How does Extraction-Transformation-Loading (ETL) differ from Extract-Load-Transform (ELT) in terms of data extraction?
- Data is extracted from the target system back to the source system
- Data is extracted in real-time from the source system
- Data is loaded into the target system before transformation
- Data is transformed before loading into the target system
ETL involves extracting data, then transforming it, and finally loading it into the target system, whereas ELT involves extracting data first, then loading it into the target system, and finally transforming it.
What is a broadcast variable in Apache Spark, and how is it used?
- A variable cached in memory for faster access
- A variable replicated to every executor node
- A variable shared across all nodes in a cluster
- A variable used for inter-process communication
A broadcast variable in Apache Spark is replicated to every executor node for efficient data distribution. It's used for broadcasting large read-only datasets to all tasks across the cluster to avoid excessive data shuffling.
Data modeling tools such as ERWin or Visio help in visualizing and designing ________.
- Data Flow Diagrams (DFDs)
- Entity-Relationship Diagrams (ERDs)
- Flowcharts
- UML diagrams
Data modeling tools like ERWin or Visio primarily aid in visualizing and designing Entity-Relationship Diagrams (ERDs), which depict the entities, attributes, and relationships in a database schema.
Which data extraction technique involves querying a database directly to retrieve specific data sets?
- Direct extraction
- Full extraction
- Incremental extraction
- Parallel extraction
Direct extraction involves querying a database directly to retrieve specific data sets based on defined criteria. This method is often used when only a subset of data is required for analysis or processing.
Scenario: Your team is experiencing performance issues with a database application. As a data engineer, how would you leverage physical data modeling to address these issues?
- Denormalization of database schema
- Implementing additional constraints and checks
- Normalization of database schema
- Optimizing table indexes and partitioning
Leveraging physical data modeling involves optimizing table indexes, partitioning data appropriately, and organizing the physical layout of data to enhance performance and address specific performance issues in the database application.
In an RDBMS, a ________ is a virtual table that represents the result of a database query.
- Index
- Stored Procedure
- Trigger
- View
In an RDBMS, a view is a virtual table that represents the result of a database query. It provides a way to present data in a structured manner without storing the actual data, thus simplifying data access and enhancing security.
Which type of data model represents the high-level structure and relationships between data entities and is independent of any specific database management system?
- Conceptual Data Model
- Hierarchical Data Model
- Logical Data Model
- Physical Data Model
A conceptual data model represents the high-level structure and relationships between data entities. It is independent of any specific database management system and focuses on the business concepts and rules.
In Dimensional Modeling, what are Dimensions?
- Categories that provide context to the facts
- Primary keys in a relational database
- Tables that store descriptive attributes
- Tables that store transactional data
Dimensions in Dimensional Modeling are categories or entities that provide context to the facts stored in the Fact Table. They contain descriptive attributes that help in analyzing and understanding the data.
Which execution mode in Apache Spark provides fault tolerance for long-running applications?
- Kubernetes mode
- Mesos mode
- Standalone mode
- YARN mode
In Apache Spark, running applications in YARN mode provides fault tolerance for long-running applications. YARN manages resources and ensures fault tolerance by restarting failed tasks on other nodes.
________ assesses the accuracy of data in comparison to a trusted reference source.
- Data accuracy
- Data consistency
- Data integrity
- Data validity
Data accuracy assesses the correctness and precision of data by comparing it to a trusted reference source. It involves verifying that the data values are correct, free from errors, and aligned with the expected standards or definitions. This process ensures that decisions and analyses made based on the data are reliable and trustworthy.