A company is looking to set up a system for real-time analytics on a large dataset that is constantly updated. They need to perform complex queries and aggregations frequently. Which type of database should they consider?
- Data Warehouse
- In-memory Database
- NoSQL Database
- Relational Database
For real-time analytics on large datasets with frequent complex queries and aggregations, an in-memory database is most suitable. In-memory databases store data in RAM for quick access, making them ideal for such scenarios.
What is a common method used to ensure data consistency in a data warehouse environment?
- Data Duplication
- Data Fragmentation
- Data Obfuscation
- ETL Processes
One common method used to ensure data consistency in a data warehouse environment is the use of Extract, Transform, Load (ETL) processes. ETL processes are responsible for extracting data from source systems, transforming it to meet the data warehousing standards, and loading it into the data warehouse, ensuring data accuracy and consistency.
Which of the following best describes a scenario where a full load would be preferred over an incremental load?
- When you need to maintain historical data in the data warehouse
- When you need to update the warehouse frequently
- When you want to keep storage costs low
- When you want to reduce data processing time
A full load is preferred over an incremental load when you need to maintain historical data in the data warehouse. Incremental loads are typically used for efficiency, but when historical data must be preserved, a full load is necessary to capture all records accurately.
After loading data into a data warehouse, analysts find discrepancies in sales data. The ETL team is asked to trace back the origin of this data to verify its accuracy. What ETL concept will assist in this tracing process?
- Data Cleansing
- Data Profiling
- Data Staging
- Data Transformation
"Data Profiling" is a critical ETL concept that assists in understanding and analyzing the data quality, structure, and content. It helps in identifying discrepancies, anomalies, and inconsistencies in the data, which would be useful in tracing back the origin of data discrepancies in the sales data.
In the context of BI, what does OLAP stand for?
- Online Analytical Processing
- Open Language for Analyzing Processes
- Operational Logistics and Analysis Platform
- Overlapping Layers of Analytical Performance
In the context of Business Intelligence (BI), OLAP stands for "Online Analytical Processing." OLAP is a technology used for data analysis, allowing users to interactively explore and analyze multidimensional data to gain insights and make data-driven decisions.
Big Data solutions often utilize _______ processing, a model where large datasets are processed in parallel across a distributed compute environment.
- Linear
- Parallel
- Sequential
- Serial
Big Data solutions make extensive use of "Parallel" processing, which involves processing large datasets simultaneously across a distributed compute environment. This approach significantly enhances processing speed and efficiency when dealing with vast amounts of data.
Which of the following techniques involves pre-aggregating data to improve the performance of subsequent queries in the ETL process?
- Data Deduplication
- Data Profiling
- Data Sampling
- Data Summarization
Data summarization involves pre-aggregating or summarizing data, usually at a higher level of granularity, to improve query performance in the ETL process. This technique reduces the amount of data that needs to be processed during queries, resulting in faster and more efficient data retrieval.
What is a primary benefit of Distributed Data Warehousing?
- Enhanced query performance
- Improved data security
- Lower initial cost
- Reduced data redundancy
One of the primary benefits of Distributed Data Warehousing is improved query performance. By distributing data across multiple servers and nodes, queries can be processed in parallel, resulting in faster response times and better performance for analytical tasks.
A common data transformation technique that helps in reducing the influence of outliers in the dataset is known as _______.
- Data Imputation
- Data Normalization
- Data Scaling
- Data Standardization
Data standardization is a common data transformation technique that helps reduce the influence of outliers in the dataset. It scales the data to have a mean of 0 and a standard deviation of 1, making it suitable for algorithms sensitive to the scale of the input features.
A high number of _______ can indicate inefficiencies in query processing and might be a target for performance tuning in a data warehouse.
- Aggregations
- Indexes
- Joins
- Null Values
In a data warehouse, a high number of joins in queries can indicate inefficiencies in query processing. Joins, especially complex ones, can impact performance. Performance tuning may involve optimizing or simplifying these joins to enhance query efficiency.