Scenario: Your team is tasked with optimizing query performance in a reporting database. Discuss whether you would consider denormalization as part of your optimization strategy and justify your answer.
- No, denormalization can compromise data integrity and increase the risk of anomalies
- No, denormalization can lead to data redundancy and inconsistency, making maintenance challenging
- Yes, denormalization can enhance data aggregation capabilities and streamline complex reporting queries
- Yes, denormalization can improve query performance by reducing the number of joins and simplifying data retrieval
In optimizing query performance for a reporting database, denormalization can be considered as it reduces the need for joins, simplifies data retrieval, and enhances data aggregation capabilities. However, it's crucial to weigh the performance benefits against the potential risks to data integrity and consistency.
What is Apache Spark primarily used for?
- Big data processing
- Data visualization
- Mobile application development
- Web development
Apache Spark is primarily used for big data processing, enabling fast and efficient processing of large datasets across distributed computing clusters. It provides various libraries for diverse data processing tasks.
The process of replicating data across multiple brokers in Kafka is called ________.
- Distribution
- Partitioning
- Replication
- Sharding
The process of replicating data across multiple brokers in Kafka is called Replication. Kafka ensures fault tolerance and reliability by replicating data across multiple brokers in a Kafka cluster.
How does Hadoop YARN improve upon the limitations of the classic MapReduce framework?
- It enables real-time data processing
- It enhances fault tolerance and data replication
- It improves data compression techniques
- It introduces a resource management layer, enabling support for diverse processing frameworks
Hadoop YARN (Yet Another Resource Negotiator) improves upon the classic MapReduce framework by introducing a resource management layer, allowing for support of various processing frameworks beyond MapReduce.
What is a weak entity in an ERD?
- An entity that can exist independently
- An entity that cannot be uniquely identified
- An entity that is strongly related to another entity
- An entity with a single attribute
A weak entity in an ERD is one that cannot be uniquely identified by its attributes alone. It depends on a related entity (owner entity) for its existence and is represented by a double-bordered rectangle.
What factors should be considered when determining the maximum number of retry attempts?
- Nature of the operation being retried
- Network bandwidth availability
- Service-level agreements (SLAs)
- Time of day
Determining the maximum number of retry attempts requires careful consideration of various factors. The nature of the operation being retried is crucial, as some operations may be more tolerant of retries than others. Service-level agreements (SLAs) also play a significant role, as they dictate acceptable response times and failure rates. Additionally, factors such as network conditions and time of day may influence the likelihood of successful retries and should be taken into account when setting retry policies.
Scenario: After finalizing the logical data model for a new database, what would be your next step in the design process?
- Data Warehousing
- Database Implementation
- Indexing
- Physical Data Model
After finalizing the logical data model, the next step would be to proceed with the database implementation phase, where the logical design is translated into the actual database schema and structures, ready for deployment.
________ feature in data modeling tools ensures that the design conforms to predefined rules and standards.
- Forward Engineering
- Reverse Engineering
- Synchronization
- Validation
The validation feature in data modeling tools ensures that the design adheres to predefined rules and standards, helping maintain consistency and quality in the database schema design process.
Why is it important to involve stakeholders in the data modeling process?
- To delay the project
- To gather requirements and ensure buy-in
- To keep stakeholders uninformed
- To make decisions unilaterally
It is important to involve stakeholders in the data modeling process to gather their requirements, ensure buy-in, and incorporate their insights, which ultimately leads to a database design that meets their needs.
The process of transforming raw data into a format suitable for analysis in a data warehouse is called ________.
- ELT (Extract, Load, Transform)
- ETL (Extract, Load, Transfer)
- ETL (Extract, Transform, Load)
- ETLT (Extract, Transform, Load, Transform)
The process of transforming raw data into a format suitable for analysis in a data warehouse is called ELT (Extract, Load, Transform). In this approach, data is first loaded into the warehouse and then transformed according to analysis requirements.
Which of the following best describes the primary purpose of Dimensional Modeling?
- Capturing detailed transactional data
- Designing databases for efficient querying
- Implementing data governance
- Organizing data for data warehousing
The primary purpose of Dimensional Modeling is to organize data for data warehousing purposes, making it easier to analyze and query for business intelligence and reporting needs.
In an RDBMS, what is a primary key?
- A key used for encryption
- A key used for foreign key constraints
- A key used for sorting data
- A unique identifier for a row in a table
In an RDBMS, a primary key is a column or set of columns that uniquely identifies each row in a table. It ensures the uniqueness of rows and provides a way to reference individual rows in the table. Primary keys are crucial for maintaining data integrity and enforcing entity integrity constraints. Typically, primary keys are indexed to facilitate fast data retrieval and enforce uniqueness.