Business Intelligence and Data Warehousing Quiz

A three-layer architecture commonly used for data warehouses: 1. Warehouse server (data server), 2. OLAP server (HOLAP/MOLAP or ROLAP), and 3. Customer server (for execution and analysis).

This refers to data that is incomplete or missing, often due to unavailable information at the time of collection, time gaps between collection and analysis, or human error.

Noisy data refers to inaccurate or unreliable data, often resulting from errors during collection, human input, data transmission, or system limitations.

Inconsistent data occurs when information from different sources conflicts or violates established rules, leading to discrepancies.

Duplicate data refers to redundant entries or synonyms that represent the same information, often indicating poor data quality.

Poor data quality significantly impacts the accuracy of results and decisions derived from the data. Even minor inconsistencies can lead to inaccurate statistics or misleading conclusions.

Extraction, Transformation, and Loading (ETL) is a crucial back-end process in data warehousing. It involves extracting data from source systems, transforming it into a consistent format, and loading it into the data warehouse.

ETL is a hidden process that ensures data quality and consistency before it reaches the end-user. It's invisible to those who utilize the data warehouse.

A data cube operation that extracts a specific data block from the cube based on a defined set of dimensions and measures.

A data cube operation that reduces the granularity of data by aggregating data points to a higher level of detail. Like going from individual sales data to total sales by region.

A data cube operation that increases the granularity of data by drilling down into more detailed information within a specific dimension. Like going from total sales by region to sales by individual store within a region.

A query language designed for analyzing and manipulating large datasets in data cubes, allowing users to perform complex operations like slicing and dicing, roll-up and drill-down.

The process of collecting data from various sources like databases, cloud platforms, or XML files. It involves identifying and selecting relevant data, ensuring its quality and quantity meet requirements, and determining its suitability for extraction.

The most challenging part of the ETL process due to the diversity of sources, potential data quality issues, and difficulty in evaluating extraction eligibility.

A necessary step in extraction that involves thoroughly understanding the data before extracting it. This is an ongoing process.

Data is extracted on a regular basis to ensure the data warehouse remains up-to-date with any changes.

A temporary storage area for extracted data where its integrity is checked and business rules are applied before it is loaded into the data warehouse.

A type of extraction where all data from the source is collected, regardless of changes. It is often used when the system can't track changes.

A type of extraction that focuses on extracting only new or updated data since the last extraction. It efficiently updates the data warehouse.

The extraction tool needs to identify new or updated data based on timestamps or other indicators to enable incremental extraction.

A data integration approach where data is loaded directly from source to target and transformed after, leading to faster loading but requires less mature tools and support.

A data integration approach using a multi-stage process of extracting, transforming, and loading data into a warehouse before it can be used for analysis.

ELT is a newer approach than ETL, meaning it may not be as widely supported by tools and solutions, but it can be advantageous in some cases.

ETL is widely used and has been for many years, leading to more readily available tools, experts, and support.

ETL often works with a data warehouse, a system designed for storing and analyzing large datasets.

ELT can work with both cloud and on-premise data warehouses, while ETL is traditionally associated with data warehouses and may require specific adjustments for cloud use cases.

ETL often handles structured data and OLAP (online analytical processing), while ELT can handle more diverse data types, including unstructured and semi-structured data.

When working with complex transformations and a smaller amount of data, ETL is a suitable approach. However, ELT is better suited for larger datasets and simpler transformations.

A technology that structures massive business databases for advanced analysis, enabling sophisticated insights and strategic decision-making.

A centralized repository designed to store and manage large volumes of data from multiple sources, enabling comprehensive analysis and reporting.

The use of statistical techniques and algorithms to discover hidden patterns and relationships within data, enabling informed decision-making.

Tools and methods used to create reports, dashboards, and visualizations based on data stored in the data warehouse, providing clear and concise insights for decision-makers.

Software that performs complex analytical operations on data stored in the data warehouse, enabling multidimensional analysis and understanding of data relationships.

A collection of data from various sources, organized and structured for efficient analysis and reporting.

The management and maintenance of data warehouses and databases to ensure their security, efficiency, and performance.

They are multidimensional data structures that represent data from multiple perspectives, allowing for efficient analysis and reporting.

A multidimensional database is designed specifically for complex data analysis and reporting, supporting OLAP queries and operations.

The act of performing data operations like slicing, dicing, drilling down, and rolling up on a multidimensional data cube.