Real-time data lakes, which aggregate and process both streaming and batch data, have emerged as key enablers of this capability. This paper explores the integration of Apache Flink, a powerful stream processing engine, and Apache Iceberg, an open table format, to build a high-performance real-time data lake. The combination of these technologies allows for seamless handling of both real-time and historical data, ensuring low-latency queries and efficient storage. We delve into the architectural design, key challenges, and optimizations required to implement a robust system capable of handling diverse workloads. Furthermore, the paper highlights best practices for managing schema evolution, optimizing data partitioning, and ensuring transactional consistency. The integration of Flink and Iceberg not only enhances data accessibility and reliability but also offers a scalable solution for organizations seeking to leverage real-time analytics. Our findings demonstrate the efficacy of this approach in improving data processing speed, accuracy, and overall system performance. In the era of big data, organizations increasingly rely on real-time analytics to gain timely insights and maintain competitive advantage. This paper presents a comprehensive approach to designing and implementing a high-performance real-time data lake using Apache Flink and Apache Iceberg. We explore how Flink, as a robust stream processing engine, can handle real-time data ingestion, processing, and analytics, while Iceberg provides an efficient and scalable data lake storage format. The integration of these technologies is examined to address key challenges such as data consistency, schema evolution, and system scalability. Through practical case studies and performance benchmarks, we demonstrate how this architecture supports low-latency querying, reliable data management, and seamless integration with existing data infrastructure. Our findings provide valuable insights into optimizing real-time data lakes for large-scale data operations and highlight best practices for leveraging Flink and Iceberg in a modern data ecosystem.

Real-Time Data Lake, Apache Flink, Apache Iceberg, Stream Processing, Data Ingestion

[1] T. Akidau, et al., "Watermarks in stream processing systems: Semantics and comparative analysis of Apache flink and google cloud dataflow," Oak Ridge National Laboratory (ORNL), vol. 14, no. 12, 2021.
[2] H. Li, et al., "Cost-efficient scheduling of streaming applications in apache flink on cloud," IEEE Transactions on Big Data, vol. 9, no. 4, pp. 1086-1101, 2022.
[3] D. Kastrinakis and E. G. M. Petrakis, "Video2Flink: real-time video partitioning in Apache Flink and the cloud," Machine Vision and Applications, vol. 34, no. 3, p. 42, 2023.
[4] T. Toliopoulos and A. Gounaris, "Adaptive distributed partitioning in apache flink," in 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), IEEE, 2020.
[5] C. Calavaro, G. Russo Russo, and V. Cardellini, "Real-time analysis of market data leveraging Apache Flink," in Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, 2022.
[6] M. R. HoseinyFarahabady, et al., "Q-flink: A qos-aware controller for apache flink," in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE, 2020.
[7] M. A. Bender, et al., "Iceberg hashing: Optimizing many hash-table criteria at once," Journal of the ACM, vol. 70, no. 6, pp. 1-51, 2023.
[8] T. Hlupi?, et al., "An overview of current data lake architecture models," in 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), IEEE, 2022.
[9] J. C. Couto and D. D. Ruiz, "An overview about data integration in data lakes," in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), IEEE, 2022.
[10] E. Zagan and M. Danubianu, "Cloud DATA LAKE: The new trend of data storage," in 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE, 2021.
[11] H. Dibowski and S. Schmid, "Using knowledge graphs to manage a data lake," in INFORMATIK 2020, Gesellschaft für Informatik, Bonn, 2021.
[12] S. Vyas, et al., "Literature review: A comparative study of real time streaming technologies and apache kafka," in 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), IEEE, 2021.
[13] S. Vyas, et al., "Performance evaluation of apache kafka–a modern platform for real time data streaming," in 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), vol. 2, IEEE, 2022.
[14] H. Wu, Z. Shang, and K. Wolter, "Learning to reliably deliver streaming data with apache kafka," in 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), IEEE, 2020.