Myth: The cloud reduces your workload In the long run, maybe. But to get started, you have to figure out which model of cloud computing is right for you; which applications or services are best suited to it; and how to ensure the proper levels of security, compliance, and uptime. And remember, monitoring the performance of any vendor takes extra time.
“When you’re running production applications, there’s a lot of thinking that goes on in terms of redundancy, in terms of reliability, in terms of performance and latencies,” says Thorsten von Eicken, CTO and founder of RightScale. Before moving applications to the cloud, customers need to ensure those requirements are met, he says, calling it “wishful thinking” that cloud-based systems automatically manage themselves.
Some basis myths of Cloud Computing ripped apart in Computer World.
TDWI has a great article on Data Warehouse Appliances which includes all-in-all solution for enterprises. Neat Read.
In the BI world, the data warehousing appliance extends this metaphor to the enterprise data center with the vision of a high-performance database system that satisfies business intelligence (decision support) requirements and includes the server hardware, network interconnect, database software, and selected load, workload, scheduling, and administration tools needed for quick installation, loading, and ongoing monitoring.
Enterprise data warehousing appliances are popular because they get the job done in many data scenarios. However, in spite of their significant success, data warehousing appliances are not a one-size-fits all proposition, nor, as any vendor will tell you, are they appropriate for every workload profile or data warehousing challenge. A diversity of appliance vendors have emerged, including appliance offerings from the large, established information technology (IT) stalwarts such as HP, IBM, Oracle, and Microsoft. Teradata objects to be called an “appliance,” though it also objects to not being named as an IT stalwart that is relevant to the appliance market.
Best-of-breed innovators continue to contribute to market dynamics. Key differentiators — about which, as a prospective buyer of a data warehousing appliance, you should examine –include the number of successful installed customers in production willing to speak about their experiences (both positive and negative); the details of the technology itself (whether the database is open source and how it is customized, whether the server, disk, and networks are a commodity components and how they can be customized; the breadth and maturity of complementary tools such as inquiry and reporting, ETL, data quality solution); and the price of acquisition and cost of operation. Published results from public benchmarks (such as tpc.org) are also useful for starting a conversation about performance and price, though don’t rely exclusively on the benchmark “winner” since results are frequently updated.
From Business Week article -
Microsoft is buying Powerset, developer of what it hopes is a smarter way to search the Web. Powerset uses so-called “semantic Web” technology that brings up results based on an understanding of a word’s meaning and the context of its use. That’s in contrast to the method used by the major search engines, which work primarily by matching words in queries to those on Web pages. Microsoft announced the acquisition July 1 on a blog, saying it shares Powerset’s vision “to take search to the next level by adding understanding on the intent and meaning behind the words in searches and webpages.” News of Microsoft’s interest in Powerset was reported June 26 by industry blog VentureBeat. According to the article, Microsoft has offered more than $100 million to acquire the company. The purchase price was not disclosed.
The purchase could give Microsoft a big leg up in efforts to catch Google. Powerset and other semantic search engines outperform Google in some cases (BusinessWeek.com, 9/17/07). They respond particularly well when users want detailed answers to questions in specific subject categories for which there are a lot of Web pages with similar keywords, such as health or law. “Semantic search takes it to the third level,” says Eric Tilenius, an early investor in Powerset and Kango, which applies semantic search technology to travel.
TDWI is running a series on developing and deploying Data Warehousing, frugally. It’s a 3 part series. Read Part 1 and 2.
Although seemingly difficult, you can make choices, which allow for the beneficial realization of data warehousing while also minimizing costs. By balancing technology and carefully positioning your business, your organization can quickly create cost-effective solutions using data warehousing technologies.
There are a few simple rules to help you develop a data warehouse on a shoestring budget:
* Use what you have
* Use what you know
* Use what is free
* Buy only what you have to
* Think small and build in phases
* Use each phase to finance or justify the remainder of the projects
It’s also a must read for businesses which have enough business sponsorship and enormous resources. Tough times in the marketplace like these call for an economical way of staying ahead on the business curve. And that’s exactly the point of this series.
I like the detailed approach Nathan Rawling towards this topic.
Mind Tree, one of India’s leading Consulting firms is making enhancements to its RUBIC[Re- Usable Business Intelligence Components] solution frame work. More about it t B-Eye-Network -
MindTree’s RUBIC framework is a set of components that provide BI implementations with structured approaches to manage various design aspects, increase the quality of the solution and substantially save time to market.
RUBIC fixes two specific pain points in a typical BI &DW engagement 1) high data integration effort – by providing reuse of code in the data integration layer and 2) business solution value enhancement providing a platform for ensuring business need coverage.
In the war of intelligence, analytics is key says SAS.
Life Insurance Corporation of India’s ambitious Data Warehouse is underway.
B-Eye Network has an amazing article Customer Data Integration – FAQs and Fiction. Part 1 and Part 2.