Stamford, CT-based analyst and market research firm Gartner released its annual data warehouse Magic Quadrant report Monday. On the one hand, data warehousing (DW) and Big Data can be seen as different worlds. But there's an encroachment of SQL in the Hadoop world, and Massively Parallel Processing (MPP) data warehouse appliances can now take on serious Big Data workloads. Add to that the number of DW products that can integrate with Hadoop, and it's getting harder and harder to talk about DW without the discussing Big Data as well. So, the release of the Gartner data warehouse report is germane to the Big Data scene overall and some analysis of it here seems sensible.
The horse race
First, allow me to answer the burning question: who "won?" Or put another way, which vendor had, in Gartner's inimitable vernacular, the greatest "ability to execute" and "completeness of vision?" The answer: Teradata. Simply put, the company's 3-decade history; the great number of industry verticals with which it has experience; the number and diversity of its customers (in terms of revenue and geography); and the contribution of the Aster Data acquisition to product diversity really impressed Gartner.
But Teradata came out on top last year as well, and its price points mean it's not the DW solution for everyone (in fact, Gartner mentions cost as a concern overall for Teradata). So it's important to consider what else the report had to say. I won't rehash the report itself, as you can click the link above and read it for yourself, but I will endeavor to point out some overall trends in the report and those in the market that the report points out.
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- Also read: Gartner, IBM, Teradata make Big Data announcements
Logical data warehouse
If there is any megatrend in the DW Magic Quadrant (MQ) report, it's the emergence of the logical data warehouse. Essentially, this concept refers to the federation of various physical DW assets into one logical whole, but there are a few distinct vectors here. Logical data warehouse functionality can allude to load balancing, disbursed data (wherein different data is stored in disparte physical data warehouses and data marts, but are bundled into a logically unified virtual DW), and multiple workloads (where relational/structured, NoSQL/semi-structured and unstructured data are integrated logically).
This multiple workload vector is a Big Data integration point too, with 10 of the 14 vendors in the report offering Hadoop connectors for their DW products.
In-memory is hot
In-memory technology, be it column store-based, row store-based, or both, and whether used exclusively or in a hybrid configuration with disk-base storage, is prevalent in the DW space now. Gartner sees this as a competitive necessity, and gives IBM demerits for being behind the in-memory curve. On the other hand, it refers three times to the "hype" surrounding in-memory technology, and generally attributes the hype to SAP's marketing of HANA. Meanwhile, Gartner notes that HANA's customer base doubled from about 500 customers at the end of June 2012 to 1,000 at the end of the year.
- Also read: SAP HANA does Big Data...with ERP, CRM and BI savvy
- Also read: SAP takes ERP in-memory
Support for R
Support for the open source R programming language seems to be accelerating in mainstream DW acceptance and recognition. Support for the language, used for statistics and analytics applications, is provided by 2013 DW MQ vendors Exasol, Oracle and SAP. Oracle offers a data connector for R, whereas Exasol and SAP integrate R into their programming and query frameworks.
- Also read: Revolution R v6.0 comes to town
I think it's likely we'll see adoption of R gain even more momentum in 2013, in the DW, Business Intelligence and Hadoop arenas.