While Hadoop is an open-source platform, the process of developing and deploying it is far from free. Hadoop’s sheer complexity makes it necessary to build and maintain a significant infrastructure. Hadoop’s widespread and growing resonance in the marketplace is based on the genuine advantages the platform provides: the market’s highest performance and scalability at much-reduced costs compared with other approaches. Once this Hadoop infrastructure is built, it enables the extraction of far more data and information than can be obtained using Oracle infrastructure or other competing solutions.

Or, frankly, better solutions. With no caveat.

The Hadoop project provides a framework that excels at capturing semi-structured and unstructured data to help enterprises get a handle on Big Data. But simply storing data in Hadoop is not enough: companies need fast and agile data analytics in order to tap the full potential of their data. The best approach is to take data from the creation layer (where it is generated) and move it to an actionable layer (an analytic performance database, such as SAND). Using SAND’s bidirectional capabilities, users can quickly analyze data and easily drill down into Hadoop for additional data. Whereas traditional platforms cause bottlenecks by requiring data to be pre-formatted prior to analysis, SAND avoids this limitation through its Infinite Data Optimization technology, ensuring that users can instantly and easily run any query. Faster data analysis empowers the enterprise to get more value out of Big Data, leading to valuable business insights and faster ROI.

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In order to manage Big Data, the industry is turning to Hadoop, an open-source software framework that supports data-intensive distributed applications. Hadoop is best suited to pioneer organizations that already have collected a large mass of data they are trying to store and analyze (as opposed to later adopters that are still trying to amass their stores of data).

This white paper, How to Use Hadoop for Immediate Analytic Value from Big Data, discusses how, using SAND’s bidirectional capabilities, users can quickly analyze data and easily drill down into Hadoop for additional data. Whereas traditional platforms cause bottlenecks by requiring data to be pre-formatted prior to analysis, SAND avoids this limitation through its Infinite Data Optimization technology, ensuring that users can instantly and easily run any query. Faster data analysis empowers the enterprise to get more value out of Big Data, leading to valuable business insights and faster ROI.

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This white paper, Leveraging the Value of Big Data for Life Sciences Companies, discusses how, using SAND Life Sciences, companies more efficiently capture, store, and analyze all of their data. Improved data analysis improves R&D cycles and identifies early indicators of success or failure in a product, enabling the company to concentrate efforts on its most promising projects. By empowering scientists through better information, SAND helps Life Sciences organizations to develop new therapies and treatments more efficiently, and bring those products to market faster, potentially saving patients’ lives.

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Big Data is literally everywhere from web logs, call centre records, video, mobile data, and more types every day. Turning Big Data into valuable, actionable information requires an intelligent solution to mine the data for value then a performant solution to power all this into the hands of your users.

In this video, SAND discusses:

• How to make your analysts more productive
• How to deliver big insights from big data
• How to use the data you already have to drive material value to your business

Watch on-demand now:

• Microsoft mainly wants to protect Windows and Office. Microsoft is a platform company, and its main goal is to protect its highly lucrative Windows and Office monopolies, while establishing other platforms that will be hard for customers to break away from later. New functionality is “drip fed” to users of those core platforms, but new products exist to protect the core. He advised extreme caution before moving to Office 365, and said not to slip into an “all-Microsoft” mentality.

• Oracle products don’t really work well together. Oracle’s sales force is extremely aggressive about pushing a suite of products, but has much fewer integration points than SAP. In fact, integration is usually left entirely up to the customer. Oracle is also very reluctant to talk about product roadmaps for fear that future products will cannibalize existing ones. The company makes more than 90% of its profits through maintenance fees, and will do whatever it takes to keep those fees flowing in. Gaughan also expressed some surprise that so many customers keep working with Oracle despite reporting that Oracle is “the most difficult vendor to deal with.”

• IBM wants to take over your IT strategy. IBM bills itself as a thought leader, but its real business is selling consulting services. To thrive, IBM account managers try to take control of a company’s IT strategy so they can keep pushing new products. Gaughan recommends taking a collaborative or partner approach.

• SAP confuses customers with pricing. A lot of SAP customers ask Gartner for help figuring out SAP’s pricing and licensing, as SAP has unusual terms for billing data going into and out of systems. Gaughan also said that a big technology transition that was driving SAP revenue for the last few years — moving existing customers from the old R/3 system to the newer Business Suite — is almost done, which means SAP will have to be more aggressive with maintenance fees. He recommended locking in maintenance prices now.

I couldn’t have said it better myself – thank you Gartner.

Source: ITNews via BusinessInsider

This white paper, Unleashing Big Data value using SAND Infinite Optimization, discusses how SAND employs a unique approach called Infinite Optimization that avoids traditional limitations. SAND delivers a high-performance analytical database that is automatically optimized for answering any question, even unusual questions that have never before been asked. SAND Infinite Optimization technology means organizations no longer need to model or tune their data before running queries; instead, all data is fully and automatically indexed so that is ready for any and all queries that users can imagine.

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The result is that Oracle’s product line is morbidly obese. Big Data’s Mr. Creosote. For those that remember the Meaning of Life — and I don’t mean “42″, I mean the Monty Python film of the same name — you will remember Mr Creosote eating just one more wafer-thin mint too many… before exploding into a million messy pieces. Is the acquisition of Endeca Oracle’s one wafer-thin mint too many?

Already Exadata, with the Oracle database at its core (that was never designed or intended for how it is currently positioned), has seen more and more acquisitions forced into its box. Oracle is designed for OLTP, not Big Data analytics, yet Larry insists on force-feeding it more and more wafer-thin acquisitions. Endeca forces a column-oriented database into Oracle. First, I’m glad to see Oracle finally figured out column orientation is a good idea for Analytics rather than the hybrid columnar compression that was first grafted onto Oracle. Well done. We knew you’d get there someday! Second, it’s crammed in there with TimesTen to improve memory performance. We knew you’d need that too. Just how clown-car crowded is it with everything from Hadoop and Berkley DB also tied to the box?

Extracting and augmenting structure onto unstructured data and merging it with structured data is the future of Big Data analytics. A video feed of someone walking into a store has multiple versions of the truth depending on the eye of the beholder. If I am running marketing, I may care which other shopping bags someone brings in, where they browse in the store and what they ultimately buy. Merging this data with their shopping basket data can tell me much more than simply looking in their basket. If I am head of security, the same person entering the store with bags and leaving without them and doing so with regularity, when given structure and merged with inventory data, can tell me who is potentially a security risk. There is immense untapped business value in extracting structure from unstructured data and merging it with structured data.

But it requires a database designed and built to handle it, not a monster overfed on an endless supply of wafer-thin acquisitions. Personally I am waiting for the bang.

“This divestiture ensures SAND’s focus is firmly on Big Data and the rapidly-expanding Analytic Database market. SAND has worked in two discrete markets over the past 5 years diluting our specialization by trying to focus in two places at the same time. While both areas are in the red-hot Data Warehousing Market, SAND focused on delivering both the fastest Analytic Database Platform and a product focused on SAP Nearline in the ILM Market. We determined with the rapid expansion of Big Data we needed to focus all our resources on the Analytic Database Platform,” said Mike Pilcher, SAND’s President and Chief Operating Officer.

“Finally we have made significant progress in properly rationalizing the assets of SAND. Pure focus on the Analytic Database Platform allows us to better capture a rapidly growing business opportunity. In addition to significant mergers and acquisition activity there has been an unending amount of press and commentary on Big Data in the Analytic Database space. Adding analytic complexity to Big Data you arrive in SAND’s sweet spot,” stated Tom O’Donnell, SAND CEO.

This white paper, Constructing a Single Customer View on the Individual Level for Better-Targeted Marketing and Increased Sales, discusses how SAND’s robust platform is able to instantly analyze these data stores and is readily scalable, to ensure that companies have the most complete Single Customer View possible—both today and in the future.

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Time-to-insight is increasingly the single most valuable metric for the success of information systems. One huge barrier can be concurrency: the number of users who can cost-effectively leverage analytical power. Register for this episode of The Briefing Room to hear industry expert Bill Inmon’s perspective on “time to insight” – why getting your data fast is key to successful analytics. He will evaluate the gap between how fast analytic databases are and how fast they need to be.

Inmon was briefed by Mike Pilcher, Chief Operating Officer of SAND, who explained how his company’s massively parallel processing (MPP) architecture meets the demand for scalability and agility, particularly around high concurrency applications. He discussed how the use of decoupled, shared storage eliminates bottlenecking and optimizes performance.