Crafting a High-Performance Real-Time Data Lake with Flink and Iceberg

Abstract

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.