Data is utilized in almost all domains today, from designing products/services to delivering top-notch customer services. There are two approaches to processing data for business operations: OLAP and OLTP. OLAP is used for complex querying using a structured language, while OLTP facilitates real-time online transactions at scale. Although both are data processing systems, they differ in how they process the data and their use cases.
In this article, let’s explore the key differences between OLTP vs OLAP and how to choose the right solution for your organization’s needs.
What is OLAP?
Anyone who has worked with data will know spreadsheets. Spreadsheets have rows and columns that store information about a record, showing you only the two-dimensional relationship within your data. Moreover, as data grows, you will need more and more spreadsheets. Especially when tracking numerous business parameters and analyzing data across different domains, spreadsheets alone are not enough.
But OLAP is different! Unlike traditional tables, it stores data in a multi-dimensional structure called cubes, providing unified access to your entire data. Users can slice or dice the cube by applying filters to get a subset of data for analysis.
Key features:
- OLAP provides a multi-dimensional view of the data.
- Integrates data from multiple sources and offers a consistent view.
- OLAP systems seamlessly integrate with BI tools like Power BI and Looker.
- Provides access to only authorized data, ensuring private data is safe.
- Indexing and pre-aggregated data offer faster query performance.
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What is OLTP?
OLTP stands for online transaction processing. It is a real-time database system commonly used in applications where multiple users execute different transactions concurrently. Examples include everyday activities like online banking, ATMs, text messages, and hotel or flight bookings.
Critical real-time transactions, like money transfers, require accuracy and consistency. To ensure this, OLTP operates on RDBMS principles that support ACID properties. It provides multi-user access while maintaining data consistency across systems.
Moreover, as transactional operations occur frequently, the goal is to enable fast processing for large volumes of transactions, all while ensuring data integrity.
Key features:
- Enables execution of numerous real-time transactions simultaneously.
- Offers robust ACID compliance.
- Provides high availability with zero downtime.
- Optimized for simple and small daily transactions.
- Ensures quick recovery in case of failures
What are the similarities between OLAP and OLTP?
Both OLAP and OLTP are built on top of RDBMS to store and manage large volumes of data effectively, allowing them to be queried, manipulated, and analyzed using SQL.
They are to process large volumes of data: OLAP for handling complex queries on historical data and OLTP to support numerous small scale transactions in real-time.
Additionally, both systems include security mechanisms for access control and user authentication to ensure privacy.
OLTP vs OLAP: Key Differences
Aspect | OLTP | OLAP |
Stands for | OLTP stands for online transactional processing. | OLAP stands for online analytical processing. |
Type of data | Contains real-time operational data | Contains massive historical data |
Characteristic | Handles large number of everyday transactions | Handles large volumes of data with complex queries |
Type of operations | Runs essential business operations on a regular basis | Involved in business decision-making through data-driven insights |
Size of data | It handles comparatively smaller size data | Size of the data handled is typically large ie., TB or PB |
Speed | Faster processing due to straightforward transactions | Comparatively slower as it handles complex queries for analysis |
Updates | OLTP systems are real-time or near real-time sync | Updated periodically according to the business needs |
Backups | Regularly backed up to enable instant rollback in case of failed transactions. | Less frequently backed up compared to OLTP |
Data storage | Comparatively smaller data storage required | Requires massive data storage capacity to maintain historical data |
Normalization | Normalized to eliminate redundant data | Not normalized because the primary goal is to optimize analytical queries |
Examples | Banking applications, e-commerce platforms, and hotel bookings | Sales analysis and financial reporting |
Users | Operational staff such as cashiers and customer service representatives | Data analysts, business analysts, and decision scientists |
When to Use Which?
Businesses today generate enormous amounts of data each day. If you don’t leverage this data to make decisions, you will be out of competition soon. That’s where OLAP databases come in. OLAP databases store enormous amounts of business data and make it available for your data teams to analyze and generate patterns. You can use OLAP databases in any of the following scenarios.
- To store and process large amounts of historical data
- To drive data-driven insights
- Study customer behavioral patterns and optimize your product/services accordingly.
- To update data periodically instead of in real-time.
- To implement complex analytical queries
- To provide a unified view of your business data
In contrast, real-time business applications are required to execute multiple transactions concurrently. That’s the main focus area of OLTP databases. Here are some scenarios where OLTP is used:
- Everyday operational applications like ATM or ticket booking transactions
- For faster transactional processing
- When you need continuous backup policies
- In applications that should strictly comply with ACID properties
- In multi-user applications
- When frequent updates to databases are required
Conclusion
In this blog, you read about the major differences between OLTP vs OLAP. In summary, high throughput applications use OLTP to enable faster transactions, while OLAP is used to analyze historical data and improve business decisions.
Typically, businesses today require both OLAP and OLTP systems due to the rise in modern applications and data demands. By utilizing OLAP systems, they can leverage data analysis, machine learning, and predictive analytics in their business operations. And with OLTP, real-time transactions can be processed with high concurrency and ACID compliance. To migrate your data in real time, Hevo is the go-to solution for many users. Sign up for Hevo’s 14-day free trial and start experiencing the ease of real-time data sync.
FAQs
1. What is the difference between OLAP and OLTP?
While OLTP emphasizes real-time transactional processes, OLAP focuses on analyzing trends in large volumes of historical data.
2. Is SQL Server OLAP or OLTP?
SQL Server enables both OLAP and OLTP operations. It has the ACID properties to handle real-time transactions and also supports complex SQL queries.
3. Is Snowflake an OLAP or OLTP?
Snowflake is primarily designed for OLAP operations, designed for data warehousing and analytical purposes.
4. What is OLTP with an example?
Any transaction you perform online in real time application is an example of OLTP. For instance, paying online on an e-commerce platform is an OLTP transaction because it involves immediate database updates, concurrency(numerous users buying at the same time), and atomicity(if payment is failed, the entire operation should be aborted).
Srujana is a seasoned technical content writer with over 3 years of experience. She specializes in data integration and analysis and has worked as a data scientist at Target. Using her skills, she develops thoroughly researched content that uncovers insights and offers actionable solutions to help organizations navigate and excel in the complex data landscape.