Why is a "workflow succeeded" status an insufficient signal for determining if a batch data pipeline is truly "healthy"?
この演習はコースの一部です
理論を実践に変える、インタラクティブな演習のひとつをお試しください
You will learn the critical role of a data engineer in developing and maintaining batch data pipelines, understand their core components and lifecycle, and analyze common challenges in batch data processing. You'll also identify key Google Cloud services that address these challenges.
You will design scalable batch data pipelines for high-volume data ingestion and transformation. You'll also optimize batch jobs for high throughput and cost-efficiency using various resource management and performance tuning techniques.
You will develop data validation rules and cleansing logic to ensure data quality within batch pipelines. You'll also implement strategies for managing schema evolution and performing data deduplication in large datasets.
You will orchestrate complex batch data pipeline workflows for efficient scheduling and lineage tracking. You'll also implement robust error handling, monitoring, and observability for batch data pipelines.
現在の演習
