ETL & Data Integration Flashcards

What does ETL stand for in data processing?

Answer: Extract, Transform, Load

ETL stands for Extract, Transform, Load, which is a fundamental data integration process. It involves extracting data from various source systems, transforming it into a clean and consistent format, and then loading it into a target system like a data warehouse or lakehouse for analysis.

Why is ETL critical in a lakehouse environment?

Answer: To prepare data for analytics

ETL is critical in a lakehouse environment because it prepares raw data from diverse sources for analytical consumption. By cleansing, transforming, and structuring the data, ETL ensures it is consistent, accurate, and optimized for performance, enabling reliable business intelligence, data science, and machine learning applications.

Which tool is often used for data orchestration in lakehouse platforms?

Answer: Apache Airflow

Apache Airflow is a widely used open-source platform for programmatically authoring, scheduling, and monitoring complex data pipelines. In lakehouse platforms, it is frequently employed for data orchestration to manage ETL/ELT workflows, ensuring that data ingestion, transformation, and loading processes run reliably and on schedule.

How does ELT differ from ETL in modern architectures?

Answer: Loads before transforming

ELT (Extract, Load, Transform) differs from ETL by loading raw data directly into the target system (like a data lake or lakehouse) before any significant transformations occur. This approach leverages the scalable processing power of modern data platforms for transformations, making it particularly efficient for large datasets and flexible for evolving analytical needs.

Which type of data integration supports real-time processing in a lakehouse?

Answer: Streaming integration

Streaming integration supports real-time processing in a lakehouse by continuously ingesting and processing data as it is generated, rather than in batches. This enables immediate insights and reactions to events, which is crucial for applications requiring up-to-the-minute data, such as fraud detection or real-time dashboards.

What ensures data quality during the ETL process?

Answer: Performing validation and cleansing

Data quality during the ETL process is primarily ensured by performing validation and cleansing steps. Validation checks data against predefined rules for accuracy and consistency, while cleansing corrects or removes erroneous, incomplete, or duplicate data, ensuring that only high-quality data is loaded for analysis.