AT&T's Migration of Billions of Events Processing From Hadoop

AT&T's strategic migration of Hadoop MapReduce jobs to Databricks for one of their network applications has resulted in significant cost and time efficiencies, setting a benchmark in big data processing and analytics. This presentation outlines the comprehensive approach and industry best practices employed by AT&T in this migration. The transition involved converting Hadoop MapReduce jobs to Spark jobs, which are natively supported by the Databricks Platform, leading to improved performance and scalability. This move resulted in a substantial 30% reduction in compute costs and more than halved the execution time, thereby enhancing AT&T's operational efficiency and productivity. The successful migration exemplifies the transformative potential of cloud-native platforms and underlines the value of adopting industry best practices in big data management and processing.