Big data SQL analytics platform has evolved as the key infrastructure for business data analysis. Compared with traditional costly commercial RDBMS, scalable solutions with open-source projects, such as SQL-on-Hadoop, are more popular and attractive to enterprises. In eBay, we build Carmel, a company-wide interactive SQL analytics platform based on Apache Spark. Carmel has been serving thousands of customers from hundreds of teams globally for more than 3 years. Meanwhile, despite the popularity of open-source based big data SQL analytic platforms, to the best of our knowledge, few empirical studies on quality issues were carried out for them. However, a deep understanding of quality issues and taking right mitigation are significant to the ease of manual maintenance efforts. To fill this gap, we conduct a comprehensive empirical study on 1,884 real-word quality issues from Carmel. We summarize the common symptoms, and identify the root causes with typical cases. Stakeholders including system developers, researchers, and platform maintainers can benefit from our findings and implications. Furthermore, we also present lessons learned from critical cases in our daily practice, as well as insights to motivate automatic tool support and future research directions.