Kognitio Tries Fast, Faster, Fastest Data Warehouse Strategy
Microsoft Excel-friendly analysis engine and three appliance configurations cover the spectrum of big-data performance demands.
(click image for larger view)
Slideshow: 8 Big DataDeployments In Detail
Fast, faster, and fastest. Kognitio became the latest data warehousing vendor to offer this three-speed appliance strategy last week. And following up this week, Kognitio added a virtual-OLAP analysis engine aimed at flexible, what-if analysis by business users.
Kognitio's OLAP-style offering is Pablo, a Picaso-inspired name for a new cube-analysis extension of the vendor's WX2 relational database. Pablo supports multidimensional analysis, but unlike conventional OLAP cubes, which have to be prebuilt, the optional module builds virtualized cubes on the fly. Thus, any dimension of data in a WX2 database can be used for rapid-fire analysis from a cube held entirely in memory.
We're not talking about SQL querying here, so you need a business intelligence tool for business-user friendly multidimensional analysis. Kognitio's answer is A La Carte, a Pablo feature that lets Microsoft Excel tap into the dozens, if not hundreds, of terabytes in Kognitio deployments. Excel is familiar to almost any data analyst, so Kognitio is following a well-worn path by integrating with world's most popular data interface.
Kognitio is a database vendor that doesn't have its own hardware. But bowing to customer interest in rapid deployment, the company has for many years put together complete appliances using hardware from HP or IBM. These appliances have typically been built to customer specs, but Kognitio responded to offerings by EMC, IBM Netezza, and Teradata last week by introducing standardized appliances at three performance levels. Lakes, Rivers, Rapids are conceptually named after the analysis required.
The Lakes appliance is focused on analyzing large pools of data at low cost, with 10 terabytes of storage and 48 compute cores per module. The memory-to-disk ratio is 17.36, so it's not about rapid data flow. Telcos or financial services might use this configuration to scan vast stores of call-data-records or financial transaction records required for compliance purposes.
The Rivers appliance balances speed and capacity, with 2.5 terabytes of storage, 48 cores per module, and a memory-to-disk ratio of 4.34. This is suitable for a general-purpose mix of speed and scale, so you can quickly answer business-critical questions while still offering what Kognitio says is a reasonable cost-to-performance ratio.
For the ultimate in query performance, the Rapids appliance applies 96 cores of processing power to just 1.5 terabytes per module. The memory-to-disk ratio is 2.92, so much of the data analyzed can be held in memory. The configuration is aimed at financial firms doing algorithmic trading and other exotic, high-end performance demands.
Teradata set the three-speed trend in 2009 when it announced high-capacity and high-performance appliances to complement its standard products. EMC and IBM Netezza recently followed with capacity- and performance-oriented products complementing their standard appliances.
Pablo's OLAP-style analysis is also not new. Teradata has offered a virtual OLAP-analysis option and a tie to Microsoft BI tools (including Excel) for several years.
The Pablo option for WX2 and the Lakes, Rivers, and Rapids appliance configurations are available immediately. Kognitio declined to detail pricing. Kognitio has 30 customers, half of which use hosted WX2 deployments.
The economies of storage networking have changed dramatically, especially in the options available for small and midsize enterprises. We analyze SME responses to our 2011 State of Enterprise Storage Survey and discuss which techs will best serve these businesses. Download our report now. (Free registration required.)
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.