projects targeting Trino or Starburst — covering profiles.yml setup, all authentication methods, materializations (table/view/incremental/materialized_view/ephemeral), incremental strategies (append/merge/delete+insert), table properties (format/partitioning/sorted_by), on_schema_change, seeds, snapshots, grants, session properties, data modeling patterns (Kimball/staging/intermediate/mart), dbt project structure, tests, and CI/CD.
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。
DE blameless postmortem — incident timeline reconstruction, impact quantification, root cause chain (5 Whys), corrective actions (CAPA), postmortem document template, severity classification, SLA breach reporting, communication templates, lessons-learned facilitation, postmortem review checklist
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。
DuckDB in-process OLAP analytics — reading Parquet/CSV/JSON/Iceberg/Delta from S3/local, SQL (window functions, PIVOT, ASOF join, QUALIFY), Python API (duckdb.connect, fetchdf, register, UDFs), extensions (httpfs/iceberg/delta/postgres), performance tuning, COPY TO, persistent DB
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。
Great Expectations (GX) — DataContext setup, Data Sources (Pandas/Spark/SQL/file), Expectation Suites, built-in expectations (null/uniqueness/range/set/regex/table-level/statistical), Validation Definitions, Checkpoints, Data Docs, Airflow integration, custom expectations, dbt integration, CI/CD usage, severity levels (warning/critical)
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。
Mage AI data pipelines — block types (data loader/transformer/exporter/sensor/custom), pipeline YAML, hybrid SQL+Python blocks, triggers (schedule/event/API), streaming pipelines, dbt integration, Spark integration, Docker/Kubernetes deployment, io_config.yaml, pipeline variables, callbacks, backfills
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。
OpenLineage data lineage tracking — RunEvent/Job/Dataset/facet spec, Marquez backend setup, Airflow/Spark/dbt integrations, column-level lineage, custom emitters, lineage-based impact 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。
PostgreSQL for data engineering — declarative partitioning (RANGE/LIST/HASH/pg_partman), index types (B-Tree/BRIN/GIN/GIST/partial/covering), COPY bulk load, EXPLAIN ANALYZE plan reading, autovacuum tuning, window functions, JSONB, CTEs, LATERAL joins, and bulk-load patterns (UNLOGGED/pg_bulkload)
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, implementing, reviewing, or optimizing production PySpark ETL/DataFrame pipelines at GB-TB+ scale, including schemas, joins, partitioning, window functions, writes, UDF avoidance, and Spark performance diagnostics.
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。
MLflow for data engineering — tracking server setup (PostgreSQL backend + S3 artifacts), experiment tracking (params/metrics/artifacts/autolog), ETL job metadata logging (row counts, DQ metrics, lineage tags), Model Registry (register/alias/promote), model serving (pyfunc/REST API/batch scoring), MLproject files, Airflow integration (MLflowClientHook/XCom/model promotion), Spark integration (mlflow.spark.autolog/log_model/Delta metadata)
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。
DE cost optimization — query cost analysis (Trino/Spark/ClickHouse/BigQuery), compute right-sizing, storage tier recommendations, spot/preemptible instances, partition pruning, Z-order/clustering, materialized view economics, data lifecycle policies, FinOps tagging, cost attribution, budget alerts
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。
DE architecture decision records (ADR) — ADR template, trade-off analysis framework, technology evaluation criteria, common DE architecture decisions (storage format, orchestrator, streaming vs batch, lakehouse vs warehouse), decision reversibility, stakeholder communication
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。
AIOps infrastructure anomaly detection — statistical baseline (z-score/IQR/seasonal decomposition), Prometheus anomaly rules (predict_linear/stddev_over_time/MAD), ML-based detection (Isolation Forest/Prophet/LSTM autoencoder on metrics), metric correlation clustering, automated alert threshold tuning from historical data, Kubernetes resource anomaly detection, data pipeline anomaly signals (row count z-score/freshness drift/throughput drop), Python sklearn/Prophet integration, Grafana anomaly panel
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。
AIOps capacity planning agent — predictive resource forecasting (Prophet/linear regression on Prometheus metrics), Kubernetes HPA/VPA right-sizing recommendations, node capacity headroom calculator, Kafka partition and broker capacity model, Spark executor sizing from job history, data lake storage growth forecast (S3/GCS), cluster autoscaler simulation, automatic HPA threshold tuning, cost-per-job attribution, capacity planning report generator
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。
AIOps autonomous incident response agent — LLM-driven diagnosis loop (Claude tool-use agent with kubectl/SQL/Prometheus tools), alert-to-action pipeline (PagerDuty webhook → agent trigger), automated RCA generation (failure taxonomy + log correlation), self-healing action executor with approval gate, incident severity classification, runbook automation (structured YAML runbooks executed by agent), escalation logic, incident timeline auto-generation, Slack integration for human-in-the-loop approvals
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 writing, reviewing, debugging, or optimizing SQL for Vertica — covering DDL (CREATE/ALTER/DROP TABLE, columns, projections, segmentation, partitions), DML (INSERT, UPDATE, DELETE, MERGE, TRUNCATE, COPY), CRUD patterns, and Vertica-specific performance guidance including encoding, segmentation keys, partition pruning, and query optimization.
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。
dbt Jinja macros — macro authoring fundamentals, Jinja syntax (blocks/filters/tests), context variables (this/target/adapter/execute), run_query, adapter.dispatch for cross-database macros, generate_schema_name, hooks, custom materializations, dbt-utils 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。
Use when optimizing, diagnosing, or reviewing Vertica 11.x SQL query performance — covering EXPLAIN plan reading, projection design for predicates/joins/GROUP BY/ORDER BY/analytic functions, segmentation strategies, column encoding, RLE, sort elimination, Top-K, INSERT-SELECT tuning, DELETE/UPDATE internals, and Data Collector diagnostics.
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。
ClickHouse OLAP — MergeTree family engines (MergeTree/ReplacingMergeTree/AggregatingMergeTree/SummingMergeTree/CollapsingMergeTree), ORDER BY/PARTITION BY/PRIMARY KEY design, data skipping indexes (minmax/set/bloom_filter), TTL tiered storage, materialized views, projections, LowCardinality/Nullable/AggregateFunction types, INSERT batching, FINAL keyword, query optimization (PREWHERE/parallel_replicas), dictionaries, Kafka engine, Python clickhouse-connect, best practices
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。
Change Data Capture with Debezium — PostgreSQL/MySQL/Oracle CDC connectors, change event structure (before/after/op/source), snapshot modes, Kafka Connect deployment, SMT transformations, outbox pattern, Iceberg/Delta sink integration, idempotency guarantees
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。