Before we look at streaming and complex event processing, let's explore the concept of OLTP and OLAP databases. OLTP stands for Online Transaction Processing. OLTP supports online transactional operations such as insert, update, and delete, and stores data in normalized form. Normalization is cleaner easier to maintain and changes as it minimizes the data duplication—for example you may store student name and student address in to tables. If you need to update the address or add two addresses for a student, you can do it efficiently without touching the student table. But to query a student's details, one needs to join the student and address tables.

OLAP stands for Online Analytical Processing. It processes historical or archived data to get business insights. In OLAP generally, data is denormalized or duplicated in multidimensional schemas for efficient querying. Here, you don't have to join ten tables to get an insight. OLAP is the foundation of business intelligence (BI). 

ETL (Extract, Transform, and Load) is a process to pull data from OLTP to OLAP. ETL is not a real-time process, jobs are generally executed at the end of the day. The ETL/OLAP model, or the typical business intelligence architecture, doesn't work when we need to process a stream of transactional data and provide business insights or detect threats or opportunities (business insights) in real time. For example, you cannot wait for a few hours to detect fraudulent credit card transaction.

Complex event processing enables real-time analytics on transactional event streams. It intercepts different events, then computes or detects patterns, and finally takes action or provides business insights. 

Apache Ignite has the capability to stream events from disparate sources and then perform complex event processing. The following diagram explains Apache Ignite's complex event processing architecture: