Optimizing data streaming pipelines: a panel recap

Apache Flink and Apache Kafka are often used together due to their complementary features, with Flink being a powerful tool for analytics and understanding the architecture of Kafka, while Kafka is performant and scalable. The combination of these two technologies can greatly enhance data streaming capabilities. However, challenges may arise when working with skewed data, which can be mitigated by adjusting metrics and reviewing partitioning mechanisms. While Flink has improved significantly over time, there are areas where it could still improve, such as operational support for SQL queries and savepoint compatibility between SQL jobs. Alternatives to Flink include kSQL, but Flink offers more flexibility due to its wide range of connectors. For those new to Flink, the most fun thing to try out is exploring the documentation and trying out the SQL client.

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