Palo is an MPP-based interactive SQL data warehousing for reporting and analysis. Palo mainly integrates the technology of Google Mesa and Apache Impala. Unlike other popular SQL-on-Hadoop systems, Palo is designed to be a simple and single tightly coupled system, not depending on other systems. Palo not only provides high concurrent low latency point query performance, but also provides high throughput queries of ad-hoc analysis. Palo not only provides batch data loading, but also provides near real-time mini-batch data loading. Palo also provides high availability, reliability, fault tolerance, and scalability. The simplicity (of developing, deploying and using) and meeting many data serving requirements in single system are the main features of Palo. In Baidu, the largest Chinese search engine, we run a two-tiered data warehousing system for data processing, reporting and analysis. Similar to lambda architecture, the whole data warehouse comprises data processing and data serving. Data processing does the heavy lifting of big data: cleaning data, merging and transforming it, analyzing it and preparing it for use by end user queries; data serving is designed to serve queries against that data for different use cases. Currently data processing includes batch data processing and stream data processing technology, like Hadoop, Spark and Storm; Palo is a SQL data warehouse for serving online and interactive data reporting and analysis querying.