In 2009 we explored the feasibility of building a hybrid SQL data analysis system that takes the best features from two competing technologies: large-scale data processing systems (such as Google MapReduce and Apache Hadoop) and parallel database management systems (such as Greenplum and Vertica). We built a prototype, HadoopDB, and demonstrated that it can deliver the high SQL query performance and efficiency of parallel database management systems while still providing the scalability, fault tolerance, and flexibility of large-scale data processing systems. Subsequently, HadoopDB grew into a commercial product, Hadapt, whose technology was eventually acquired by Teradata. In this paper, we provide an overview of HadoopDB's original design, and its evolution during the subsequent ten years of research and development effort. We describe how the project innovated both in the research lab, and as a commercial product at Hadapt and Teradata. We then discuss the current vibrant ecosystem of software projects (most of which are open source) that continued HadoopDB's legacy of implementing a systems level integration of large-scale data processing systems and parallel database technology.

Hadapt is a start-up company currently commercializing the Yale University research project called HadoopDB. The company focuses on building a platform for Big Data analytics in the cloud by introducing a storage layer optimized for structured data and by providing a framework for executing SQL queries efficiently. This work considers processing data warehousing queries over very large datasets. Our goal is to maximize perfor mance while, at the same time, not giving up fault tolerance and scalability. We analyze the complexity of this problem in the split execution environment of HadoopDB. Here, incoming queries are examined; parts of the query are pushed down and executed inside the higher performing database layer; and the rest of the query is processed in a more generic MapReduce framework.

HadoopDB is a hybrid of MapReduce and DBMS technologies, designed to meet the growing demand of analyzing massive datasets on very large clusters of machines. Our previous work has shown that HadoopDB approaches parallel databases in performance and still yields the scalability and fault tolerance of MapReduce-based systems. In this demonstration, we focus on HadoopDB's flexible architecture and versatility with two real world application scenarios: a semantic web data application for protein sequence analysis and a business data warehousing application based on TPC-H. The demonstration offers a thorough walk-through of how to easily build applications on top of HadoopDB.

The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private "clouds". At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them well-suited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.