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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devops data engineering AI Skills
Discover devops data engineering skills, compare source repositories and read the instructions for each workflow.
Categories: devops data engineering
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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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GitHub
freshness-latency-slos
See the main Data Freshness and Latency skill for comprehensive coverage of freshness monitoring and SLO tracking.
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