International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
www.irjet.net
Modern Data Engineering with Apache Spark Structured Streaming and Apache Flink Sreyashi Das Netflix, USA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Over the last decade, Apache Spark
inherent challenges of real-time data processing. Methodologies for building scalable, resilient streaming applications are discussed, highlighting best practices for optimizing performance and ensuring data quality. Through practical applications the benefits of each framework are illustrated, offering insights into their unique strengths and trade-offs.
Structured Streaming and Apache Flink have emerged as leading frameworks for real-time data processing. This paper proposes advanced techniques for designing and building reliable streaming components that seamlessly integrate into data pipelines, thereby simplifying data transformations and enhancing query performance. By leveraging Spark Structured Streaming's capabilities, such as micro-batch processing, adaptive query execution, and enhanced state management, we demonstrate how to ensure scalability and resilience in handling large-scale streaming applications. Additionally, Flink's support for temporal table joins, stateful stream processing, and dynamic table management empowers data practitioners and engineers to efficiently and reliably transform data, meeting the demands of modern data engineering teams and organizations. This paper provides valuable insights into the art of moving and transforming data, offering practical guidance for building robust streaming applications.
Furthermore, the paper discusses the future directions of stream processing, including the integration of AI/ML and edge computing, and how these trends are shaping the next generation of data pipelines. This paper serves as a valuable resource for data practitioners and engineers, providing guidance on selecting and implementing the right streaming solutions to meet the demands of modern data-driven organizations.
2. Historical context and evolution 2.1 The emergence of Stream processing The early 2000s marked the beginning of a shift towards real-time data processing, driven by the need for immediate insights from data as it was generated. Initial solutions were often limited in scalability and flexibility. The introduction of Apache Spark in 2009, followed by Spark Streaming in 2013, provided a robust framework for processing live data streams using a micro-batch model[Fig-3].
Key Words: (streaming data pipelines, spark structured streaming, flink
1.INTRODUCTION In the realm of big data, real-time data processing has become essential for organizations seeking to derive immediate insights and maintain a competitive edge. Deploying streaming data pipelines efficiently with high throughput and low latency present challenges due to complex event processing, inconsistent data quality and resource management to optimize query performance. Addressing these challenges, requires a deep-dive into how streaming data pipelines[Fig-1] are implemented for real-world applications. A comparative analysis reveals shifting strengths, with Flink's true stream processing model offering distinct advantages in latency and state management while Spark structured streaming’s integration with existing data ecosystem.
Apache Flink, initially developed as Stratosphere in 2009, introduced a true stream processing model in 2014. Flink's ability to handle data in real-time with event time semantics and stateful computations distinguished it from other frameworks. Table -1: Key Milestones of stream processing Key Milestones
This paper explores the intricacies of deploying streaming data pipelines using two prominent frameworks: Apache Spark Structured Streaming and Apache Flink. By examining their historical evolution, technical capabilities, and recent advancements, the paper provides a comprehensive understanding of how these technologies can be leveraged to overcome the
© 2025, IRJET
|
Impact Factor value: 8.315
|
2013
Spark Streaming introduced micro-batch processing, enabling seamless integration with the Spark ecosystem
2014
Apache Flink released with a focus on low-latency, high-throughput stream processing
2016
Flink introduced exactly-once state consistency, revolutionizing stateful stream processing
ISO 9001:2008 Certified Journal
|
Page 714