It’s really fascinating to see how we are generating an incredible amount of data every minute. Plenty of blogs and articles have been written stating that the better utilization of this data may result in more profit generation for the organization. So here are some tips to begin with the path of better utilization.

Currently what most of the organizations see is they are Creating data, and they want to generate some additional revenue from it, but they miss how to link these two aspects. 

To begin with, they must consider to use the Big Data Engines like Hadoop, Google Bigdata, Horton Works, MongoDB or anything similar. They help to find the patterns and correlations between different types of data.

Next is Data Warehousing. Data warehousing is not just maintaining a database. It has many advantages over the traditional databases. 

And another important step is Data Visualization. From the above two steps we have the data properly managed and organized. In this step we visualize the data in a productive manner.

Read More at https://www.informationweek.com/big-data/real-world-tools-to-help-navigate-a-data-driven-world/a/d-id/1332377?

Hadoop has helped in shaping the future of weather forecasting in a much better way than the analyst could have ever predicted.  More factors can now be evaluated in the models. Also, the advancement in big data has allowed meteorologists to highly depend on the digital models. How exactly will Hadoop affect the future of meteorology? Find out at:

https://www.smartdatacollective.com/hadoop-predictive-analytics-improves-weather-forecasting-models/

With every passing day, improvement in technology keeps making our lives easier and more organized. Social business intelligence is a technology which allows systematic data sharing, computing and analysis to obtain an efficient market data through different social media analytics. It is of utmost importance for companies today to have an efficient business management system which is capable to deal with huge databases coming from social media and IT systems. Also, the systems should be accessible and cost-effective. But, the occurrence of cyber hacks and security impeachments hamper the growth of this market. In the coming years, open-source software framework systems like Hadoop and MapReduce will benefit the companies in efficient data management. FMI reports suggest that business intelligence tools are likely to take over the BSFI, IT and the telecommunication sector by 2017. Read more at

 http://www.business2community.com/business-intelligence/social-business-intelligence-become-important-enterprises-01826088#zh5mpOHGhiZODQ7Y.97

Is big data serving these businesses or is it just obscuring the decision-making procedure? Collective with analytics, Big data has numerous applications and is cast-off to find responses to glitches in a variation of businesses, it can benefit them comprehend customer behavior and get the most out of business procedures. But like so numerous things that complete good in concept, it’s not precisely working out for numerous organizations. 36% say that it has caused data overload and has made procedure of decision making poorer. Combination of the info with classy analytics tools can benefit organizations to turn rare and formless data into planned insight to get ahead of the struggle.  Since more business operators need to be able to take this data and produce intellect around it, demand is increasing for analytics tools. Read more at: http://www.hadoop360.datasciencecentral.com/blog/is-big-data-harm-or-good

The emergence of massive unstructured data sources like Facebook and Twitter has created a need to develop distributed processing systems for Big Data Analytics. Hadoop (A Java based programming framework) has become the first choice of developers and industry experts mainly because its: Highly scalable, flexible, and cheap. An application is broken down into various small parts which runs on thousands of nodes to achieve fast computing speed and reduce overall operation time. Hadoop architecture continues to operate even if a node fails. Its incredible design allows you to process large volumes of data and extract computationally difficult features of users/customers.

Read more at : http://www.datasciencecentral.com/forum/topics/how-to-use-hadoop-for-data-science

A data lake is a single repository (Hadoop or another NoSQL platform) that accesses and stores all types of data, i.e. structured or unstructured, enabling authorized users to quickly access data from one place. A data lake also captures changes to data. It should be a part of an enterprise data storage strategy for getting the most value from your organization’s data. Nowadays, data lakes consist of machine-generated logs and sensor data, raw customer data collected from website clicks, social media, collections of documents such as e-mail and customer files, and geo-location traces. To know more about data lakes & its benefits, follow:  http://it.toolbox.com/blogs/it-solutions/what-a-data-lake-isand-what-it-should-do-69978

Vendors intent to make big data analytics accessible because although it is relatively cheaper to work in Hadoop and NoSQL but practically it is costlier in the sense that it is hard and there is a lack of talent.  The following ways to make big data available are featured below:

• Developers love Couchbase mobile - Couchbase mobile has acquired importance lately due to the creation of mobile apps that may work with or without internet connection.
• Can datawatch make everyone big data expert?                                       
•  Platfora remedies big data disillusionment - Introduction of Platfora 4.5 helps data scientists to derive big data insights in remarkable time.
• Cubes on Hadoop?                                   
•  Wrapping up the pieces - Companies takes maximum advantage of big data but is also apprehensive of the challenges to overcome                                                                                                                                                                                                                      For further details on this study, please follow the link: http://www.cmswire.com/big-data/big-data-bits-big-data-for-all-edition/

The mission of Matt Morgan, the vice-president of global product marketing of Hortonworks is to establish Hadoop as the foundational technology of modern enterprise data architecture. Hortonworks Data Platform (HDP 2.3) is the only enterprise Hadoop-based platform that is made up of 100% Apache open source components. Enhanced security and data governance have been added to HDP 2.3 including new encryption of data, and the extension of the data governance initiative with Apache Atlas. But many doubt that skill shortage is one of the barrier to Hadoop adoption. Read more about this article at:  http://www.cmswire.com/big-data/is-hortonworks-paving-the-way-for-pervasive-hadoop-adoption/

For a future IT landscape dominated by Big Data technologies, it is crucial to use technologies and tools for Big Data. Hadoop and MongoDB are designed to perform in the Cloud which gives firms ability to scale computing and storage resources. Also, data professionals have to undergo proper training to acquire skills. Data stored in cloud should be according to the company's security policies and compliance laws. Mastering the art of messages, promotions, and marketing on a micro-level, will lead to more customer-centricity, and deeper customer engagement, will ultimately result in better ROI. Read more about this article at: http://www.marketingprofs.com/articles/2015/27754/seven-ways-to-get-ready-for-big-data-of-the-future

Transport for London (TfL) data, collected through ticketing systems, censored vehicles, traffic signals, survey groups etc. is provided through open API's for 3rd party app developers. This data is then used to produce maps showing when and where people are traveling, and allowing analysis at the level of individual journeys by using Big Data. The key priority to initiate this data was to provide travel information which gives the routes customers use and to send travel updates to them. Thus Bernard Marr from Forbes in his article showed how big data played a big part in re-energizing London's transport network. Read more about this article at: http://www.content-loop.com/big-data-internet-things-improve-public-transport-london/

When one starts a new project that involves analyzing his company's data especially when the data is stored across functional areas, that person is in trouble. The data lake model helps in this case. To get access to data doesn't require an integration effort, because data is already there in the lake and one can apply MapReduce and other algorithms to use it. In the lake some data are unstructured or not structured by us for a given project. To construct a data lake one needs to learn some of the Hadoop stack such as Sqoop, Oozie and Flume. Next a data scientist should be found who understands Hadoop as well as business and the company’s business data in particular. Then one should start with basic cases and use simple and familiar tools like Tableau to make nice charts, graphics, and reports demonstrating that he can do something useful with the data. Next security up front should be considered, as well as who can access what data. Use of core Hadoop platform is beneficial. Apart from this one should keep in mind that lake security may have business unit implications and one should not have a lot of mini lakes i.e. data ponds that are separate and not equal. Read more at:http://www.infoworld.com/d/application-development/how-create-data-lake-fun-and-profit-246874?page=0,0

One can use the big data, available in hand, in a right or a wrong way. Here is the list of top 10 worst big data practices which one should try to avoid. First, though MongoDB has an aggregation platform, it is not good as an analytical system and thus should not be used as big data platform. Second, RDBMS schema is used as files by many which should be avoided too. Third, creating a series of data points. Fourth, failing to develop use cases. Fifth, over-dependence on Hive should be reduced as the whole point of big data is to expand beyond what one could do with one technology. Sixth, it's not right to treat HBase like an RDBMS. Seventh, trying to install Hadoop and all its moving parts on 100 nodes by hands is also a worst practice. Eighth, one should also avoid RAID/LVM/SAN/VM-ing one's data nodes. Ninth, instead of treating HDFS as just a file system one needs to think about how one is going to secure all of this and for whom. Finally, everyone is free but each one should have a plan. Read more at:http://analytics.theiegroup.com/article/53c925453723a81857000073/The-10-Worst-Big-Data-Practices-

A Survey, based on the responses from 111 data scientists in US, found that Hadoop is too slow according to 76% of data scientists as they believe that the open source software framework requires too much effort to program and isn't fast enough to keep up with big data demands. On the other hand almost 91% of the survey respondents claim that they are performing complex analysis of data on the basis of which 39% of overall respondents say that their job is getting tougher. However, Big Data is becoming highly important for all enterprises. According to a research commissioned by Dell and conducted by Competitive Edge Research, a big section of midmarket companies with 2,000 to 5,000 employees are embracing the rise of big data and almost 80% percent of the midmarket thinks they need to better analyze their data, as they believe big data initiatives provide a significant boost to company decision making. Read more at:http://analytics.theiegroup.com/article/53baa9d23723a81e1300007b/Survey-Finds-Hadoop-Is-Too-Slow-Big-Data-Is-Too-Complex

With the advent of web and social media the speed of the evolution of language has increased dramatically. There are many contributing factors to language that affect the changes. Big data takes linguistics to the next level and the technology like Hadoop helps in assisting interested parties in gaining deeper and clearer insights into linguistics. The reasons why Linguistics should be understand are that- Firstly, to benefit from the insights into linguistics provided by big data whether it may be vocabulary or grammar or something else. Secondly, today's technology continues to develop and improve, the use of voice commands for phones, TV's and game systems is going to increase and it's more important that developers understand the language people will be speaking to their devices in order to ensure the responsiveness. Big data will greatly enhance their ability to provide such speech oriented aspects. Thirdly, in case of learning a language and the way it is learned, understanding of linguistics matters a lot. Finally, to understand the past and looking to the future, it is again important to understand linguistics. With big data technology, the huge amount of data and information can be gathered and used to provide better insights into the past and future of language. Read more at:http://analytics.theiegroup.com/article/53bd6b6d3723a864d8000023/The-Impact-Of-Big-Data-On-Linguistics

Analytics for Small Medium Businesses (SMB) today, is a much discussed topic. SMBs face the challenges of effectively using analytical tools to gain precious business insights from data generated. Today markets are able to provide solutions to SMBs which were costly before and this was made possible by the advent of Qlikview, Tableau etc. in Analytics sector. SMBs are realizing that analytics can help them understand customer preferences, expand their market share, cut down cost, increase efficiency and give them a competitive advantage even against the big players. Moreover, with the advent of new technologies like cloud, social media and open source platforms like Pentaho and Hadoop, the requirement for big infrastructural set-up and capital cost have been reduced considerably. The success of Analytics tools depends to a large extent on collecting and managing data and in such case ERP and CRM tools are a must for success. For successful implementation of analytics tool, SMBs need to assess the external market as well as their internal systems and processes. However, SMBs will soon be able to adapt their systems to bring in the external big data from social media like any other big enterprises and hence make their analytics more robust. Read more at:http://www.informationweek.in/informationweek/perspective/296888/value-analytics-businesses

Big Data is about large volumes of unstructured data along with rapid analysis with insights being noted within seconds. Big Data allows narrower customer segments and help in tailoring precise products and services which will then allow for companies to develop the next-generation products and services. The fact is that Big Data requires more capacity, highly efficient accessibility. It would require scale-out or clustered storage systems - such as scale-out NAS (Network Attached Storage) which can scale out to meet capacity and uses systems which are distributed across many storage devices and can handle billions of files without degradation of performance. Big Data using Hadoop stack has been gaining acceptance widely. Also, organizations which create and store more transactional data in digital form can collect more accurate and detailed performance information on everything. RAID-based storage systems have huge storage capacity but not necessarily what Big Data requires and RAID based systems cannot protect data from loss. Thus, most IT organizations incur additional costs which use RAID for Big Data storage as they need to copy it two or three times to protect it from loss. Read more at:http://www.informationweek.in/informationweek/perspective/296730/environments-object-storage

Rapid Miner had an existing partnership with Radoop - an analytics company that optimizes the big data platform known as Hadoop. Now, after successfully acquiring Radoop, Rapid Miner will be able to provide access to many other Hadoop features to its customers which will in turn build a larger presence in the Hadoop ecosystem for RapidMiner. The acquisition also brings partnerships with Hadoop platforms Cloudera and Hortonworks, and adds 20 new clients to RapidMiner’s customer base. The powerful combination of RapidMiner and Radoop will allow applications of advanced analytics to big data. Apart from providing scripting and advanced predictive analytics for experts, it will also help non-technical people to access, analyze, and visualize big data.

To read more, Visit the following link:

http://betaboston.com/news/2014/06/17/rapidminer-acquires-big-data-analytics-company-radoop/

Docker is an open source Linux containerization technology which lets an admin run multiple apps with all their dependencies in secure sandboxes on the same underlying Linux OS. It makes an attractive alternative to typical virtualization. Hadoop community is working on patches that will bring Docker into the data management system. To know more, go through the article by Jack Clark, World's only Distributed Systems Reporter.

http://www.theregister.co.uk/2014/05/02/docker_hadoop/

Red Hat outlined engineering partnership with Hortonworks to collaborate on enabling more storage file systems. The integration allows Hadoop to run directly on a POSIX(Portable Operating System Interface)-compliant storage node. The two companies will create test suites to validate compatibility between Hadoop and alternative file systems, which will be contributed to the open source community. To know more on this, go through the article by Larry Dignan, Editor in Chief of ZDNet and SmartPlanet as well as Editorial Director of ZDNet's sister site TechRepublic.

http://www.zdnet.com/red-hat-forges-hortonworks-engineering-pact-ties-storage-into-openstack-7000016801/

Red Hat has gathered a selection of open source software to create a Big Data development and deployment environment. It announced a software stack which includes Red Hat Enterprise Linux, Red Hat Storage, a Hadoop plug-in allowing Hadoop to process data stored using Red Hat Storage, Red Hat Enterprise Virtualization etc. The company has thrown its hat into the ring to compete with and cooperate with many in the Hadoop community. To know more on this, go through the article by Daniel Kusnetzky, a reformed software engineer and product manager.

http://www.zdnet.com/red-hat-throws-its-hat-into-the-big-data-ring-7000011680/