An e-commerce company wants a system that can handle millions of transactions per day, with quick read and write capabilities and consistency. What kind of database system would be most suitable?
- Columnar Database
- Document Database
- Key-Value Store
- NewSQL Database
To handle a high volume of transactions with quick read and write capabilities and consistency, a NewSQL database is the most appropriate choice. NewSQL databases combine the benefits of SQL (consistency) and NoSQL (scalability and speed) databases.
Which type of database, between traditional RDBMS and columnar databases, is typically better for OLTP (Online Transaction Processing) operations?
- Both are equally suitable
- Columnar Database
- Neither is suitable for OLTP
- Traditional RDBMS
Traditional RDBMS (Relational Database Management Systems) are typically better suited for OLTP (Online Transaction Processing) operations, which require fast and efficient handling of numerous small, concurrent transactions.
For an organization that requires immediate insight into business operations, which type of BI would be most appropriate?
- Analytical BI
- Operational BI
- Strategic BI
- Tactical BI
For an organization needing immediate insights into real-time business operations, Operational Business Intelligence (Operational BI) is most suitable. Operational BI focuses on monitoring and analyzing current data to support daily decision-making and operational activities. It provides up-to-the-minute information for faster response.
A business analyst is trying to analyze monthly sales data for the past three years. She wants to view sales by year, quarter, month, and day. What concept will aid her in viewing data at these different granularities?
- Data Modeling
- Data Normalization
- Data Warehousing
- Dimensional Modeling
Dimensional modeling is a technique used in data warehousing that enables the analysis of data at different granularities, such as year, quarter, month, and day. It involves creating fact tables and dimension tables to represent data hierarchies, facilitating efficient querying and reporting.
The design in which dimension tables are normalized, resulting in the formation of a structure that resembles a snowflake is called _______ schema.
- Constellation
- Galaxy
- Snowflake
- Star
A snowflake schema is a design approach in data warehousing where dimension tables are normalized to reduce data redundancy. This leads to a structure that resembles a snowflake due to the multiple related tables. It can help save storage space and improve data integrity but may require more complex queries.
In a sales data model, which hierarchy is most likely to be used to analyze sales trends?
- Customer Hierarchy
- Location Hierarchy
- Product Hierarchy
- Time Hierarchy
In a sales data model, the Time Hierarchy is crucial for analyzing sales trends. It allows analysts to explore sales data over different time periods, such as daily, monthly, or yearly, to identify patterns, seasonality, and trends. This hierarchy helps in time-based analysis, forecasting, and decision-making.
In a top-down approach to building a data infrastructure, which is typically built first?
- Data Integration
- Data Marts
- Data Sources
- Data Warehouses
In a top-down approach to building a data infrastructure, data sources are typically the first components to be addressed. Data sources include various systems and databases that store raw data, and they need to be integrated and processed to feed into data warehouses and data marts. Starting with data sources is fundamental to ensuring data quality and consistency.
The process of cleaning and enhancing the data so it can be loaded into a data warehouse is known as what?
- Data Extraction
- Data Integration
- Data Loading
- Data Transformation
The process of cleaning, transforming, and enhancing the data to prepare it for loading into a data warehouse is called "Data Transformation." During this phase, data is cleansed, structured, and enriched to ensure its quality and consistency for analysis.
A strategy that involves making copies of the data warehouse at regular intervals to minimize data loss in case of failures is known as _______.
- Data Cleansing
- Data Erosion
- Data Purging
- Data Replication
Data replication is a strategy in data warehousing that involves creating copies of the data warehouse at regular intervals. This approach helps minimize data loss in case of failures by ensuring that there are up-to-date backup copies of the data readily available. Data replication is essential for data resilience and disaster recovery.
Your data warehouse system alerts show frequent memory overloads during peak business hours. What could be a maintenance strategy to address this?
- Add more data storage capacity
- Implement data partitioning
- Increase CPU processing power
- Upgrade network bandwidth
To address memory overloads in a data warehouse, implementing data partitioning is a strategic maintenance strategy. Data partitioning involves dividing large tables into smaller, more manageable segments. This can reduce the memory requirements and improve query performance during peak hours.