GitHub
opensolve-pipe-data-model
OpenSolve Pipe component chain data model conventions. Use when creating or modifying components, piping segments, or project structures.
Skills IA devops data engineering
Découvrez les skills devops data engineering, comparez leurs dépôts et consultez leurs instructions.
Catégories: devops data engineering
devops data engineering Effacer les filtres
Explorer
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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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data-lineage
Mapping the flow of data from source to destination for transparency, impact analysis, and troubleshooting.
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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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data-quality-checks
Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines
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data-freshness-and-latency
Monitoring and optimizing how quickly data flows through pipelines and ensuring it meets timeliness requirements.
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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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database-health-monitoring
Continuous monitoring and diagnostics for ensuring high availability, performance, and stability of database systems.
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schema-drift-handling
See the main Schema Drift Detection skill for comprehensive coverage of schema drift detection and management.
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data-quality-monitoring
Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.
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schema-management
Strategies for managing data structures, versioning, and evolutions across databases and event streams.
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lineage-and-provenance
See the main Data Lineage skill for comprehensive coverage of data lineage tracking and provenance.
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data-quality-checks-and-validation
Implementing comprehensive data quality checks across the data pipeline to ensure accuracy, completeness, and reliability.
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ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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schema-drift-detection
Detecting and managing schema changes that can break data pipelines and downstream consumers.
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backfill-and-reconciliation-playbook
Playbook สำหรับ backfill historical data และ reconcile inconsistencies ระหว่าง data sources
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ai-data-privacy
Protecting personal and sensitive data throughout the machine learning lifecycle, from training to inference.
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data-quality-rules
See the main Data Validation Rules skill for comprehensive coverage of data quality rule implementation.
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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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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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data-validation-rules
Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.
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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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data-incident-response
Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.
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freshness-latency-slos
See the main Data Freshness and Latency skill for comprehensive coverage of freshness monitoring and SLO tracking.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.