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OLAP
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OLTP
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Purpose
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Primarily designed for data analysis and decision-making support, OLAP allows users to perform complex queries and generate reports on large volumes of data.
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Focused on managing day-to-day transactions and operations, OLTP systems ensure data integrity, maintainability, and consistency for real-time business applications.
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Data Structure
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Organizes data using a multidimensional model, which enables efficient querying and data analysis. Commonly used structures include star schema, snowflake schema, and cube.
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Uses a relational model with tables, rows, and columns to store data. It typically employs normalization to minimize data redundancy and improve data integrity.
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Query Type and Complexity
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Handles complex, ad-hoc queries that usually involve aggregations, calculations, and data exploration across multiple dimensions.
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Processes simple, repetitive, and predefined queries that focus on inserting, updating, or deleting data records.
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Data Volume and Storage
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Manages large volumes of historical and aggregated data, which are often derived from multiple sources and transformed for analysis purposes.
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Maintains current, real-time data that is typically more compact and transactional in nature.
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Performance Metrics
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Prioritizes query response time, as users need quick access to insights and trends for decision-making.
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Emphasizes transaction throughput, as a high volume of concurrent transactions must be efficiently processed and maintained.
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Data Update Frequency
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Usually updated less frequently, as it deals with historical and aggregated data. Updates are performed during ETL (Extract, Transform, Load) processes.
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Requires frequent updates to ensure the system stays current with real-time transactions and operations.
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