These terms are often confused with each other, so what are their main differences? How do you choose the right term for your situation?
We live in a data-driven age, and organizations that use data to make smarter decisions and respond faster to changing needs are more likely to excel. You can see this data in new service offerings, such as ride-sharing apps, and in the powerful systems that drive retail (e-commerce and in-store transactions).
In 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 actionable. However, there are some meaningful ways to use both systems to solve data problems.
The question is not which to choose, but how to get the best of both processing types for your situation.
What is OLAP?
Online Analytical Processing (OLAP) is a system for high-speed multidimensional analysis of large amounts of data. Typically, this data comes from a data warehouse, data mart, or some other centralized data store. OLAP is ideal for data mining, business intelligence, and complex analytical calculations, as well as business reporting functions such as financial analysis, budgeting, and sales forecasting.
At the heart of most OLAP databases is the OLAP cube, which allows you to quickly query, report, and analyze multidimensional data. What are data dimensions? It is just one element of a particular dataset. For example, sales data might have multiple dimensions related to region, time of year, product model, and so on.
OLAP cubes extend the column-wise format of traditional relational database schemas and add layers for additional data dimensions. For example, while the top layer of the cube might organize sales by region, a data analyst can also "drill down" to layers that have sales by state/province, city, and/or specific store. This historical aggregate data for OLAP is usually stored in a star schema or snowflake schema.
The following diagram shows an OLAP cube for multidimensional sales data - by region, by quarter and by product:
What is OLTP?
Online transaction processing (OLTP) enables large numbers of people (usually over the Internet) to perform large numbers of database transactions in real time. OLTP systems support many of our daily transactions, from ATMs to in-store purchases to hotel reservations. OLTP can also facilitate non-financial transactions, including password changes and text messages.
OLTP systems use relational databases that can:
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Handles a large number of relatively simple transactions—typically insertions, updates, and deletions of data.
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Enable multi-user access to the same data while ensuring data integrity.
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Supports very fast processing, with response times measured in milliseconds.
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Provides indexed datasets for fast search, retrieval and query.
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Available 24/7/365 with continuous incremental backups.
Many organizations use OLTP systems to provide data for OLAP. In other words, the combination of OLTP and OLAP is essential in our data-driven world.
Key Differences Between OLAP and OLTP: Types of Processing
The main difference between the two systems is their names: analytical vs. transactional. Each system is optimized for that type of processing.
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OLAP is optimized for complex data analysis to make smarter decisions. OLAP systems are designed for use by data scientists, business analysts, and knowledge workers, and they support business intelligence (BI), data mining, and other decision support applications.
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OLTP, on the other hand, is optimized for handling a large number of transactions. OLTP systems are designed for frontline workers (such as cashiers, bank tellers, hotel front desk clerks) or customer self-service applications (such as online banking, e-commerce, travel booking).
Other key differences between OLAP and OLTP
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Important: OLAP systems allow you to extract data for complex analysis. Queries often involve large numbers of records in order to drive business decisions. In contrast, OLTP systems are well suited for simple updates, insertions, and deletions in databases. Queries usually involve only one or a few records.
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Data sources: OLAP databases have a multidimensional schema, so it can support complex queries on multiple data facts from current and historical data. Different OLTP databases can be used as sources of OLAP aggregate data, and they can be organized as a data warehouse. OLTP, on the other hand, uses traditional DBMSs to accommodate high volumes of real-time transactions.
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Processing time: In OLAP, the response time is orders of magnitude slower than in OLTP. Workloads are read-intensive and involve large datasets. When it comes to OLTP transactions and responses, every millisecond counts. Workloads involving simple read and write operations via SQL (Structured Query Language) require less time and less storage space.
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Availability: Since they do not modify current data, OLAP systems can be backed up less frequently. However, OLTP systems frequently modify data because of the nature of transaction processing. They require frequent or concurrent backups to help maintain data integrity.
OLAP vs. OLTP: Which is Right 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 massive amounts of data. Do you need to manage your daily transactions? OLTP is designed to quickly process large numbers of transactions per second.
Note that traditional OLAP tools require data modeling expertise and often require collaboration across multiple business units. In contrast, OLTP systems are business-critical, and any downtime can result in disrupted transactions, lost revenue, and damaged brand reputation.
Most of the time, organizations use both OLAP and OLTP systems. In fact, OLAP systems can be used to analyze data leading 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 power our daily lives. To learn more about database systems used with OLAP and OLTP, we encourage you to browse the Learning Center articles on these topics. We also recommend viewing IBM content on relational databases and their use cases for OLTP, IoT solutions, and OLAP data warehouses.
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What is OLAP?
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What is OLTP?
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Use Cases for Relational Databases
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