Online Transaction Processing (OLTP) and Online Analytic Processing (OLAP)

Online Transaction Processing (OLTP): OLTP databases are meant to be used to do many small transactions, and usually serve as a “single source of storage”. An example of OLTP system is online movie ticket booking website. Suppose two persons at the same time wants to book the same seat for the same movie for same movie timing then in this case whoever will complete the transaction first will get the ticket. The key thing to note here is that OLTP systems are designed for transactional priority instead data analysis. Figure – Pictorial Representation of OLTP

Benefits of using OLTP services:-

• The main benefit of using OLTP services is it responds to its user actions immediately as it can process query very quickly.
• OLTP services allows its user to perform operations like read, write and delete data quickly.
• Consistency: OLTP services ensure the consistency of data in real-time. Any changes made by one user will reflect immediately and accurately for all other users.
• Data Integrity: OLTP services maintain data integrity by validating the input data to ensure that it conforms to the specified rules and constraints.
• High Availability: OLTP services ensure high availability by providing real-time access to the data. They are designed to handle a large number of users and transactions without affecting the system’s performance.
• Scalability: OLTP services are highly scalable and can handle an increasing number of users and transactions. This makes them ideal for applications that require real-time access to the data.
• Security: OLTP services provide high levels of security by implementing various security features such as authentication, authorization, and encryption to ensure that only authorized users can access the data.
• Better Decision Making: OLTP services provide real-time access to data, allowing users to make better and informed decisions. This is because the data is accurate, up-to-date, and reliable.

Drawbacks of OLTP service:-

• The major problem with the OLAP services is it is not fail-safe. If there is hardware failures, then online transactions gets affected.
• OLTP allow users to access and change the data at the same time which cause unprecedented situation.
• Limited analysis capabilities: OLTP systems are designed to handle operational tasks and not intended for complex analysis or reporting. They lack the ability to aggregate and analyze large amounts of data quickly.
• Limited scalability: OLTP systems are not easily scalable and may require significant infrastructure changes to handle increased transaction loads. This can result in costly downtime and impact on user experience.
• Data integrity issues: With many users accessing and modifying data concurrently, OLTP systems may experience issues with data integrity, such as duplicate or inconsistent data.
• High maintenance costs: OLTP systems require frequent maintenance, including backup and recovery procedures, to ensure data is not lost and the system is available to users at all times. This can result in high maintenance costs for organizations.

Online Analytic Processing (OLAP): OLAP databases on the other hand are more suited for analytics, data mining, less queries but they are usually bigger (they operate on more data). We can say that any Datawarehouse system is an OLAP system. Many company compare their sales of current month with previous month to keep trace of business. Here company compare the sales and keep the result in another location, which is a separate database. Here company uses OLAP databases. Figure – Pictorial Representation of OLAP

Benefits of using OLAP services:-

• The main benefit of using OLAP services is it helps to keep trace of consistency and calculation.
• OLAP builds one single platform where we can store planning, analysis and budgeting for business analytics.
• With the OLAP as service, we can easily apply security restrictions to protect data.
• OLAP services provide a unified view of data from different sources, making it easier for business analysts to access and analyze data.
• OLAP services enable complex queries and data analysis by providing a multidimensional view of data, allowing users to slice and dice data in various ways.
• OLAP services support data mining and predictive analytics by providing access to historical data and trends, allowing users to identify patterns and make informed business decisions.
• OLAP services can handle large volumes of data, making it suitable for enterprise-level business intelligence applications.
• OLAP services can be integrated with other tools and applications, such as reporting and visualization tools, to provide a complete business intelligence solution.

Drawbacks of OLAP service:-

• The major problem with the OLAP services is it always needs IT professionals to handle the data because OLAP tools require a complicated modeling procedure.
• As mentioned in the benefits of using OLAP services, we can use OLAP as a single platform where we can store planning, analysis and budgeting for business analytics but here we need help of different departments at one time i.e., OLAP tools need cooperation between people of various departments, which leads dependency problem.
• OLAP services can be very expensive to implement and maintain, especially for large datasets.
• There may be a delay in data availability as data needs to be extracted, transformed, and loaded into the OLAP system before it can be analyzed.
• OLAP services are optimized for read-heavy workloads, so write operations may be slower or less efficient.
• OLAP services may not be suitable for real-time analysis or decision-making as data is typically updated on a periodic basis.
  

The key differences between OLTP and OLAP databases:

OLTP	OLAP
OLTP is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE).	OLAP is characterized by relatively low volume of transactions.
OLTP queries are simple and easy to understand.	OLAP Queries are often very complex and involve aggregations.
OLTP is widely used for small transaction.	OLAP applications are widely used by Data Mining techniques.
OLTP is highly normalized.	OLAP is typically de-normalized.
OLTP is used for Backup religiously.	OLAP is used for regular backup.
OLTP usually uses schema used to store transnational databases is the entity model (usually 3NF).	OLAP uses star model to store the data.
Performance of OLTP is comparably fast as compared to OLAP.	Performance of OLAP is comparably low as compared to OLTP.

OLTP and OLAP services are different from each other, therefore, it is wise to look into the differences and use them wisely as per your application/need demands.