Optimization of Analytic Data Flows for Next Generation Business Intelligence Applications

Abstract

This paper addresses the challenge of optimizing analytic data flows for modern business intelligence (BI) applications. We first describe the changing nature of BI in today’s enterprises as it has evolved from batch-based processes, in which the back-end extraction-transform-load (ETL) stage was separate from the front-end query and analytics stages, to near real-time data flows that fuse the back-end and front-end stages. We describe industry trends that force new BI architectures, e.g., mobile and cloud computing, semi-structured content, event and content streams as well as different execution engine architectures. For execution engines, the consequence of “one size does not fit all” is that BI queries and analytic applications now require complicated information flows as data is moved among data engines and queries span systems. In addition, new quality of service objectives are desired that incorporate measures beyond performance such as freshness (latency), reliability, accuracy, and so on. Existing approaches that optimize data flows simply for performance on a single system or a homogeneous cluster are insufficient. This paper describes our research to address the challenge of optimizing this new type of flow. We leverage concepts from earlier work in federated databases, but we face a much larger search space due to new objectives and a larger set of operators. We describe our initial optimizer that supports multiple objectives over a single processing engine. We then describe our research in optimizing flows for multiple engines and objectives and the challenges that remain.