A retail company wants to analyze the past 10 years of transaction data to forecast future sales. They are considering big data solutions due to the volume of data. Which storage and processing model would be most suitable?
- Data Warehousing
- Hadoop Distributed File System (HDFS)
- NoSQL Database
- Relational Database
For handling vast volumes of data and conducting complex analytics, a big data solution like Hadoop Distributed File System (HDFS) is well-suited. It can store and process large-scale data efficiently, making it ideal for analyzing extensive historical transaction data.
How does logical modeling differ from physical modeling in terms of its audience or target stakeholders?
- Logical modeling and physical modeling have the same target audience.
- Logical modeling deals with data visualization, while physical modeling deals with data analysis.
- Logical modeling focuses on data structures, while physical modeling focuses on business processes.
- Logical modeling targets business users, while physical modeling targets IT professionals.
Logical modeling is primarily intended for business users and stakeholders who want to understand the data in a business context. It focuses on data structure and representation without considering technical implementation details. In contrast, physical modeling is aimed at IT professionals who design the actual database systems and consider implementation specifics.
How does a data mart differ from a data warehouse in terms of data integration?
- Data marts are smaller and more focused subsets of a data warehouse
- Data marts have more historical data than data warehouses
- Data warehouses are only used for reporting purposes
- Data warehouses do not support data integration
A data mart is a smaller, more focused subset of a data warehouse that is designed for a specific business unit or department. Unlike data warehouses, data marts are not intended for enterprise-wide use, and they contain data that is tailored to the needs of a particular group.
Cloud-based data warehousing solutions are often _______ scalable, meaning they can adjust to workload demands in real-time.
- Horizontally
- Rapidly
- Statically
- Vertically
Cloud-based data warehousing solutions are often "Horizontally" scalable, allowing them to adjust to workload demands in real-time by adding or removing resources horizontally, such as adding more servers or clusters. This scalability is a key advantage of cloud-based data warehousing, ensuring performance and flexibility.
A retail company wants to analyze sales data specifically for its clothing department, without considering other departments like electronics or groceries. Which data storage solution would be most appropriate?
- Data Lake
- Data Mart
- Data Warehouse
- NoSQL Database
In this scenario, a Data Mart would be the most suitable data storage solution. A Data Mart is a specialized data repository designed for a specific business function or department, making it ideal for isolating and analyzing sales data for the clothing department while excluding other unrelated data. It provides a more focused and efficient way to store and access department-specific information.
How do columnar storage databases optimize query performance in big data scenarios?
- Applying complex indexing techniques
- Encoding and compressing data in columnar format
- Storing data in rows for faster retrieval
- Utilizing a single data column for all records
Columnar storage databases optimize query performance in big data scenarios by encoding and compressing data in a columnar format. This minimizes the amount of data read from storage, leading to faster query execution. It also enhances compression, reducing storage requirements.
In data warehouse monitoring, a(n) _______ provides a visual representation of the system's performance metrics in real-time.
- Dashboard
- Data Mart
- Data Query
- ETL Process
A dashboard is a crucial tool in data warehouse monitoring. It offers a visual representation of the system's performance metrics in real-time. Dashboards help data professionals track key performance indicators and quickly identify issues or opportunities for optimization.
What does the term "data skewness" in data profiling refer to?
- A data visualization method
- A type of data transformation
- Data encryption technique
- The tendency of data to be unbalanced or non-uniformly distributed
"Data skewness" in data profiling refers to the tendency of data to be unbalanced or non-uniformly distributed. It indicates that the data has a skew or imbalance in its distribution, which can affect statistical analysis and modeling. Understanding skewness is crucial in data analysis and decision-making.
When a change in a dimension attribute results in marking the old record as inactive and inserting a new record with the changed data, it represents SCD type _______.
- SCD Type 1
- SCD Type 2
- SCD Type 3
- SCD Type 4
In Slowly Changing Dimension (SCD) Type 2, changes in dimension attributes are handled by marking the old record as inactive and inserting a new record with the updated data. This allows historical tracking of attribute changes.
In big data analytics, the process of analyzing current and historical data to make predictions about future events is known as _______.
- Data Aggregation
- Data Retrieval
- Descriptive Analytics
- Predictive Analytics
In big data analytics, the process of analyzing current and historical data to make predictions about future events is known as "Predictive Analytics." Predictive analytics uses statistical algorithms and machine learning techniques to identify patterns and trends in data, helping organizations make informed decisions and forecasts.