GitHub
neon-postgres
Neon PostgreSQL serverless database - connection pooling, branching, serverless driver, and optimization. Use when deploying to Neon or building serverless applications.
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tools data engineering AI Skills
Discover tools data engineering skills, compare source repositories and read the instructions for each workflow.
Categories: tools data engineering
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GitHub
opensolve-pipe-data-model
OpenSolve Pipe component chain data model conventions. Use when creating or modifying components, piping segments, or project structures.
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GitHub
dbt-patterns
Comprehensive guide to dbt (data build tool) patterns, modeling best practices, testing strategies, and production workflows for modern data transformation
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GitHub
data-lineage
Mapping the flow of data from source to destination for transparency, impact analysis, and troubleshooting.
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GitHub
kafka-streaming
Comprehensive guide to Apache Kafka for real-time data streaming including topics, producers, consumers, stream processing, and production best practices
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GitHub
data-quality-checks
Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines
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GitHub
data-freshness-and-latency
Monitoring and optimizing how quickly data flows through pipelines and ensuring it meets timeliness requirements.
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GitHub
data-migrations-backfill
Zero-downtime patterns for production schema changes and large backfills, including expand/contract, batching, verification, and rollback strategies
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GitHub
database-health-monitoring
Continuous monitoring and diagnostics for ensuring high availability, performance, and stability of database systems.
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GitHub
schema-drift-handling
See the main Schema Drift Detection skill for comprehensive coverage of schema drift detection and management.
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GitHub
data-quality-monitoring
Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.
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GitHub
schema-management
Strategies for managing data structures, versioning, and evolutions across databases and event streams.
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GitHub
lineage-and-provenance
See the main Data Lineage skill for comprehensive coverage of data lineage tracking and provenance.
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GitHub
data-quality-checks-and-validation
Implementing comprehensive data quality checks across the data pipeline to ensure accuracy, completeness, and reliability.
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GitHub
ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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GitHub
schema-drift-detection
Detecting and managing schema changes that can break data pipelines and downstream consumers.
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GitHub
backfill-and-reconciliation-playbook
Playbook สำหรับ backfill historical data และ reconcile inconsistencies ระหว่าง data sources
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GitHub
ai-data-privacy
Protecting personal and sensitive data throughout the machine learning lifecycle, from training to inference.
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GitHub
data-quality-rules
See the main Data Validation Rules skill for comprehensive coverage of data quality rule implementation.
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GitHub
retention-and-deletion
Comprehensive guide to data retention policies, deletion workflows, and implementing GDPR/CCPA right to erasure across distributed systems.
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GitHub
lakehouse-patterns
Comprehensive guide to data lakehouse architecture combining data lake flexibility with data warehouse performance using Delta Lake, Iceberg, and Hudi
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GitHub
data-validation-rules
Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.
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GitHub
elt-modeling
Comprehensive guide to ELT (Extract, Load, Transform) modeling patterns, dimensional modeling, fact and dimension tables, and data warehouse design
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GitHub
data-incident-response
Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.
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The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.