Solution overviews

Db2 stands out as a premier enterprise-grade database solution, purpose-built to handle the most demanding transactional workloads while seamlessly supporting advanced analytical processing. With its robust architecture, Db2 delivers exceptional scalability, reliability, and performance, making it the ideal choice for organizations that require real-time data integrity and deep insights from complex queries.

Common database workloads

Transactional and analytic database workloads serve distinct purposes in data management.

Online Transactional Processing (OLTP) workloads: OLTP database systems are optimized for handling a large number of short, atomic operations such as insertions, updates, and deletions—typical in day-to-day business transactions like banking, retail, or order processing. They prioritize speed, reliability, and data integrity, often using highly normalized schemas to reduce redundancy.; For more information, see Db2 OLTP database solutions.
Online Analytic Processing (OLAP) workloads: OLAP database systems are designed for complex queries and data analysis, supporting operations like aggregations, trend analysis, and multidimensional reporting. These systems typically use de-normalized schemas, such as those used by star or snowflake, to improve query performance.; For more information, see Db2 OLAP database solutions.

For many customers, workloads are not purely OLTP or OLAP, but predominantly one or the other. For example, you might have workloads that are mainly OLTP, but include some minor OLAP capabilities. You might have workloads that are the opposite, with mainly OLAP tasks with some operational workloads, something that is referred to as Operational Analytics. The advantage of Db2 is that, with the common SQL engine, it can deliver solutions on a wide spectrum of workloads.

Comparing transactional and analytic database systems

The advantage of OLTP lies in its ability to process high volumes of concurrent transactions quickly and accurately, while OLAP excels in providing deep insights from large datasets. OLTP is ideal for operational tasks such as customer management or inventory tracking, whereas OLAP is suited for strategic decision-making, such as sales forecasting or performance analysis.

Table 1. Comparison of OLTP and OLAP databases
Attribute	Transactional (OLAP)	Analytic (OLAP)
Purpose	Manage day-to-day operations	Support decision-making and data analysis
Typical operations	Insert, update, delete, simple queries	Complex queries, aggregations, trend analysis
Data volume	Smaller, real-time data	Large volumes of historical data
Query Complexity	Simple and fast	Complex and resource-intensive
Schema design	Highly normalized to reduce redundancy	De-normalized, such as schema used by Snowflake
Performance focus	Fast transaction processing	Fast query performance and analysis
Concurrency	High, with many users working simultaneously	Moderate, with fewer users submitting longer queries
Use cases	Banking, retail, CRM, inventory systems	Business intelligence, reporting, forecasting
Advantage	Real-time accuracy and speed	Deep insights and strategic decision support