Building a modern streaming analytics pipeline can be efficiently achieved using Confluent Cloud and Imply's Polaris, both provided as SaaS solutions for event streaming and real-time analytics, respectively. This setup involves generating a simulated clickstream event stream that is processed using Confluent Cloud's managed ksqlDB and then ingested into Imply Polaris for visualization. Traditional analytics relied on OLTP and OLAP databases with batch ETL processes, which were often slow and outdated for modern needs. In contrast, streaming analytics using a Kappa architecture simplifies the process by utilizing a single data path to handle both real-time and historical data, as exemplified by Apache Druid, which powers Imply Polaris. The tutorial demonstrates how to set up and manage data ingestion, transformation, and filtration using Confluent Cloud's ksqlDB and the seamless integration with Polaris, ultimately allowing users to create dashboards without writing complex code. This approach highlights the evolution from batch to streaming ETL, emphasizing the flexibility and immediacy of modern data processing techniques.