OLAP vs. OLTP: The Differences

Before data can be put to use, it must be processed. Online analytical processing (OLAP) and online transactional processing (OLTP) are the two primary data processing systems used in data science. The key difference between OLAP and  OLTP is that OLAP is used for complex data analysis, while OLTP is used for real-time processing of online transactions at scale. Although each one’s purpose and method of processing data are different, OLAP and OLTP systems are both valuable for solving complex business problems. Let’s dive deeper into the differences between OLAP and OLTP and look at various applications of each that illustrate their use cases.

Key Differences Between OLAP vs. OLTP

While each of these processes are distinctly different, organizations are not typically making a decision between OLAP and OLTP. Some teams have a need for one or the other, while others need both. Here are the key differences between the two.

Purpose

The main distinction between OLAP vs. OLTP is the core purpose of each system. An OLAP system can process large amounts of data quickly, enabling in-depth data analysis across multiple dimensions for decision-making and problem-solving. Teams can use this data for decision-making and problem-solving. 

In contrast, OLTP systems are designed to handle large volumes of transactional data involving multiple users. Relational databases rapidly update, insert, or delete small amounts of data in real time. Most OLTP systems are used for executing transactions such as online hotel bookings, mobile banking transactions, ecommerce purchases, and in-store checkout. Many OLAP systems pull their data from OLTP databases via an ETL pipeline and can provide insights such as analyzing ATM activity and performance over time. 

Simply put, organizations use OLTP systems to run their business while OLAP systems help them understand their business. 

Data sources

OLAP’s multidimensional schema is well suited for complex queries that draw from multiple data sets, such as historical and current data, including from OLTP sources as mentioned. An OLTP system stores transaction data in a relational database, optimized to handle the large volumes of transactional data funneled into this system. 

Updates and backups

OLAP systems are designed to process queries that include thousands to millions of rows of data. Data is updated hourly to daily depending on the needs of the organization. In contrast, OLTP systems typically update a few rows of data at a time in real time or near real time. OLTP systems are also backed up much more frequently than OLAP systems—due to OLTP’s nature as a transaction processing tool, regular backups are required to maintain business operations and comply with relevant legal and regulatory requirements. Any data loss incurred in an OLAP system can be remedied by simply reloading the lost data from the original source. 

Speed

By definition, OLTP systems have response times that are measured in milliseconds. OLAP databases process significantly more data, so their response times are slower. Depending on the technology used and the amount of data being processed, response times for an OLAP system can range from a second to several hours. 

Data storage capacity

Providing that historical transaction data is archived, OLTP systems have relatively modest data storage requirements. In contrast, OLAP systems require massive amounts of data storage capacity to function. The sheer size of the aggregated data required in OLAP applications requires the use of a modern cloud data warehouse that can accommodate massive storage requirements. 

Intended users

OLTP systems are customer-facing and designed for use by frontline workers such as store clerks and hotel reservation specialists as well as online shoppers. OLAP systems are business-facing and are used by data scientists, analysts, and business users such as team leads or executives. These decision-makers access data using analytics dashboards.

What are OLTP Applications

OLTP databases play a vital role in supporting a wide array of critical business and consumer applications, facilitating seamless and real-time transaction processing. In business scenarios, these databases are responsible for powering various essential tasks, such as call center staff accessing customer information instantly during a phone call, efficient order entry and management within warehouses, processing online financial transactions securely, and handling various other time-sensitive operations.

The significance of OLTP extends to consumer applications as well, where its swift data processing capabilities enable smooth user experiences. Consumers benefit from OLTP-powered applications in several ways, including sending text messages instantaneously, booking vacation rentals with real-time availability checks, and making quick and hassle-free purchases from online meal delivery services, enhancing convenience and user satisfaction.

By leveraging OLTP databases, businesses can ensure rapid response times and reliable data handling for their day-to-day operational needs, while consumers can enjoy smooth interactions and seamless transactions in their online experiences. These databases form the backbone of many critical systems, ensuring that real-time data requirements are met efficiently and effectively in various domains of business and consumer interactions.

What are OLAP Applications

Unlike OLTP, OLAP is used to query data to analyze it. Organizations across industries use OLAP to power their businesses, including healthcare, manufacturing, and advertising. 

Healthcare sector: OLAP's ability to drill down into data sets allows healthcare providers to conduct in-depth analyses of health outcomes using a multitude of data dimensions. They can explore crucial factors like the length of hospital stays, healthcare providers involved, medical diagnoses, and patient demographic data. This enables them to gain valuable insights, improve patient care, and make informed decisions for better health outcomes.

Manufacturing: OLAP's versatile capabilities provide rich insights into various aspects of operations. Manufacturers can delve into individual customer and product profitability, facilitating better strategic planning and resource allocation. Additionally, OLAP aids in supply and demand forecasting, allowing manufacturers to optimize their production processes and inventory management. The variance analysis provided by OLAP helps identify areas for improvement, streamlining operations and enhancing overall efficiency.

Advertising: OLAP plays a pivotal role in processing customer data and enabling detailed analysis. Advertisers leverage OLAP's capabilities to gain a deeper understanding of customer behavior, churn patterns, and engagement levels. Armed with this knowledge, they can refine their advertising strategies, tailor campaigns to specific target audiences, and work towards increasing customer lifetime value, ultimately driving higher returns on their marketing investments.

The versatility and analytical prowess of OLAP empower organizations in multiple domains, providing them with actionable insights and a competitive edge in an ever-evolving business landscape. Its widespread adoption across industries is a testament to its effectiveness in transforming raw data into valuable knowledge, supporting better decision-making and driving overall business success.

Snowflake for Transactional and Analytical Data

Part of the Data Cloud, Snowflake Unistore offers a modern approach to working with transactional and analytical data together in a single platform. With Unistore, teams can break down these traditionally silo'd data types and build transactional business applications directly on Snowflake, run real-time analytical queries on transactional data, and leverage a unified approach to governance and security.

See Snowflake’s OLAP capabilities for yourself. To give it a test drive, sign up for a free trial.