:rocket: The journey concludes! I'm excited to share the final installment, Part 5 of my "𝐆𝐞𝐭𝐭𝐢...

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[Jaehyeon Kim]

🚀 The journey concludes! I'm excited to share the final installment, Part 5 of my "𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐢𝐧 𝐊𝐨𝐭𝐥𝐢𝐧" series:

"𝐅𝐥𝐢𝐧𝐤 𝐓𝐚𝐛𝐥𝐞 𝐀𝐏𝐈 - 𝐃𝐞𝐜𝐥𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐒𝐭𝐚𝐭𝐬 𝐢𝐧 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞"!

After mastering the fine-grained control of the DataStream API, we now shift to a higher level of abstraction with the 𝗙𝗹𝗶𝗻𝗸 𝗧𝗮𝗯𝗹𝗲 𝗔𝗣𝗜. This is where stream processing meets the simplicity and power of SQL! We'll solve the same supplier statistics problem but with a concise, declarative approach.

This final post covers:

• Defining a 𝐓𝐚𝐛𝐥𝐞 over a streaming 𝐃𝐚𝐭𝐚𝐒𝐭𝐫𝐞𝐚𝐦 to run queries.
• Writing declarative, 𝐒𝐐𝐋-𝐥𝐢𝐤𝐞 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 for windowed aggregations.
• Seamlessly 𝐛𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞 𝐓𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚𝐒𝐭𝐫𝐞𝐚𝐦 𝐀𝐏𝐈𝐬 to handle complex logic like late-data routing.
• Using Flink's built-in Kafka connector with the avro-confluent format for declarative sinking.
• Comparing the declarative approach with the imperative DataStream API to achieve the same business goal.
• Demonstrating the practical setup using 𝐅𝐚𝐜𝐭𝐨𝐫 𝐇𝐨𝐮𝐬𝐞 𝐋𝐨𝐜𝐚𝐥 and 𝐊𝐩𝐨𝐰 for a seamless Kafka development experience.
This is the final post of the series, bringing our journey from Kafka clients to advanced Flink applications full circle. It's perfect for anyone who wants to perform powerful real-time analytics without getting lost in low-level details.

Read the article: https://jaehyeon.me/blog/2025-06-17-kotlin-getting-started-flink-table/

Thank you for following along on this journey! I hope this series has been a valuable resource for building real-time apps with Kotlin.

🔗 𝐒𝐞𝐞 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐬𝐞𝐫𝐢𝐞𝐬 𝐡𝐞𝐫𝐞:
1. Kafka Clients with JSON
2. Kafka Clients with Avro
3. Kafka Streams for Supplier Stats
4. Flink DataStream API for Supplier Stats