Nicoleta Lazar dives into the specifics of real-time data streaming using StarRocks, Flink, and Airflow in part two of the series. The article emphasizes the data pipelines at Fresha, which utilize Change Data Capture (CDC) from PostgreSQL databases via Kafka with Debezium connectors. It details how data flows from Kafka into various platforms, including Snowflake and Elasticsearch, culminating in StarRocks. A key focus is the Routine Load method for ingesting Kafka data into StarRocks tables. This method allows for basic SQL transformations and operates as a background job, enabling exact-once delivery semantics. The author outlines the process of creating a Routine Load job, specifying required columns and transformations, job properties, and Kafka source properties. This setup is essential for handling data efficiently, especially when updates or deletes are necessary, which can be performed using a designated `__op` field to manage operations like UPSERT and DELETE. The article also introduces the StarRocks Kafka Connector, detailing its configuration requirements for subscribing to Kafka topics and mapping them to StarRocks tables. Key parameters include connector class, tasks, and authentication information. It highlights the need for careful setup to ensure the Kafka Connect cluster can access StarRocks endpoints. Overall, the piece provides a practical view of integrating these data technologies, emphasizing the technical nuances that can enhance data ingestion processes.