We describe how multi-national retailer ADEO successfully employed database virtualization to migrate all workloads of a complex Enterprise Data Warehouse (EDW) from a legacy Teradata system to Google BigQuery. We demonstrate the generality of the technology and the approach.

Performing a detailed workload analysis is a crucial step in determining the feasibility, timeline and cost of a major data warehouse replatforming project, i.e., migration from one platform to another. A large company's data warehouse applications may include millions of queries, some of which will use features that are unsupported or have different semantics in the new warehouse, or may have poor performance there.

In recent years, increased interest in cloud-based data warehousing technologies has emerged with many enterprises moving away from on-premise data warehousing solutions. The incentives for adopting cloud data warehousing technologies are many: cost-cutting, on-demand pricing, offloading data centers, unlimited hardware resources, built-in disaster recovery, to name a few. There is inherent difference in the language surface and feature sets of on-premise and cloud data warehousing solutions. This could range from subtle syntactic and semantic differences, with potentially big impact on result correctness, to complete features that exist in one system but are missing in other systems. While there have been some efforts to help automate the migration of on-premise applications to new cloud environments, a major challenge that slows down the migration pace is the handling of features not yet supported, or partially supported, by the cloud technologies. In this paper we build on our earlier work in adaptive data virtualization and present novel techniques that allow running applications utilizing sophisticated database features within foreign query engines lacking the native support of such features. In particular, we introduce a framework to manage discrepancy of metadata across heterogeneous query engines, and various mechanisms to emulate database applications code in cloud environments without any need to rewrite or change the application code.

The database industry is about to undergo a fundamental transformation of unprecedented magnitude as enterprises start trading their well-established database stacks on premises for cloud database technology in order to take advantage of the economics cloud service providers have long promised. Industry experts and analysts expect the next years to prove a watershed moment in this transformation, as cloud databases finally reached critical mass and maturity.

Wall Street's trading engines are complex database applications written for time series databases like kdb+ that uses the query language Q to perform real-time analysis. Extending the models to include other data sources, e.g., historic data, is critical for backtesting and compliance. However, Q applications cannot run directly on SQL databases. Therefore, financial institutions face the dilemma of either maintaining two separate application stacks, one written in Q and the other in SQL, which means increased IT cost and increased risk, or migrating all Q applications to SQL, which results in losing the inherent competitive advantage on Q real-time processing. Neither solution is desirable as both alternatives are costly, disruptive, and suboptimal. In this paper we present Hyper-Q, a data virtualization plat- form that overcomes the chasm. Hyper-Q enables Q applications to run natively on PostgreSQL-compatible databases by translating queries and results on the fly. We outline the basic concepts, detail specific difficulties, and demonstrate the viability of the approach with a case study.

Writing applications for sensor networks often involves low-level programming. In this demo we show a generic sensor network platform (SwissQM/SwissGate) that provides a high level interface for programming sensor networks and also provides a multi-tier architecture for efficiently handling and optimising the operation of the network. The demo is based on a small scale (deployment in a building) where the network is used concurrently by several applications to measure heating, ventilation, and air conditioning control (HVAC) parameters. The network also implements several event detection functions for fire, burglar, and user triggered alarms. In the demo we show how the sensor network can be programmed using queries in several languages (SQL, Java, XQuery), including user-defined functions (in a C-like language) and the results obtained as a stream of data tuples. We also show the ability to efficiently use the network concurrently.

The real usability of data stream systems depends on the practical aspect of building applications on data streams. In this demo we show two possible applications on data streams implemented on our prototype platform XTream. One application integrates VoIP and E-Mail, the other one incorporates streams in a Smart Home setting. Using these applications we try to identify and discuss the functionality that data stream management systems should provide. Those attending the demo will be able to compose their own applications.

SwissQM is a novel sensor network platform for acquiring data from the real world. Instead of statically hand-crafted programs, SwissQM is a virtual machine capable of executing bytecode programs on the sensor nodes. By using a central and intelligent gateway, it is possible to either push aggregation and other operations into the network, or to execute them on the gateway. Since the gateway is built in an entirely modular style, it can be dynamically extended with new functionality such as user interfaces, user defined functions, or additional query optimizations. The goal of this demonstration is to show the flexibility and the unique features of SwissQM.