Spark is one of the world’s most popular open-source, large-scale data processing engines. Spark shoulders the heavy workload of distributed computing and big data processing in many different sectors such as software development, finance, eCommerce, healthcare, media and entertainment, construction, and more.
Yet, despite its ability to handle large data sets and perform resource-intensive computing processes, Spark can struggle to perform at an optimum level. When this happens, companies are at risk of inefficiently utilizing their compute, spending beyond their IT budget and failing to meet their SLAs.
Learn how companies successfully optimize their Spark when it slows down.