In the age of big data, organizations are constantly challenged to process and analyze vast amounts of data in real time. The choice of the right big data framework is critical for building efficient, scalable, and resilient data pipelines. Among the most prominent frameworks are Apache Spark, Apache Flink, Apache Storm, and Apache Kafka, each offering unique strengths tailored to different use cases.

This talk will provide an in-depth comparative analysis of these four leading big data frameworks. We will explore their architectural differences, performance characteristics, and suitability for various data processing tasks such as batch processing, stream processing, and complex event processing. Through practical examples and benchmarks, we will examine how each framework handles real-time data ingestion, transformation, and analysis, and discuss the trade-offs involved in choosing one over the others.

We will discuss the core features and capabilities of Spark, Flink, Storm, and Kafka.
How to evaluate and select the appropriate framework based on specific project requirements.
Real-world scenarios where each framework excels or faces limitations.
Best practices for integrating these frameworks into a cohesive big data architecture.
Whether you're a data engineer, architect, or decision-maker, this session will equip you with the knowledge to make informed decisions about which big data framework is best suited for your organization's needs.