data preprocessingdata pipelinedependency_management
When ETL processes become overly complex and start to dominate the data pipeline, it can lead to a 'land grab' where multiple teams and stakeholders vie for control over the data. This can result in inefficiencies, duplication of effort, and decreased data quality.
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When ETL processes become overly complex, it's essential to reassess the data pipeline architecture and simplify the workflow. One approach is to break down the ETL process into smaller, more manageable tasks, and leverage specialized tools for data ingestion, transformation, and loading. Additionally, implementing data governance policies and standards can help prevent the 'land grab' by ensuring that data is properly managed and accessible across teams.
Key Takeaways
Reassess and simplify ETL workflow
Leverage specialized tools for data pipeline tasks
Implement data governance policies and standards
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