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. OLAP is designed to analyze multiple data dimensions at once, helping teams better understand the complex relationships in their data. This system is ideal for uncovering valuable business insights. OLTP is a simple transactional system ideal for handling 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.
The main distinction between OLAP vs. OLTP is the core purpose of each system. An OLAP system is designed to process large amounts of data quickly, allowing users to analyze multiple data dimensions in tandem. 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.
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.
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.
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.
OLTP databases power a range of important business and consumer applications. Business applications include call center staff accessing customer information during a phone call, warehouse order entry, taking orders by phone, and processing online financial transactions. Consumer applications include sending a text message, booking a vacation rental, or purchasing dinner from an online meal delivery service.
Healthcare: OLAP’s ability to drill down into data sets makes it possible for healthcare providers to analyze health outcomes using a range of data dimensions including length of hospital stay, provider, medical diagnosis, and patient demographic data.
Manufacturing: These same capabilities provide rich insights into manufacturing operations by individual customer and product profitability, supply and demand forecasting, and variance analysis.
Advertising: Advertisers use OLAP to process customer data to analyze churn, improve engagement, and increase customer lifetime value.
Snowflake for Transactional and Analytical Data
Part of the Data Cloud, Snowflake Unistore offers 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.