SAP SE has rolled out an updated version of its flagship HANA in-memory database and application platform this week, with the emphasis on Big Data analytics and the Internet of Things (IoT).
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Big Data & Digital Marketing
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Data analytics as the key to know your customers and offer them what they really want.
Curated by
Luca Naso
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Familiar with SaaS? If not, let us give you a brief introduction. The easier spoken breakdown of Software-as-a-Service, SaaS is a distribution model that d
Luca Naso's insight:
With SaaS, many of the challenges associated with the traditional software distribution model are eliminated right off the bat, essentially making a big data deployment much less of a hassle to deal with. In an SaaS arrangement, a service provider: 1. hosts the software and delivers it to you over the web. 2. handles all the grueling maintenance aspects that come along with it behind the scenes. Microsoft's Windows Azure HDInsight is the perfect example of such an application. Right now, big data applications are limited in comparison to other fields, but there will surely be more variety available soon. 
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IBM has launched support for the BLU Acceleration service, which will make data processing and analysis faster and more economical.
Luca Naso's insight:
IBM launches a new Big Data architecture acceleration: BLU Acceleration.
Bob Picciano, IBM's general manager, claims that BLU Acceleration is 10 times faster than other database systems, and that in some cases the runtime dropped from minutes to milliseconds.
I am sceptical about having queries running _thousands_ times faster than before, but I welcome innovative solutions from IBM.
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Auto drivers spend the vast majority of their time peering through the windshield, only a fraction of that time glancing in their rear-view mirrors. That's because driving is a mission-critical activity, not unlike the decision-making required to run a serious business. Why then do most executives focus their analytical attention on the past? Why not look at what's happening now, what's coming down the pike?
Luca Naso's insight:
SQLStream is a real-time streaming Big Data software company headquartered in San Francisco. SQLstream provides a relational stream computing platform for analyzing large volumes of service, sensor and machine and log file data in real-time. Unlike a traditional relational database management systems (RDBMS) SQL query, which returns a result and exits, streaming SQL queries do not exit, generating results continuously as soon as new data become available.
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Carrefour improves its marketing with Galigeo and Esri implementation.
Luca Naso's insight:
After Walmart, also Carrefour aims at exploiting Big Data for improving their business.
The approach here is different, and more traditional in a sense. Keywords are: retail site selection, competition analysis, optimization of direct marketing activities, enhancement of store performance (through a better understanding of sales territories and customer needs). An interesting innovative part concerns the usage of Galigeo's Geodashboard which, by representing and displaying data on interactive maps, identifies hidden trends that are not discernible in tables, charts, or other traditional solutions.
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If sustained, the global Big Data market would exceed $47 billion by 2017
Luca Naso's insight:
While global markets continue to experience degrees of stagnation and even decline, the worldwide enterprise software market continue to experience solid growth throughout 2012, according to the International Data Corporation (IDC).
More and more people are realising how important Big Data is to increase ROI and a range of services and products are now available on the market.
The report also stated that “Big data and analytics are also closely tied to the fast growth social business software markets"
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Hadoop is a savior of this big data world. This article gives an introduction to this tool.
Luca Naso's insight:
This is an extremely basic description of Hadoop, and yet it introduces relevant concepts: parallelism and MapReduce.
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Apache Hadoop Big Data Analytics Tool and Technology: Defination, Advantages, Disadvantages
Luca Naso's insight:
Pro and cons of one of Apache Hadoop, one of the most famous and used platform for working with Bigh Data.
Pro: 1. Cheap 2. Fast 3. Scales to large amounts of big data storage 4. Scales to large amounts of big data computation 5. Flexible with types of big data 6. Flexible with programming languages
1. Hard to to set up 2. Hard to manage 3. Hard to keep alive 4. Hard to use 5. Is not secure 6. Is not optimized for your hardware
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GE just announced a new "big-data" and analytics platform it calls “Predictivity," and it has some big partners on board to give it reach.
Luca Naso's insight:
Megacorporation General Electric has just announced a new big-data and analytics platform named “Predictivity”, whose main features are providing real-time insight and analytics.
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From
gigaom
When it comes to using big data technology effectively, there’s a lot to like about SaaS.
Luca Naso's insight:
Great article, really.
Big Data is really Big, and it does not scale! You need clouding, but that's not enough; you need time, but you will never have enough time to dig into all of the data.
You need Big Brains, to make plans and hypotheses to filter and analyse the data in a smart way, in the smart way _your company_ needs.
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Big data was the buzzword of the day today during our second day of IBM training on analytics and, for my social media marketers, today had a BIG
Luca Naso's insight:
Think about all those Tweets and Facebook Status Updates about your brand. Think about all those consumers talking about unmet needs, dissatisfaction with their current products, and new features they’d love to see in the products they own. How much do you hear of what your customers are saying about your products? Start developing a plan on how to collect, store and analyse those data. And then give them to your marketing experts. They will be very grateful to you ;)
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According to Steve Lucas, SAP’s president of platform solutions, service pack 10 is one of the biggest-ever updates to the platform since it was launched in 2010.
Main changes:
1. A new remote data-synchronization feature that allows organizations to synchronize data between remote locations and the enterprise;
2. A new Web-based development workbench to help enterprises cleanse data and manage duplicates (this could dramatically reduce the size of data centres);
3. Expanded data-integration capabilities that include support for the latest Cloudera Inc., Hortonworks Inc. and MapR Technologies Inc. Hadoop distributions.