In Part 1, I discussed the growing demand for real-time analytics in today’s fast-paced world, where instant results and immediate insights are crucial. It compared real-time analytics with traditional analytics, highlighting the freshness of data and the speed of deriving insights as key features. The article emphasized the need for selecting the appropriate data architecture for real-time analytics and raised considerations such as events per second, latency, dataset size, query performance, query complexity, data stream uptime, joining multiple event streams, and integrating real-time and historical data. And I teased the following Part 2 of the article, which delves into designing an appropriate architectural solution for real-time analytics.

Building Blocks 

To effectively leverage real-time analytics, a powerful database is only part of the equation. The process begins with the capacity to connect, transport, and manage real-time data. This introduces our first foundational component: event streaming.

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