Today, data has become an evolving asset in this ever-growing technological world dominated by the advanced concepts of the internet of everything (IoE) and the internet of things (IoT). Several modern organizations rely on data to make business decisions backed by insights derived from it.
Within the data analytics field, online analytical processing (OLAP) and online transaction processing (OLTP) are two fundamental processing systems used to solve complex data problems. This article explains the principles of OLAP and OLTP and their key comparisons.
Table of Contents
Today, in this ever-growing technological world dominated by the advanced concepts of the internet of everything (IoE) and the internet of things (IoT), data has become an evolving asset. Several modern organizations rely on data to make business decisions backed by insights derived from it.
Within the data analytics field, online analytical processing (OLAP) and online transaction processing (OLTP) are two fundamental processing systems used to solve complex data problems. Let’s understand each type in detail.
What is OLAP?
Online analytical processing (OLAP) is the online processing system that analyzes and processes large quantities of data at considerable speeds to perform multidimensional data analysis that allows the user to view data from different perspectives or vantage points and make corresponding decisions. The data component of OLAP comes from a centralized data store, data mart, or data warehouse.
Typically, OLAP systems are designed to carry out complex analytical calculations with a strong inclination for business intelligence (BI) processes and data mining practices that promote business analysis. Moreover, OLAP also supports business reporting, such as the ones observed in financial analysis, budgeting, and sales forecasting.
For example, OLAP in BI allows concerned stakeholders to query, view, and explore business-related data without any intervention from the IT department. Users can generate reports on the fly, be it custom or specific reports, without needing to call IT staff for help.
Technically, OLAP databases rely on OLAP cube to query, view, analyze, and report multidimensional data having different features or attributes. Data dimension primarily refers to a single element or entity of a dataset. For example, car sales figures can include dimensions such as car sale region, car sale period, car models sold, average monthly sales, and so on.
The OLAP cube is represented via the row-by-column format, which is a part of the relational database (dB) schema. Once the structure is established, other dimensions are added in the form of add-on layers. For example, consider the previous example of car sales figures. In this case, the uppermost layer may define car sales by region, which one can further mine to reveal car sales in a particular country, state, area, or store. Traditionally, OLAP data is stored in a star or snowflake schema.
Let’s visualize the OLAP cube that shows car sales figures by region, car model, and time.
OLAP systems use relational databases such as MySQL for implementation and are referred to as ROLAP (relational OLAP). OLAP applications include business reporting for sales, financial reporting, business process management, marketing, management reporting, and so on.
Some of the best OLAP tools include Integrate.io, IBM Cognos, Micro Strategy, Palo OLAP Server, and Apache Kylin.
Characteristics of OLAP
Let’s take a look at some of the essential attributes of OLAP:
- Provides a multidimensional yet conceptual view of OLAP data: OLAP systems are designed in a manner that allows business users and stakeholders to have a dimensional and logical data view residing within the data warehouse. It promotes slice and dice operations, wherein slicing and dicing refer to the methods used to separate a portion (slice) of the OLAP cube and dice the cube (slice) to view the OLAP data from different vantage points comprehensively.
- Offers multi-user support: OLAP systems share techniques between organizations, enabling normal database operations such as data retrieval, update, etc., ensuring data integrity and security.
- Accessibility: OLAP is an intermediary between front-end and data sources (data warehouses).
- Reporting performance: An increase in the database size and dimensions within an OLAP system does not affect its reporting performance.
- OLAP results: OLAP provides a complex data analysis to its users by performing calculations and comparisons. Moreover, OLAP systems present results in understandable, meaningful, and interactive formats such as charts and graphs.
See More: What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends
What is OLTP?
Online transaction processing (OLTP) refers to systems that help execute multiple database transactions in real time for a group of people active on the internet. It typically stores and manages data in day-to-day operations such as ATM transactions, e-commerce purchases, online banking, and hotel bookings.
A simple database transaction involves insertion, deletion, or updating data in a database. Such transactions act as a record for financial transactions that one performs daily, such as in-store shopping. Moreover, OLTP is also an integral part of non-financial database interactions, such as the ones involved in text messages or password updates.
OLTP operations generally relate to relational databases or ACID (Atomicity, Consistency, Isolation, and Durability)-compliant databases with high availability and are designed to process and handle many transactions. Moreover, the databases used by OLTP systems ensure data integrity while managing multiple user access at one time. In short, OLTP is more about data processing than analysis, where the focus is on handling large data volumes.
In simple terms, OLTP refers to how data stores are accessed and used. For example, if we consider the MongoDB database, which stores and processes data from daily operations such as social media activities like registering new users, storing likes, dislikes, and so on, we can consider it a part of OLTP.
Some known OLTP systems include MySQL, IBM DB2, Microsoft SQL, and Oracle database.
Characteristics of OLTP
An OLTP system reveals the following features:
- Processes simple database transactions: Database transactions include insertions, deletions, and updates to data, along with the execution of simple queries, such as a balance check at an ATM.
- Offers multi-user access: OLTP systems employ concurrency control algorithms that tend to prevent concurrent transactions on the same data. They also maintain the appropriate transaction order, which avoids any data conflict. For example, consider an online booking system. OLTP-enabled algorithms prevent the booking of the same seat by two people.
- Faster data processing and quicker response time: The performance of an effective OLTP system is measured by the metric that evaluates the total number of transactions processed per second.
- High availability (24/7): As OLTP systems are prone to situations where concurrent transactions often occur, there can be significant data loss, which can greatly impact the overall system performance. Regular data backups are a must for OLTP systems, which are always available, preventing system downtime.
- Examples: OLTP has become an omnipresent system with the advent of the internet and IoE technologies. Some day-to-day examples include ATMs, online banking applications, credit card payments, online retail purchases, online reservation systems, etc.
See More: What Is Logistic Regression? Equation, Assumptions, Types, and Best Practices
OLAP vs. OLTP: 13 Key Comparisons
OLAP and OLTP are integral to data science. OLAP is more focused on the analytics part of data processing, wherein large volumes of multi-dimensional data are analyzed to reveal relationships between disparate data types. It helps organizations identify, extract, and analyze hidden insights from the data.
OLAP vs. OLTP
On the contrary, OLTP focuses on managing a series of online transactions. While both systems are essential to solve business matters, each type has its unique purpose and different method of processing data.
Here’s an overview of the critical differences between OLAP and OLTP:
| Characteristics | OLAP | OLTP |
| Objective | Analyze data to identify hidden patterns and derive insights. | Controlling, executing, and managing real-time business operations |
| Queries | SELECT queries with aggregations that identify data patterns | Simple queries for INSERT, DELETE, and UPDATE operations |
| Availability | Lacks high-availability features | Has high availability |
| Processing Time | Response time ranges from seconds, a couple of minutes, to hours | Response time is within a few milliseconds |
| Data Source | OLAP databases that employ multi-dimensional schema | Relational databases such as DBMS (database management system) |
| Database Table Design | Relies on denormalized database tables | Inclined toward using normalized database tables |
| User Examples | Used by data analysts, managers, and corporate executives who are responsible for knowledge management | Used by IT professionals, clients, corporate clerks, and frontline workers such as bank tellers and cashiers |
| Database Design | Subject-specific database design | Application- or industry-specific database design |
| Data View | Multi-dimensional view of organizational data | Data view lists day-to-day transactions |
| Access Type | Allows read-only operations | Access type ensures concurrency, control, and data recovery methods |
| Dataset Size | Terabytes | Gigabytes |
| Data Updates | Data periodically refreshed | Short, fast, and regular updates |
| Space Requirements | Significant space requirement as OLAP aggregates datasets | Space requirements are directly proportional to the number of transactions processed |
Let’s dig deeper into the differences between OLAP and OLTP:
1. Objective
OLAP: The primary objective of the OLAP system is to analyze data to identify hidden patterns and derive insights from it that allow businesses and stakeholders to make calculated decisions that are backed by data.
OLTP: On the other hand, OLTP is more inclined toward controlling, executing, and managing real-time business operations.
2. Queries
OLAP: An OLAP system uses SELECT queries with several aggregations to draw patterns and derive insights from data to find a solution or answer to specific business queries.
OLTP: OLTP systems perform INSERT, DELETE, and UPDATE operations using simple database queries.
3. Availability
OLAP: OLAP systems are not designed to execute, manage, or handle any real-time transaction over an existing dataset. Hence, organizations must refrain from investing resources and efforts to develop high-availability features. Although highly available OLAP systems may inevitably serve better, it isn’t necessary. As a result, OLAP systems do not require frequent backups to function smoothly.
OLTP: OLTP refers to a transaction system where transactional data is inserted or modified frequently. Hence, the OLTP system must have high availability. Moreover, real-time transactions demand the system to have frequent or concurrent data backups to ensure data integrity.
4. Processing time
OLAP: OLAP systems handle large quantities of data requiring ample time to parse the data. As such, response time for OLAP systems may range from seconds, minutes, to hours, depending on the data that needs to be processed. The scenario seems plausible as such systems also tend to use complex database queries compared to OLTP systems.
OLTP: OLTP systems are expected to be highly responsive, having response time scaled to a few milliseconds. As the database queries executed on these systems are simple, easy, and space-efficient, achieving such minimal processing times isn’t a problem.
5. Data sources
OLAP: OLAP systems use OLAP databases as a data source, employing multi-dimensional schema, as observed in the OLAP cube example earlier.
OLTP: OLTP systems rely on relational databases such as DBMS (database management system) as their data source to perform real-time database transactions.
6. Design of database table
Fundamentally, database normalization refers to the methods employed to restructure relational databases to standard forms to ensure data integrity and manage data redundancy.
OLAP: OLAP systems rely on denormalized database tables that are key and easier to access when it comes to data analysis.
OLTP: On the other hand, OLTP systems are inclined toward using normalized database tables to keep the system’s efficiency in place.
7. User examples
OLAP: OLAP is a market-focused data analysis tool. It is used by data analysts, managers, and corporate executives responsible for knowledge management.
OLTP: OLTP is a transaction processing tool that is focused on customers. Such systems use simple queries to process database transactions and are used by IT professionals, clients, corporate clerks, or frontline workers such as bank tellers and cashiers.
8. Database design
OLAP: OLAP systems have a subject-specific database design with an inclination for a star or snowflake model. OLAP is thereby suitable for sales, inventory, or marketing departments.
OLTP: OLTP systems present an application- or industry-specific database design and use an entity-relationship data model. OLTP systems are suitable for the retail, manufacturing, and banking sectors.
9. Data view
OLAP: OLAP systems are all about analysis; hence, such systems tend to use different database schema versions. Moreover, the OLAP data is shared across multiple storage media as different organizations collaborate. Thus, businesses offer a multi-dimensional view of organizational data.
OLTP: In OLTP systems, only current data is the focus without considering historical data or data from multiple organizations. Thus, businesses offer lists of day-to-day transactions.
10. Access type
OLAP: OLAP systems generally allow read-only operations with the help of complex queries.
OLTP: OLTP systems consider shorter database transactions, requiring concurrency, control, and data recovery methods to be in place.
11. Dataset size
OLAP: In OLAP systems, the size of datasets is within Terabytes.
OLTP: In OLTP systems, dataset size is within Gigabytes.
12. Data updates
OLAP: In OLAP systems, data is periodically refreshed. Moreover, long-running batch jobs are scheduled well in advance.
OLTP: In OLTP systems, updates are fast and short and are triggered by the user.
13. Space requirements
OLAP: An OLAP system can have significant space requirements as it is known for aggregating large datasets.
OLTP: In OLTP systems, space requirements are directly proportional to the number of transactions processed and the variable defining online storage capacity. In comparison to OLAP, OLTP requires less space, typically when historical data is archived.
See More: What Is a Neural Network? Definition, Working, Types, and Applications in 2022
Takeaway
OLAP and OLTP are known online processing systems capable of fulfilling different data processing needs. Picking the right one from amongst the two largely depends on end-user requirements.
In simple words, for managing day-to-day tasks, OLTP is the better option. However, OLAP is the go-to option among the two when processing historically-stored data, which helps in critical problem-solving and decision-making.
Did this article help you understand the difference between OLAP and OLTP? Comment below or let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!
MORE ON ARTIFICIAL INTELLIGENCE
- What Is Quantum Computing? Working, Importance, and Uses
- What Is HCI (Human-Computer Interaction)? Meaning, Importance, Examples, and Goals
- What Is Semantic Analysis? Definition, Examples, and Applications in 2022
- What Is Ubiquitous Computing (Pervasive Computing)? Meaning, Layers, Examples, and Applications
- What Is Computer Vision? Meaning, Examples, and Applications in 2022