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.
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.
Scenario: Your team is tasked with designing a complex database schema for a large-scale project. Which data modeling tool would you recommend and why?
- ERWin
- Lucidchart
- PowerDesigner
- Visio
PowerDesigner is recommended due to its robust features for handling complex database schemas, including advanced visualization capabilities, support for large-scale projects, and collaboration features.
Metadata management plays a crucial role in ________ by providing insights into data lineage and dependencies.
- Data analysis
- Data governance
- Data integration
- Data storage
Metadata management is essential for effective data governance, as it enables organizations to manage, control, and ensure the quality and usability of their data assets. By maintaining metadata, organizations can gain insights into data lineage, dependencies, and relationships, which are essential for making informed decisions about data usage, compliance, and risk management.