These terms are often confused with each other, so what are their key differences and how do you choose the right one for your situation?
We live in a data-driven era, where organizations that use data to make smarter decisions and respond faster to changing needs are more likely to rise to the top. You can see this data at work in new service offerings (such as ride-sharing apps) as well as in the powerful systems that power retail (both e-commerce and in-store transactions).
Within the field of data science, there are two types of data processing systems: online analytical processing (OLAP) and online transaction processing (OLTP). The main difference is that one uses data to gain valuable insights, while the other is purely operational. However, there are significant ways to use both systems to solve data problems.
The question is not which one to choose, but how to make the best use of both types of processing for your situation.
What is OLAP?
Online analytical processing (OLAP) is a system for performing high-speed multidimensional analysis on large volumes of data. Typically, this data comes from a data warehouse, data mart, or some other centralized data warehouse. OLAP is ideal for data mining, business intelligence, and complex analytical calculations, as well as enterprise reporting features such as financial analysis, budgeting, and sales forecasting.
The core of most OLAP databases is the OLAP cube, which allows you to quickly query, report, and analyze multidimensional data. What is a data dimension? It is simply an element of a particular data set. For example, sales figures can have several dimensions related to region, time of year, product models, and more.
The OLAP cube extends the column-by-column formatting of a traditional relational database schema and adds layers for other data dimensions. For example, while the top layer of the cube can organize sales by region, data analysts can also “drill down” into layers for sales by specific state/province, city, and/or stores. This aggregated historical data for OLAP is usually stored in a star schema or a snowflake schema.
following graphic shows the OLAP cube for sales data in several dimensions: by region, by quarter, and by product:
What is OLTP? Online
transactional processing (OLTP) enables the real-time execution of a large number of database transactions by a large number of people, typically over the Internet. OLTP systems are behind many of our daily transactions, from ATMs to in-store purchases to hotel reservations. OLTP can also drive non-financial transactions, including password changes and text messages.
OLTP systems use a relational database that can do the following:
- Process a large number of relatively simple transactions, usually insertions, updates, and deletions of
- Enable multi-user access to the same data, while ensuring data integrity
- It supports very fast processing, with response times measured in milliseconds
- Provide indexed datasets for searches, retrievals, and quick queries.
- Be available 24/7/365, with constant incremental backups.
Many organizations use OLTP systems to provide data for OLAP. In other words, a combination of OLTP and OLAP is essential in our data-driven world.
difference between OLAP and OLTP: Type of processing The main
distinction between the two systems is in their names: analytical vs. transactional. Each system is optimized for that type of processing.
OLAP is optimized for complex data analysis for smarter decision making. OLAP systems are designed to be used by data scientists, business analysts, and knowledge workers, and support business intelligence (BI), data mining, and other decision support applications.
OLTP, on the other hand, is optimized to process a large number of transactions. OLTP systems are designed to be used by frontline workers (e.g., tellers, bank tellers, hotel front desk clerks) or for customer self-service applications (e.g., online banking, e-commerce, travel reservations).
Other key differences between OLAP and OLTP
- Focus: OLAP systems allow you to extract data for complex analysis. To drive business decisions, queries often involve a large number of records. In contrast, OLTP systems are ideal for performing simple updates, insertions, and deletions in databases. Queries usually involve only one or a few records.
- Data source: An OLAP database has a multidimensional schema, so it can support complex queries of various current and historical data data. Different OLTP databases can be the aggregated data source for OLAP, and they can be organized as a data store. OLTP, on the other hand, uses a traditional DBMS to accommodate a large volume of transactions in real time.
- Processing time: In OLAP, response times are orders of magnitude slower than OLTP. Workloads are read-intensive, involving huge data sets. For OLTP transactions and responses, every millisecond counts. Workloads involve simple read and write operations through SQL (Structured Query Language), which require less time and less storage space.
- Availability: Because they do not modify current data, OLAP systems can be backed up less frequently. However, OLTP systems modify data frequently, as this is the nature of transactional processing. They require frequent or simultaneous backups to help maintain data integrity.
OLAP vs. OLTP: Which is best for you
Choosing the right system for your situation depends on your goals. Do you need a single platform for business insights? OLAP can help you unlock value from large amounts of data. Do you need to manage day-to-day transactions? OLTP is designed for fast processing of a large number of transactions per second.
Keep in mind that traditional OLAP tools require data modeling expertise and often require cooperation between multiple business units. In contrast, OLTP systems are business-critical, and any downtime results in disrupted transactions, lost revenue, and damage to your brand reputation.
Most of the time, organizations use OLAP and OLTP systems. In fact, OLAP systems can be used to analyze data that leads to business process improvements in OLTP systems.
Learn more about OLAP and OLTP
Online processing systems are behind the business decisions and data transactions that drive our daily lives. To learn more about the database systems used with OLAP and OLTP, we recommend that you explore the Learning Center articles on these topics. We also recommend checking out IBM’s content on relational databases and their use cases for OLTP, IoT solutions, and data warehousing for OLAP.
- What is OLTP?
- Use cases for relational databases
To learn more about integrating data for faster queries and more intuitive insights, read our eBook on IBM Db2: The AI Database.