There has been a spate of acquisitions in the data warehousing and business analytics market in recent months. In May 2010 SAP announced an agreement to acquire Sybase, primarily for its mobility technology and had already been advancing its efforts with SAP HANA and BI. In July 2010 EMC agreed to acquire data warehouse appliance vendor Greenplum. In September 2010 IBM countered by acquiring Netezza, a competitor of Greenplum. In February 2011 HP announced after giving up on its original focus with HP Neoview and now has acquired analytics vendor Vertica that had been advancing its efforts efficiently. Even Microsoft shipped in 2010 its new release of SQL Server database and appliance efforts. Now, less than one month later, Teradata has announced its intent to acquire Aster Data for analytics and data management. Teradata bought an 11% stake in Aster Data in September, so its purchase of the rest of the company shouldn’t come as a complete surprise. My colleague had raised the question if Aster Data could be the new Teradata but now is part of them.
This is the second in a series of posts on the architectures of analytic databases. The first post addressed massively parallel processing (MPP) and database technology. In this post, we’ll look at columnar database technology. Given the recent announcement of HP’s plan to acquire Vertica, a columnar database vendor, there is likely to be even more interest in columnar database technology, how it operates and what benefits it offers.
It’s clear that now we are living in the era of big data. The stores of data on which modern businesses rely are already vast and increasing at an unprecedented pace. Organizations are capturing data at deeper levels of detail and keeping more history than they ever have before. Managing all of the data is thus emerging as one of the key challenges of the new decade.
Kognitio announced the addition of MultiDimensional eXpressions (MDX) capabilities for its WX2 product line. John Thompson, CEO of U.S. operations, and Sean Jackson, VP of marketing, shared some of the details with me recently. I find the marriage of MDX and large-scale data both technically challenging and potentially valuable to the market.
This is the first in a series of posts on the architectures of analytic databases. This is relational database technology that has been “supercharged” in some way to handle large amounts of data such as typical data warehouse workloads. The series will examine massively parallel processing (MPP), columnar databases, appliances and in-database analytics. Our purpose is to help those evaluating analytic database technologies understand some of the alternative approaches so they can differentiate between different vendors’ offerings. There is no single best solution for all analytics for all types of applications; usually the decision involves a series of trade-offs. Understanding what you might be giving up or gaining, you may be able to make a better decision about which solution is best for your organization’s needs.