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Lessons Learned Using Apache Spark for Self-Service Data Prep in SaaS World

Lessons Learned Using Apache Spark for Self-Service Data Prep in SaaS World

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In this talk, we will share how we benefited from using Apache Spark to build Workday’s new analytics product, as well as some of the challenges we faced along the way. Workday Prism Analytics was launched in September 2017, and went from zero to one hundred enterprise customers in under 15 months. Leveraging innovative technologies from Platfora acquisition gave us a jump-start, but it still required a considerable engineering effort to integrate with Workday ecosystem. We enhanced workflows, added new functionalities and transformed Hadoop-based on-premises engines to run on Workday cloud. All of this would not have been possible without Spark, to which we migrated most of earlier MapReduce code. This enabled us to shorten time to market while adding advanced functionality with high performance and rock-solid reliability. One of the key components of our product is Self-Service Data Prep. Powerful and intuitive UI empower users to create ETL-like pipelines, blending Workday and external data, while providing immediate feedback by re-executing the pipelines on sampled data. Behind the scenes, we compile these pipelines into plans to be executed by Spark SQL, taking advantage of the years of work done by the open source community to improve the engine’s query optimizer and physical execution. We will outline the high-level implementation of product features, mapping logical models and sub-systems, adding new data types on top of Spark, and using caches effectively and securely, in multiple Spark clusters running under YARN, while sharing HDFS resources. We will also describe several real-life war stories, caused by customers stretching the product boundaries in complexity and performance. We conclude with the unique Spark tuning guidelines distilled from our experience of running it in production, in order to ensure that the system is able to execute complex, nested pipelines with multiple self-joins and self-unions.
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