A Data Warehouse is a subject-orientedcentral repository of integrated data from one or more disparate sources. It is a system used for reporting and data analysis, and is considered a core component of business intelligence.
Data warehouses store current and historical data (time-variant) and are used for creating analytical reports for knowledge workers throughout the enterprise, enabling business modelling, strategic planning and informed decision making.
Data Warehousing
Data Integration
Data warehousing involves the consolidation of data from various sources. This process ensures that the data is consistent and uniform, which is crucial for accurate reporting and analysis.
One of the challenges in data warehousing is ensuring data and consistency across different sources.
Data Storage
It provides a large-scale storage solution for integrating data from multiple sources. This data can be structured, semi-structured, or unstructured, and is optimized for fast retrieval and analysis.
Data warehousing is a technique used to large amounts of structured and unstructured data for analysis.
Data Analysis
One of the primary purposes of a data warehouse is to perform data analysis. Businesses use data warehouses to examine and analyze their data in various ways, including by creating reports and dashboards.
Which of the following is true about data warehousing?
Historical Intelligence
Data warehouses typically store large amounts of historical data. This allows businesses to analyze different time periods and trends to make future predictions.
Data warehousing allows for analysis by storing and analyzing data over time.
Business Intelligence (BI) Tools
These tools are often used in conjunction with data warehouses to extract meaningful insights from the data.
BI tools can include :
query tools
reporting tools
analytical processing tools
data mining tools.
Business intelligence tools enable users to explore data and uncover .
BI Dashboard
A Business Intelligence (BI) dashboard is a tool that visualizes key performance indicators and data relevant to an organization, using charts, graphs, and tables.
It helps in tracking, analyzing, and presenting data for better decision-making based on real-time information.
Features of a BI Dashboard
The main features of a BI dashboard include:
Key Performance Indicators (KPIs)
Crucial metrics reflecting business performance.
Data Visualization Tools
Charts, graphs, and tables for easy data interpretation.
Interactive Filters and Controls
Elements like dropdowns and sliders for dynamic data exploration.
Dashboard Layout and Design
Organized and intuitive design for effective data presentation.
Real-time Data and Refresh Capabilities
Ensuring the dashboard displays current and up-to-date information.
Dashboards typically display key and performance indicators.
Data Mart
A data mart is a subset of a data warehouse, often designed to serve a specific purpose or business area. It's like a smaller, more focused version of a data warehouse.
Key features of Data Marts
Specific Focus
Unlike a data warehouse that covers a wide range of subjects for the entire organization, a data mart is focused on a single subject area or department, such as sales, finance, or marketing.
Smaller Size and Complexity
Data marts are smaller in size and less complex compared to data warehouses. This smaller scale often results in reduced costs and easier maintenance.
Easier User Accessibility
They are designed to be more accessible to a specific group of users, providing them with the data they need in a format that is easy to understand and work with.
Faster Performance
Due to their focused nature and smaller size, data marts can often provide faster query responses and better performance than querying a large data warehouse.
Data Mart
Cloud-based Warehousing
With advancements in technology, cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake are becoming popular. They offer scalability, performance, and cost-effectiveness.
A cloud data warehouse uses a architecture to provide high performance.
Pros of Data Warehousing
Consolidated Data Analysis
It enables the consolidation of data from multiple sources into a single, central repository, making it easier to conduct comprehensive, timely analysis and provide improved business intelligence.
Historical Data Storage
It allows for the storage of large volumes of historical data, enabling businesses to analyze trends over time.
Enhanced Data Quality and Consistency
Data warehousing efforts often lead to the improvement of data quality and consistency.
High Query Performance
These systems are optimized for read access, providing fast response times for complex queries.
Separation of Analytical and Transactional Processes
By separating operational and analytical processing, data warehousing ensures that the performance of operational systems is not impacted by complex queries.
Cons of Data Warehousing
Complexity and Cost
Setting up and maintaining a data warehouse can be complex and costly. The initial setup, as well as ongoing maintenance and management, require significant resources and expertise.
Data Latency
There can be a delay in data availability in the warehouse due to the time taken in ETL (Extract, Transform, Load) processes.
Scalability Issues
Traditional data warehouses may face challenges in scaling up with rapidly growing data volumes and user demands.
Rigidity in Structure
Data warehouses often have a predefined structure which can be inflexible to changes and evolving business requirements.
Data Security and Privacy Concerns
Storing large amounts of sensitive data in a single repository raises concerns about data security and privacy
Which of the following is a disadvantage of data warehousing?
