When talking about relational database management systems, there are 2 concepts that must be understood in order to accomplish the best out of these systems. The best involves the 5 Vs (velocity, volume, veracity, value and variety).
These 2 concepts are OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing). During the process of design, these concepts must be considered because depending on what the database will be used for, the wrong design, will be costly.
OLTP vs OLAP
What is OLTP?
OLTP stands for Online Transaction Processing. It is a database system that should support transaction-based operations. The OLTP database should capture every transaction of an organization to keep records.
OLTP are intense on INSERTS, UPDATES AND DELETES. The main focus should be to accommodate those types of operations in a fast and reliable way.
It focuses on query processing and maintaining data integrity. OLTP enables the effectiveness measured by the total number of transactions per second.
In other words, OLTP ensures the real-time execution of a large number of online database transactions.
Understanding the concept of a ‘transaction is also important.’ In database terms, a transaction is considered a change. It can be the insertion, modification, or deletion of a query. Basically, every operation that impacts the database is a transaction.
OLTP systems drive many, if not all of the financial transactions that we regularly make. These include online banking, ATM transactions, in-store purchases, e-commerce, and service booking.
But that’s not it! An OLTP can also drive non-financial database transactions. These can be either text messages or password changes. Among the many properties of a database transaction, OLTP focuses on atomicity.
It means ‘all or none.’ Atomicity refers to whether a transaction should complete successfully or fail and roll back. This property does not allow leaving a transaction in a pending state.
Characteristics of OLTP
Let’s look at the various characteristics of the OLTP system to get the broader picture.
Processes Simple Transactions
The OLTP system processes only simple transactions, such as insertion, deletion, or updating data. However, it can handle a large number of such transactions simultaneously.
Indexed Data Sets
OLTP provides Indexed data sets, which are used for rapid searching, querying, as well as retrieval. However, this should be kept to a minimal possible. Indexes may also hurt these transactions capabilities. So, well thought indexes are a MUST in OLTP
No Downtime
OLTP systems are responsible for handling an organization’s transactions. Therefore, downtime can have a huge negative impact. OLTP systems are available with backups so that they can be available 24/7/365.
Rapid Processing
OLTP systems are very effective with rapid processing. Their response time is measured in milliseconds.
Concurrency Control
OLTP systems enable multi-user access to the same data. They are based on concurrency algorithms to ensure that data remains consistent even when multiple users are accessing it simultaneously.
The best example of that is an online hotel booking. Due to the OLTP system, a user can not reserve a booked room.
Normalization in OLTP Systems
In database systems, normalization refers to the process of removing anomalies. With this, we can achieve a database with no or minimized data redundancy. OLTP systems should be HIGHLY normalized.
Normalization helps to maintain consistency and eliminates data anomalies and redundancy.
What is OLAP?
OLAP stands for Online Analytical Processing. It is a classification of software that is used for analytical purposes and can have many different sources and multiple DBMS systems at the same time. It enables analysts to view business insights and real-time data from various points of view.
Databases used as Data warehouse are usually a great example of OLAP systems. Data warehouses may hold data from all different systems and sources.
OLAP are created based on the extraction of data from several sources. These external sources for extraction can be, but are not limited to, other OLAP, OLTP, files, APIs data lakes and so forth.
Usually, business analysts use OLAP to group and join data. Aggregating operations can be very helpful, especially for data mining. Using OLAP, one can pre-calculate and pre-aggregate data. This way, you can speed up the process several times for reporting and insights.
Characteristics of OLAP
Here are all the characteristics of the OLAP system:
Multi-User Support
An OLAP system is shared, so it provides multi-user support. It provides data retrieval, update, normal database operations, integrity, security, and update.
Multi-Dimensional Conceptual View
Another ‘multi’ characteristic of OLAP is its multi-dimensional conceptual view. It allows businesses to have a dimensional and logical view of the data warehouse.
OLAP is most popular for its popular slice and dice multi-dimensional operations. These operations help an organization in decision-making.
A Medium
OLAP serves as a medium between data warehouses and the front-end. Thus, it keeps the data sources and OLAP front-end connected.
Deep Analysis
OLAP defines the method to access and extract business logic and analysis data. This data is relevant to the function as well as the user.
Although it requires some programming to define applications, it provides calculation flexibility.
Through OLAP, users can pre-calculate and pre-aggregate data, as mentioned earlier. Thus, it proves that the system has a deep analytic nature.
Scalable and Meaningful
OLAP allows users to increase the database size without degrading the performance. You can also add more dimensions, and it still offers efficient results. These results can be charts and graphs.
Accurate
OLAP can perform tricky calculations and comparisons. Plus, it always produces precise results. It always computes correctly, thanks to its understanding of the difference between zero and missing values.
Normalization in OLAP Systems
Normalization in OLAP systems is a bit more complicated than OLTP. That is because of its star schema. In this, tables are mostly in 1NF. On the other hand, the dimension tables are generally in 2NF.
For normalization, you can decompose dimension tables. This way, they can end up in 3NF. Generally, a star schema is difficult to perform both normalization and denormalization.
The term ‘denormalization’ occurs when you have fewer joins.
However, the OLAP system does not maintain data integrity since it should be normalized.
- A Detailed Comparison on OLTP vs. OLAP
| OLTP | OLAP |
| OLTP can handle a large number of simple transactions. | OLAP handles a large number of complex queries to perform operations on data. |
| It works with simple, standardized queries, such as insert, delete, and update. | It works with complex queries to analyze data. |
| It can perform several basic operations. | It is based on only the SELECT command to aggregate and joins data. |
| OLTP is fast and has an impressive response time in milliseconds. | OLAP can take seconds, minutes, up to hours, depending on the amount of data to process. |
| The OLTP system has an industry-specific design, like banking or manufacturing. | The OLAP system has a subject-specific design. These can include inventory, sales, and marketing. |
| It can help a business to control and run essential operations. | It can help a business to strategize, solve problems, decision-making, and discover insights. |
| OLTP has short and fast data updates. These are usually initiated by the user. | OLAP periodically refreshes data with scheduled and long-running jobs. |
| The source for OLTP is the transactions. | The source for OLAP is the aggregated data from transactions. |
| OLTP requires regular backups to ensure integrated data for a business. | OLAP can restore the lost data from the OLTP database. |
| It monitors and lists regular business transactions. | The multi-dimensional view of enterprise data can be accessed and extracted by OLAP. |
Normalization in OLTP vs. OLAP
As mentioned earlier, normalization helps remove data redundancy. Thus, it makes a database consistent. But, what’s the difference between Normalization in OLTP vs. OLAP?
OLTP does not require normalization. That is because it already has a fully normalized schema. It offers normalized databases for maximum efficiency and consistency. Its database designs contain minimum data redundancy. Thus, it is much faster in its response time. All the tables in the OLTP database are in 3NF (Third Normal Form).
On the other hand, OLAP does not have normalization. Its tables are not normalized, thus, making it less efficient in terms of data redundancy. Due to this issue, the response time can also increase exponentially with the data. Hence, OLAP contains denormalized databases that are used for analysis.
Availability
OLTP systems can modify data frequently. That is because of the nature of transactional processing. There are numerous transactions made by an organization in a day. Thus, the data should be saved with each new one as it is the only way users can get consistent data.
Plus, concurrency also plays a key role in transactional processing. Suppose multiple users are reading the same data while one user is changing it. Hence, the readers should get the updated data. It is also possible due to the quick timing of OLTP.
In contrast, OLAP systems do not require that frequent modification. Since they do not make changes to the data in real-time, it is easy for users to keep up without updating. Another benefit is that it requires less frequent backups too. The rate of maintainability is also observed to be lower than OLTP.
Data Source
OLAP databases has a multi-functional schema. It can support complex queries of both current and historical data. OLAP also considers OLTP databases as the source of aggregated data. These databases can be organized as a data warehouse.
OTLP, however, uses a convensional DBMS. It can store a large number of real-time transactions. Therefore, it uses these transactions as its data source.
- OLTP vs. OLAP: Which One is Better?
A frequently asked question about OLTP vs. OLAP is, which one is better? Well, there’s no straightforward answer. Choosing the right system depends on your organization’s requirements and usage.
OLTP is designed to process a large number of transactions per second. Hence, businesses that need to monitor their online transactions should opt for this system. These are well-suited to fit your organization’s needs. They are fast, do not face downtime, can modify data frequently, and manage your transactions on a daily basis.
However, OLTP also has some downsides. A single downtime can disrupt your business transactions on a significant level. Moreover, lost data can have major consequences that can impact your organization.
OLAP is the ultimate system to provide all-important business insights. It can grant you access to a large amount of data, historical and current. It can process complex queries, offers a multi-dimensional view, restores lost data, and has a subject-specific design.
Similarly, OLAP also has some downsides. For instance, it is denormalized, unlike OLTP. Plus, it is slower and can only aggregate data.
In the end, it all comes down to your business needs to figure out which one suits you better. Many companies use the best of both OLTP and OLAP. In fact, combining both can lead to a significant business process improvement. That is because OLAP systems can use OLTP databases as their data source.
Conclusion
The key point that distinguishes the two systems is in their names. An OLTP is an online transaction system. In comparison, an OLAP is an online data analysis and retrieval system.
And, that covers all you should know about OLTP vs. OLAP. We have covered all the aspects, including their definitions, characteristics, and differences. Another thing that confuses a lot of people is the concept of Normalization among these two systems. We have also discussed that to provide a clearer picture. Are you still confused about which system you should choose for your business? Make sure to note down all your requirements and compare which one fits them better from OLTP vs. OLAP. If your business is capable, you can combine both!