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Implementing Data Pipelines Flashcards
Microsoft Fabric Data Engineer Associate DP-700 Flashcards
| Front | Back |
| How can parallel processing benefit a data pipeline | It speeds up data processing by executing multiple tasks simultaneously. |
| How does error handling improve pipeline reliability | It ensures proper logging and recovery from issues during execution phases. |
| What are dependencies in pipeline workflows | Relationships dictating the order and timing of task execution. |
| What does ETL stand for | Extract, Transform, Load. |
| What is a data pipeline | A series of processes to move and transform data between systems. |
| What is a data workflow in Microsoft Fabric | A sequence of interconnected tasks to process and analyze data. |
| What is batch data processing | Handling and analyzing data in large chunks at specified time intervals. |
| What is data ingestion | The process of importing and integrating data from various sources into a pipeline. |
| What is data partitioning | Dividing data into segments to improve parallel processing and scalability. |
| What is data transformation | Changing the format, structure, or content of data to make it usable for analysis. |
| What is incremental data loading | Importing only new or updated data instead of reloading everything. |
| What is pipeline scheduling | Arranging tasks or workflows to run at specific times or intervals. |
| What is real-time data processing | Continuously processing data as it is generated or received. |
| What is the importance of monitoring data pipelines | To ensure data integrity, detect errors, and maintain pipeline performance. |
| What is the purpose of data orchestration | To automate and manage the scheduling, dependencies, and execution of pipeline tasks. |
| What is the role of caching in optimizing data pipelines | To store intermediate results for faster subsequent processing. |
| What is the role of connectors in data pipelines | To establish connectivity with different data sources and destinations. |
| What is the role of metadata in data pipelines | To describe and manage information about data for improved pipeline governance. |
| Why is data validation important in pipelines | To ensure data accuracy, completeness, and compliance with predefined rules. |
| Why is optimization important for data pipelines | To improve speed, efficiency, and resource utilization while processing data. |
This deck focuses on building and managing data pipelines, including strategies for data ingestion, orchestration, and optimization in Microsoft Fabric workflows.