Five Mistakes to Avoid When Using Spark - ERP Spain.com - España y América Latina

Apache Spark is playing a critical role in the adoption and evolution of Big Data technologies because it provides sophisticated ways for enterprises to leverage Big Data compared to Hadoop. The increasing amounts of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine. Drawing on experiences across dozens of production deployments, Pepperdata Field Engineer Alexander Pierce explores issues observed in a cluster environment with Apache Spark and offers guidelines on how to avoid common mistakes. Attendees can use these observations to improve the usability and supportability of Spark and avoid such issues in their projects. Topics include: – Serialization – Partition sizes – Executor resource sizing – DAG management – Shading