Apache Spark is a full-fledged, data engineering toolkit that enables you to process large data sets without worrying about the underlying infrastructure. It also provides sophisticated ways for enterprises to leverage big data when compared to Hadoop. Whether you come from a Java, Scala, Python, R, or SQL background, Spark promises ease of use that makes it simple to get up and running with the analytics engine.
Spark is known for its speed, which is a result of the improved implementation of MapReduce that focuses on keeping data in memory instead of persisting data on disk. However, the increasing amounts of data being analyzed and processed through the framework are massive and continue to push the boundaries of the engine.
So while it offers great benefits, Spark has its issues—such as complex deployment and scaling. What is the best way to deal with these challenges and ultimately maximize the value you can get from Spark?
Find out by downloading the 5 Apache Spark Best Practices infographic. The infographic draws on the experiences of our experts here at Pepperdata across dozens of production deployments, to thoroughly explore the best practices for managing Apache Spark performance. Learn how to avoid common mistakes, and improve the usability, supportability, and performance of Spark.
You can expect to learn the best practices for dealing with:
1. Serialization
2. Partition recommendations and sizing
3. Executor size and YARN memory overhead
4. DAG management
5. Library conflicts
Rather than scouring the internet to find what you need to know about optimizing your Spark performance, take a look at the 5 Apache Spark Best Practices infographic to find what you need to know in one place. Say goodbye to library conflicts, serialization issues, and more.
Download it now to ensure you’re making the most out of your Spark performance.
