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. Talk By: Akshay Sharma, Senior Solutions Consultant, Databricks ; Praveen Vemulapalli, Director- Technology, AT&T Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data… Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc