发现 tools data engineering 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Master Effect Schema composition patterns including Schema.compose vs Schema.pipe, transformations, filters, and validation. Use this skill when working with complex schema compositions, multi-step transformati...
Research and document write/create APIs of a source system to enable write-back testing functionality.
Validate that a connector's CDC/incremental sync implementation correctly tracks offsets and filters records.
Implement a Python connector that conforms to the LakeflowConnect interface for data ingestion.
Microsoft Fabric Lakehouse, OneLake, and Fabric Warehouse connectors for Azure Data Factory (2025)
T-SQL query optimization techniques for SQL Server and Azure SQL Database. Use this skill when: (1) User needs to optimize slow queries, (2) User asks about SARGability or index seeks, (3) User needs help with...
Azure DevOps pipeline best practices, patterns, and industry standards
Comprehensive Azure Data Factory knowledge base with official documentation sources, CI/CD methods, deployment patterns, and troubleshooting resources
Databricks Job activity and 2025 Azure Data Factory connectors
Salesforce Data Cloud integration patterns and architecture (2025)
Comprehensive Azure Data Factory validation rules, activity nesting limitations, linked service requirements, and edge-case handling guidance
Process and validate datasets for training. Use when setting up data pipelines.
Expert data engineer specializing in building scalable data pipelines, ETL/ELT processes, and data infrastructure. Masters big data technologies and cloud platforms with focus on reliable, efficient, and cost-o...
Expert database optimizer specializing in query optimization, performance tuning, and scalability across multiple database systems. Masters execution plan analysis, index strategies, and system-level optimizati...
Helps write Python code using the everyrow SDK for AI-powered data processing - transforming, deduping, merging, ranking, and screening dataframes with natural language instructions
PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and...
Guidance for using the TiDB Cloud Serverless Driver (Beta) in Node.js, serverless, and edge environments. Use when connecting to TiDB Cloud Starter/Essential over HTTP with @tidbcloud/serverless, or when integr...
Ecto patterns for Phoenix/Elixir apps. Covers schemas, changesets, migrations, queries, Ecto.Multi, transactions, constraints, associations, pagination, tenant partitioning, performance, and testing.
Safe patterns for evolving database schemas in production with decision trees and troubleshooting guidance.
Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
Neon serverless Postgres with autoscaling, instant database branching, and zero-downtime deployments. Use when building serverless applications, implementing database branching for dev/staging, or deploying wit...
Understand MIMIC-IV table relationships, join patterns, and identifier hierarchy. Use for correct data linkage, avoiding duplicates, and proper temporal joins.
Master AI-powered natural language data exploration with Lumen AI. Use this skill when building conversational data analysis interfaces, enabling natural language queries to databases, creating custom AI agents...
Master declarative, no-code data dashboards with Lumen YAML specifications. Use this skill when building standard data exploration dashboards, connecting multiple data sources (files, databases, APIs), creating...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。