Apache Flink is an open-source framework for processing large-scale datasets in streaming or batch mode, known for its fault tolerance and suitability for mission-critical workloads. Redpanda complements Flink as a streaming data platform that offers low-latency, high-throughput data processing with strong fault tolerance and data durability. Together, they are effective in building scalable operational and analytical use cases, such as event-driven applications and real-time analytics. This tutorial, the first in a series, guides users through creating a simple streaming ETL pipeline using Flink and Redpanda. It involves using Docker to set up the necessary environment, Redpanda to manage data streams, and Flink SQL to perform data transformations, specifically transforming JSON-formatted clickstream events to uppercase before routing them back to Redpanda. The setup includes cloning a GitHub repository, configuring Docker containers, and verifying installations before deploying the pipeline to a Flink cluster, demonstrating the integration's capabilities.