HP Upgrades Neoview Data Warehouse Appliance - InformationWeek

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HP Upgrades Neoview Data Warehouse Appliance

Enhancements target mixed workloads, high data volumes and low-latency demands of operational BI deployments.

Enhanced query optimization and all-new data-ingest/data-extraction capabilities top the list of upgrades to HP Neoview 2.3, the latest version of the company's data warehouse appliance announced and released June 2. The enhancements are aimed at supporting operational business intelligence — deployments characterized by vast data volumes, high numbers of users, mixed query loads and, increasingly, demand to handle all these challenges simultaneously with minimal latency.

The top-three upgrades to version 2.3 are its Adaptive Segmentation, Skew Buster and Transporter features. The Adaptive Segmentation feature has been enhanced in version 2.3 to do a better job of matching compute resources to queries. While most data warehouse appliances are well suited to handle complex analytic queries, HP says some of these devices balk when simultaneously presented with short, transactional queries and other loads.

"Let's say several hundred customer service representatives query the warehouse expecting one- to- two-second query response times, but at the same time you have a massive data ingests and large data extractions tied to fraud detection," says Vish Mulchand, Neoview product marketing director. "Some of our customers couldn't even get these types of workloads running simultaneously in conventional data warehouses, but with Neoview and Adaptive Segmentation, they are not only running mixed loads, they are meeting demanding service-level targets." Mulchand says Adaptive Segmentation improvements have increased query throughput by two to seven times.

Most data warehouse appliances employ massively parallel processing (MPP), which can be exploited to spread queries across many compute nodes for optimum performance. Bottlenecks can emerge, however, if data sets are heavily skewed, placing extreme workloads on one or a small number of nodes. Even if DBAs carefully craft queries for even distribution, skewed data often emerges in intermediate stages of complex queries, says HP. An enhanced "Skew Buster" feature in Neoview 2.3 is said to evenly redistribute workloads as queries unfold, thereby taking full advantage of parallel processing and maximizing performance.

The new HP Neoview Transporter feature is said to deliver enhanced data-ingest and data-extraction capabilities as well as streaming data, trickle feeding and event detection features crucial to real-time performance. Retailers scoring credit card transactions, banks looking for fraud and telecom companies studying calling patterns are among the many examples of businesses that need to quickly ingest and extract large volumes of data.

"The Transporter not only supports massive extracts, you can also stream data, so applications can register for events as they appear in the database," says Mulchand. "If a retailer is watching a particular stock level, for example, the database can alert them to a crucial under-stock condition without them having to wait for massive queries or hours-old reports."

Neoview 2.3, which supplants the 2.2 version released last fall, is already shipping. Pricing was not disclosed.

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