Скачать с ютуб Batch process analysis of Apache Spark, Hadoop, and Flink в хорошем качестве

Batch process analysis of Apache Spark, Hadoop, and Flink

Racing through big data: A comparative analysis of Apache Spark, Hadoop, and Flink in batch processing Lakshmana Yenduri, Sr. Staff Software Engineer @ Visa In the era of Big Data, efficient data processing architectures are crucial for the timely analysis of vast datasets to extract valuable insights. Apache Hadoop (AH), Apache Spark (AS), and Apache Flink (AF) are prominent contenders in large-scale data processing. Lakshmana's talk focuses on batch processing to evaluate performance, aiming to shed light on execution time with large datasets. Through experiments ranging from 1 GB to 5 GB, AS emerged as the frontrunner, showing significant performance advantages over AF and AH. Despite the absence of parallelism, Spark maintained its lead, indicating its potential for scalable batch processing. Further research is needed to explore Spark's performance in distributed environments. Overall, this talk underscores Spark's significance in batch processing large datasets, contributing to our understanding of Big Data processing and informing data analytics workflows.