OLAP and OLTP are both online processing systems but are slightly different. While OLAP is an analytical processing system, OLTP is a transactional processing system.
In data science, a given piece of information needs to be processed before putting it to use.
OLAP and OLTP are two data processing systems that businesses and individuals use primarily.
Although the methods of processing data and purpose are different, they are equally valuable in solving critical business problems.
In DBMS, both processing systems play an essential role in helping businesses with analytical and transactional tasks.
Let’s get into the details of OLAP and OLTP, their advantages and limitations, and the differences between these systems.
What is DBMS?
A database management system (DBMS) is a tool to manage the complete data of an organization. A database engine can access data, lock it, and modify it as needed. A database schema is used to describe the database’s structure.
A DBMS offers a centralized view of all the data for multiple users to access it from various locations in a precise and controlled manner. It can limit which data can be accessed by end users and how they view it, providing different views of the database schema.
Furthermore, DBMS offers physical and logical independence to secure applications and protect users from knowing where the data is. It refers to a sophisticated piece of a system that consists of various integrated components, which further deliver a managed and consistent environment for accessing, creating, and modifying data in the databases. These components are:
Database access language
Popular database management systems and models include NoSQL DBMS, in-memory DBMS, cloud DBMS, multimodel DBMS, columnar DBMS, NewSQL DBMS, and RDBMS. The biggest advantage of using DBMS is that it allows application programmers and users to access similar data while maintaining data integrity.
OLAP vs. OLTP: What are they?
What is OLAP?
Online Analytical Processing (OLAP) is an online processing system that performs multidimensional analysis on large volumes of data at high speed. This type of data comes from a data mart, centralized data store, or data warehouse.
An OLAP system is ideal for complex analytical calculations, business intelligence, data mining, and business reporting functions such as budgeting, sales forecasting, and financial analysis.
Furthermore, the OLAP cube, which is the core of OLAP databases, lets you quickly report, query, and analyze multidimensional data. Here, data dimension can be referred to as the element of a specific dataset.
For example, sales figures have various dimensions related to the time of year, region, product models, etc.
The OLAP cube extends the row and column format of a relational database schema and adds layers to several data dimensions. This historical data is then stored in a snowflake or star schema.
Examples of OLAP: Year-over-year financial marketing and performance trends, Netflix movie or series recommendations as per your previous search, and Spotify analyzing songs to enable users to create their desired playlists.
In short, OLAP stores historical data from which you can extract the information you want from a large database. You can use this information to make better business decisions.
The transaction is long in OLAP, and hence, comparatively takes more time to process the required data. You will find three types of OLAP systems:
Multidimensional OLAP that indexes directly into the multidimensional database
Relational OLAP performs multidimensional analysis of the data dynamically stored in the relational database
Hybrid OLAP is the combination of relational OLAP and multidimensional OLAP and is developed to combine data capacity with processing capability.
What is OLTP?
Online Transactional Processing (OLTP) is an online processing system that enables the execution of various database transactions by different people over the internet. From ATMs to store purchases and reservations, OLTP systems are behind many everyday transactions.
Apart from financial transactions, it drives non-financial transactions, such as text messages and password changes. OLTP uses a relational database that:
Enables multi-user accessibility for the same data
Processes a large number of simple transactions, usually updates, deletions of data, and insertions
Provides data sets for rapid retrieval, querying, and searching
Supports rapid processing with response times calculated in milliseconds
Remains available 24×7 with constant incremental backups
Moreover, many organizations use OLTP software systems to provide information for OLAP systems. In simple words, the combination of both is beneficial in our today’s data-driven world.
Let’s understand this with an example of an ATM. Suppose a couple has a joint account in a bank. One day both reach different ATMs simultaneously and try to withdraw the total amount present in the joint account.
In general, the person with a speedy hand will withdraw the money first. In this case, the OLTP software system ensures the withdrawn amount is less than the present amount in the bank. Thus, the key point here is OLTP systems are designed for transaction superiority rather than data analysis.
OLAP vs. OLTP: Working Principle
How does OLAP work?
OLAP helps in storing data in the data warehouses collected from different data sources. It then cleans and organizes the data into data cubes. Every OLAP cube includes data categorized by various dimensions, such as geographic sales region, time period, customers, etc., and derived by the dimension tables.
The data are organized hierarchically to help members find the required one easily. The data cubes are pre-summarized across the dimensions to enhance query time. Analysts perform five types of analytical operations against the multidimensional databases:
The OLAP system then locates the dimensions intersections, such as products sold in the western region at a certain price during a specific period, and displays the data.
How does OLTP work?
OLTP systems involve taking transactional information, processing data, and updating the back-end database to showcase the new input. While the applications are complex, these updates involve a few database records.
A relational database management system (RDBMS) controls and manages OLTP. OLTP requires a database to handle multiple updates and queries while supporting high-speed response times. This implies that RDBMS is a good option for OLTP systems.
Furthermore, OLTP is used for executing database transactions that front-end workers generate, including bank tellers and cashiers. Customer self-service apps like eCommerce, travel and online banking also generate online database transactions.
Usually, online transactional processing systems use a three-tiered architecture that consists of the application, data, and presentation tiers.
OLAP vs. OLTP: Characteristics
Characteristics of OLAP
The main features of OLAP are:
OLAP allows business owners to have a logical and dimensional view of data.
Provides multi-user support
Acts as a mediator between the front end and data warehouses
The results are stored separately from the data sources.
Offers a uniform documenting performance
Can distinguish between missing and zero values
Ignores missing values and computes the correct values
Facilitates complex analysis and interactive queries for the users
It can give you the power to perform intricate comparisons and calculations
OLTP software systems find their use in a wide range of markets. They are as follows:
Text messaging sending
Call center staff viewing
Updating customer details
Telemarketers recording survey results
Airline ticket booking
OLAP vs. OLTP: Benefits
Benefits of OLAP
OLAP is a useful tool for businesses to improve their knowledge of business sales, marketing, processes, and engagement. Having more data allows businesses to make more precise decisions. Let’s discuss some of the benefits of using OLAP:
Ad hoc reporting
High-speed data processing
Aggregated and detailed information
Familiar business expressions
‘What if’ scenarios
An almost flat learning curve
Business focussed calculations
Benefits of OLTP
The benefits include:
Complete business insight
Large database support
Expanded customer base
Easy data manipulation through data partitioning
It acts as a feeder for other databases like OLAP.
Allows decision-making at an upper level
User-friendly and convenient transactions
Broadens the customer base by attracting new customers
OLAP vs. OLTP: Limitations
Limitations of OLAP
Despite many advantages, OLAP systems have limitations too. The primary one is that it seems less attractive to many users. Other limitations include:
Poor computation capability
OLAP is relational
Always need pre-modeling
Shallow interactive analysis
Heavy dependency on IT
Can be slower
Limitations of OLTP
Like OLAP, there are some challenges or shortcomings in the OLTP systems as well based on their managing and designing systems. These limitations include:
Data silos and overload
Certain difficulties for small to medium businesses
Online transactions are affected if the system encounters hardware failures.
A low number of queries and updates
It needs employees to work in groups to maintain the list.
In case of server failure, a large number of data can be erased permanently, affecting the business in many ways.
OLAP vs. OLTP: Differences
The main difference between the two online processing systems is their purpose, i.e., analytical vs. transactional. Each system is optimized to make them work accordingly to help you make better business decisions in real time.
OLAP is designed to conduct complex data analyses and is used by data scientists, knowledge workers, and business analysts. On the other hand, OLTP is designed to process a large number of transactions and is used by frontline workers, such as bank tellers, cashiers, and hotel desk clerks.
Let us see the key differences between OLAP and OLTP in the table below.
It consists of historical data from different databases. Or, you can say it uses different OLTP databases as data sources.
It consists of current operational data.
It lets you extract information for complex analysis. The queries often involve a massive number of records to drive business decisions.
It is ideal for simple updates, deletions, and insertions in databases. Here, the queries involve one or a few records.
It allows users to create a view by using a spreadsheet. It is characterized by a massive volume of data.
It is easy and simple to create and maintain. It is characterized by large numbers of online transactions.
Transactions are less frequent but longer.
Transactions are very frequent, fast, and short.
Relatively slow due to the large volume of data. The queries may take hours.
The queries operate very fast.
Data integrity is an issue as the database doesn’t modify frequently.
It always needs to maintain data integrity constraints.
The processing time for complex queries is lengthy.
It is fast as compared to OLAP because of straightforward queries.
Tables are not normalized.
The tables are normalized.
Maximum read and rarely write operation.
Both write and read operations.
It is designed with a focus on a subject.
It is designed with a focus on the application.
Generally large because of large datasets.
Generally small if the historical data are being archived.
It can enhance the productivity of data analysts, executives, and business managers.
It can enhance the productivity of the end users.
Backup and Recovery
Lost data can be retrieved from the OLTP database.
To meet legal requirements and ensure business continuity, regular backups are required.
It offers fast results for regularly used data.
It ensures a quick response to the query.
Number of users
It allows thousands of users.
It allows hundreds of users.
Types of users
The ideal users are data scientists, CEOs, managers, top management, and others who need detailed insights into the whole data.
Clerks, DBA, database people, and others who need essential information are ideal users.
It is subject-oriented and is used for analytics, data mining, etc.
It is application-oriented and is used for business tasks.
Choosing the right data processing software system ultimately depends upon your goal or objectives.
OLAP can help unlock value from large amounts of data, while OLTP can help you process large numbers of transactions quickly. The traditional OLAP tools need data-modeling expertise along with cooperation across different business units. On the other hand, OLTP systems are business-critical.
In many cases, organizations use OLAP and OLTP systems together. This means OLAP systems are used to analyze data that can help in improving business processes in OLTP systems.
Thus, you can choose one of them based on your data analysis or transaction needs. But if you require both functionalities, using both OLAP and OLTP will be the best.
Durga Prasad Acharya
Durga Prasad Acharya is a Freelance Technical Writer who loves writing on emerging technologies, such as AI & ML, Cybersecurity, Web Hosting, SaaS, Cloud Computing, and more. Besides writing, he’s a web designer and is passionate about… read more
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