Breaking Spark: The top five mistakes to avoid when using Apache Spark in production
Drawing on his experiences across 150+ production deployments, Neelesh Srinivas Salian focuses on five common issues observed in a cluster environment setup with Apache Spark (Core, Streaming, and SQL) to help you improve the usability and supportability of Apache Spark and avoid such issues in future deployments.
Talk Title | Breaking Spark: The top five mistakes to avoid when using Apache Spark in production |
Speakers | |
Conference | Strata + Hadoop World |
Conf Tag | Make Data Work |
Location | New York, New York |
Date | September 27-29, 2016 |
URL | Talk Page |
Slides | Talk Slides |
Video | |
Apache Spark has been growing in deployments for the past two years. The increasing amount of data being analyzed and processed through the framework is massive, and it continues to push the boundaries of the engine. Drawing on his experiences across 150+ production deployments, Neelesh Srinivas Salian focuses on five common issues observed in a cluster environment setup with Apache Spark (Core, Streaming, and SQL) to help you improve the usability and supportability of Apache Spark and avoid such issues in future deployments. Topics include: