'sql' 검색 결과

Plan and manage Cloud Spanner databases on Google Cloud, design tables and primary keys, write SQL and client code, and investigate slow queries using database statistics to identify resource-intensive operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create Cloud Spanner instances and databases
• Design primary keys and related tables
• Write SQL queries and client library code

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Manage BigQuery datasets, tables, views, and jobs, and run SQL queries for basic data analysis. Provides setup examples and references for data ingestion, command-line operations, client libraries, access permissions, and infrastructure configuration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create BigQuery datasets and tables
• Search and aggregate data with SQL
• Load data and manage processing jobs

Create and administer AlloyDB for PostgreSQL databases, including clusters, instances, backups, secure connections, and access permissions. It also covers command-line operations, infrastructure configuration, and MCP tools that let agents automate database tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an AlloyDB cluster and primary instance
• Manage backups and plan recovery
• Configure secure connections and access permissions

Guide database structure changes in a project using Drizzle and node-pg-migrate. Covers generating and reviewing migration files, checking compatibility during deployment, handling indexes separately, and validating changes before applying them to shared databases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding or changing database tables and columns
• Creating or removing indexes on existing tables
• Reviewing compatibility during staged schema changes

Route code changes and SQL analysis to isolated Kortix work sessions with clear requirements and workspace details. Coordinate parallel investigations or independent pull request reviews, then summarize deliverables, verification results, and differences between findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegate feature implementation or bug fixes to a separate session
• Compare two agents' approaches to a refactor
• Combine findings from two independent pull request reviews

Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add SQL benchmarks for DataFusion operators
• Select representative input sizes and filter conditions
• Reduce unrelated processing overhead in measurements

Advises on Amazon RDS open-source engines (MySQL, MariaDB, PostgreSQL) for instance creation, upgrade planning, commitment pricing, proxy evaluation, and Blue/Green deployments. Handles any RDS MySQL, MariaDB, or PostgreSQL question, including create a production-ready RDS MySQL instance, provision an RDS PostgreSQL database, run the RDS upgrade advisor for my RDS MySQL instance, what are my upgrade options, upgrade RDS MariaDB from 10.6 to the latest version, should I buy reserved instances or a savings plan for db.r7g.2xlarge RDS MySQL, change a VARCHAR to INT column on RDS MySQL 8.0 with Blue/Green, and does RDS Proxy help when PgBouncer already runs in transaction mode. Covers instance creation with production best practices, describe-db-instances and describe-db-engine-versions upgrade-target workflow, live prechecks via SSM or direct connection, RI versus DSP commitment pricing, RDS Proxy versus PgBouncer, and Blue/Green lifecycle with binlog replay compatibility.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge for Azure Database Migration Service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when planning Azure DMS migrations for MySQL, PostgreSQL, MongoDB, SSIS-to-Azure SQL, or hybrid workloads, and other Azure Database Migration Service related development tasks. Not for Azure Migrate (use azure-migrate), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect and exploit SQL injection vulnerabilities using both manual techniques and automated tools. Use this skill when testing web applications for database injection flaws including UNION-based, error-based, blind boolean, blind time-based, and out-of-band SQL injection. Covers WAF bypass, second-order SQLi, authentication bypass, and full database extraction with sqlmap.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create a MySQL CDC capture using flowctl with binlog replication. Use when setting up streaming from MySQL, Amazon RDS MySQL, or Aurora MySQL. Use when user says "capture MySQL", "stream from MySQL", "MySQL CDC", "binlog replication", or "connect MySQL to Estuary".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Handle system backups and restore data from backup files while managing a SQLite database. The skill also covers data export, import, and incremental snapshots, which capture changes since an earlier saved state.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a system backup
• Restore data from a backup file
• Export or import SQLite data

Turn defects found during Oracle-to-PostgreSQL migration into structured bug reports. Document expected and actual behavior, reproduction steps, severity, root causes, proposed changes, and validation on both databases so teams can act on the findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting behavioral differences found during migration
• Organizing reproduction steps and test data
• Recording fixes, affected files, and validation steps

Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

Design and build backend services with API scaffolding, database migrations, authentication reviews, and load testing. Generate route handlers from OpenAPI specifications, identify PostgreSQL optimization opportunities, and compare architecture choices against traffic, data sensitivity, and reliability requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating API route handlers from OpenAPI
• Investigating slow PostgreSQL queries
• Reviewing authentication and input validation

Design an enterprise search system that answers questions using retrieved company content, an approach called RAG. Gather requirements and produce architecture diagrams, deployment guidance, and validation plans using GKE, AlloyDB, and open models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing conversational search for internal documents
• Planning RAG with a vector-enabled SQL database
• Hosting open models on GKE

Convert Snowflake dbt SQL models to BigQuery through a bulk migration workflow. Preserve Jinja template logic while adapting configuration, explicit type conversions, JSON access, and deduplication patterns, with translated files saved in their directory structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating Snowflake dbt models to BigQuery in bulk
• Preserving dbt references and incremental processing logic
• Adapting JSON, array, and STRUCT access during migration

Answer business data questions with Wren CLI by checking model definitions, reusing past queries, and executing SQL or predefined aggregations. The workflow also covers query verification, error diagnosis, and saving successful queries for future use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyze monthly revenue trends
• Rank customers by purchase value
• Compare metrics across plans or regions

Use Quicknode to connect applications to blockchains, retrieve wallet and asset data, and monitor activity. Covers event notifications, live data streams, historical backfills, and SQL queries, with guidance for choosing suitable services and access methods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting an app to blockchain data
• Setting up transaction and event notifications
• Recovering missing historical stream data

Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manages a Fabric SQL database item, the OLTP SQL Server engine, including running T-SQL through sqlcmd, temporal and vector similarity search, schema and sys.tables inspection, dacpac deployment, and Query Store, blocked sessions and regressed-plan investigation. Use for any query or change against a SQL database item. Warehouse, lakehouse SQL endpoint and mirrored items belong to sqldw-cli.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge for SQL Server on Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning SQL VM vs Managed Instance, Always On/FCI HADR, Azure storage/backups, Key Vault/Entra auth, or KQL telemetry, and other SQL Server on Azure Virtual Machines related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines), Azure Data Science Virtual Machines (use azure-data-science-vm).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.