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OLTP vs OLAP | SQL for Beginners - Software Development PDF Download
Online Transaction Processing vs Online Analytical Processing
Online Transaction Processing System:OLTP
OLTP System deals with operational data. Operational data are those data involved in the operation of a particular system.
Example: In a banking System, you withdraw amount through an ATM. Then account Number,ATM PIN Number,Amount you are withdrawing, Balance amount in account etc are operational data elements.
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Operational Data
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Operational data are usually of local relevance
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Frequent Updates
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Normalized Tables
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Point Query
In an OLTP system data are frequently updated and queried. To prevent data redundancy and to prevent update anomalies the database tables are normalized.Set of tables that are normalized are fragmented.Normalization makes the write operation in the database tables more efficient.Operational data are usually of local relevance.It involves Queries accessing individual tuple(individual record).These type of queries are termed as point queries.
Examples for OLTP Queries:
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What is the Salary of Mr.John?
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What is the address and email id of the person who is the head of maths department?
Online Analytical Processing:OLAP
OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time.
Example: If we collect last 10 years data about flight reservation, The data can give us many meaningful information such as the trends in reservation. This may give useful information like peak time of travel, what kinds of people are traveling in various classes (Economy/Business)etc.
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Historical Data or Archival Data
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Infrequent updates
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Analytical queries require huge number of aggregations
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Integrated data set with a global relevance
Updates are very rare here.Analytical queries requires huge number of aggregations. In analytical queries the performance issue is mainly in query response time.Query need to access large amount of data and require huge number of aggregation.
OLAP Queries have significant importance in strategic decision making. This helps the top level management in decision making.
Examples for OLAP Queries
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How is the profit changing over the years across different regions ?
Is it financially viable continue the production unit at location X?
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FAQs on OLTP vs OLAP - SQL for Beginners - Software Development
| 1. What is the difference between OLTP and OLAP? |  |
OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) are two different approaches to data processing in a database system.
OLTP is used for transactional processing, where the focus is on real-time data entry, retrieval, and processing. It is designed to handle a large number of small, frequent transactions with a low response time. OLTP databases are typically normalized and optimized for efficient transaction processing.
On the other hand, OLAP is used for analytical processing, where the focus is on complex queries and data analysis. It is designed to handle large volumes of data and provide fast response times for complex queries and aggregations. OLAP databases are typically denormalized and optimized for efficient data analysis.
| 2. What are the main characteristics of OLTP systems? | |
OLTP systems have the following main characteristics:
- They are designed for real-time transaction processing, where data is entered, retrieved, and processed in real-time.
- They handle a large number of small, frequent transactions with a low response time.
- They prioritize data integrity and consistency, ensuring that transactions are completed accurately and reliably.
- They are typically used in operational environments, such as retail, banking, and e-commerce, where immediate data processing is essential for day-to-day operations.
- They have a normalized database structure to minimize redundancy and optimize data integrity.
| 3. What are the main characteristics of OLAP systems? | |
OLAP systems have the following main characteristics:
- They are designed for complex data analysis and reporting, where users perform ad-hoc queries and aggregations on large volumes of data.
- They provide fast response times for complex queries, allowing users to explore and analyze data interactively.
- They prioritize data consolidation and summarization, enabling users to view data from different dimensions and levels of granularity.
- They are typically used in decision-making and strategic planning environments, such as business intelligence and data warehousing.
- They have a denormalized database structure to optimize query performance and facilitate data analysis.
| 4. How do OLTP and OLAP databases differ in terms of database design? | |
OLTP databases are designed for transactional processing and prioritize data integrity. They typically have a normalized database structure, where data is organized into multiple related tables to minimize redundancy and ensure data consistency. This design approach is suitable for real-time data entry, retrieval, and processing.
On the other hand, OLAP databases are designed for analytical processing and prioritize query performance. They typically have a denormalized or partially denormalized database structure, where data is organized into fewer tables with pre-aggregated values to optimize query response times. This design approach facilitates complex data analysis and reporting.
| 5. What are some common use cases for OLTP and OLAP systems? | |
Common use cases for OLTP systems include:
- Retail: Tracking sales, inventory management, and order processing in real-time.
- Banking: Managing customer accounts, processing transactions, and handling online banking activities.
- E-commerce: Handling online purchases, payments, and inventory management.
- Healthcare: Managing patient records, appointments, and medical billing.
Common use cases for OLAP systems include:
- Business Intelligence: Analyzing sales data, customer behavior, and market trends for decision-making.
- Financial Analysis: Performing financial reporting, budgeting, and forecasting.
- Supply Chain Management: Analyzing inventory levels, logistics, and supplier performance.
- Customer Relationship Management: Analyzing customer data, segmentation, and campaign effectiveness.
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