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