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

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html

As the field of Data Science is growing, the confusion regarding the skills needed to be a data scientist is also increasing. Most of us think data science skills range from computer science and statistics, to machine learning and strong communication. But, the top data science skills list includes data analysis at the top, followed by others like R, Python and machine learning. As per recruiter lists, R, Python, SQL, SAS and Hadoop are appreciated. To know more about data science skills, follow the article written by Daniel Levine (Content Marketer for RJMetrics) at: http://www.smartdatacollective.com/daniellevine/366486/top-20-data-science-skills

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

Hadoop has grown to be one of the best large scale and batch oriented analytics tool, used by webscalers as well as enterprises. Hadoop was designed to integrate with and complement the existing business intelligence of any corporation. But, the issue with Hadoop is that the adoption rate is very slow with the data center administrators. So, most developers have been looking at possible alternatives. Today we will name a few worthy alternatives that you can look at which have a potential of replacing Hadoop in the years to come. They are:
• Disco
• Misco
• Cloud MapReduce
• Bashreduce
• Qizmt
• HTTPMR
• Skynet
• Sphere
• Riak
• Octopy
• MapReduce
• Filemap
• Plasma MapReduce
• Mapredus
• Mincemeat
• GPMR
• Elastic Phoenix
• Preregrine
• R3
• Ceph
• QFS
• Cloud-Crowd
• HPCC
• Condor
• Storm
• HaLoop
• MapRejuice
• GoCircuit
• Spark
• Stratosphere
• Gridgain
• MongoDB
• Mars
• Minceat
• Dato Core
• HPCC
• MapReduce Lite
• Gearman

For more information visit:
http://www.fromdev.com/2015/03/hadoop-alternatives.html?m=0

Sentiment Analysis is a text mining technique used to analyze the sentiment (unstructured data representing opinions, emotions or attitudes) about a particular product or topic, employing machine learning technique. Organizations across the world are extracting insights from the social data to understand how public feels about the product at a particular point in time. Sentiments are first categorized into negative or positive and then analyzed using various software like Hadoop. It can also be performed on excel. To know more, read the article at: https://www.brandwatch.com/2015/01/understanding-sentiment-analysis/

The introduction of Hadoop has changed the outlook of businesses. It is a software system that has made processing large sets of data located on distributed servers an easy task. Hadoop is mainly used for analytical purposes boosting business performances and customer relationship management. It has worked wonders for small businesses as it has incorporated concepts of big data analytics. Data is much more accessible now and the cost of procurement has been drastically reduced. Some convenient features in Hadoop has made it much more attractive for small businesses. Hadoop has made effective use of big data possible to optimize businesses. With its value increasing day by day, more and more companies from various sectors are going in for Hadoop. Read more at: http://www.dataversity.net/an-introduction-to-hadoop-for-small-businesses/

Choosing the right Hadoop distribution can be a tricky process. There are 4 basic categories that businesses should look at for specific qualifying criteria.
1. Performance
Hadoop is widely chosen as a data platform due to its high performance achieved by replacing the stock MapReduce by Apache Spark. However not all operations need such superior hardware and a business must choose its hardware on basis of the operations it hopes to perform.
2. Dependability
When looking for a distribution, dependability is a significant but rare feature. Only few implementations in Hadoop can guarantee a system availability of 99.999%. Look for a distribution that provides Self-Healing, No Downtime Upon Failure, Tolerance of Multiple Failure, 100% Commodity Hardware, No Additional Hardware Requirements, Ease of Use, Data Protection and Disaster Recovery.
3. Manageability
Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring.
4. Data Access
Gathering and storing data is just the beginning of the process. What really matters is that the stored data must me easily accessible for further processing. Look for a distribution that provides
• Full access to the Hadoop file system API
• Full POSIX read/write/update access to files
• Direct developer control over key resources
• Secure, enterprise grade search
• Comprehensive data access tooling
Hopefully these four specification along with your criterions will enable you to choose the best Hadoop distribution for you.

For more information visit:
http://www.smartdatacollective.com/davemendle/324791/four-considerations-when-choosing-hadoop-distribution

Hadoop, for many years, was the leading open source Big Data framework but recently the newer and more advanced Spark has taken over. Spark is reported to be 100 times faster although it lacks its own distributed storage system. For this reason many projects involve installing Spark on top of Hadoop, where Spark’s advanced analytics can make use of data stored using the Hadoop Distributed File System (HDFS).
What really gives Spark the edge is speed. Spark handles most of its operations ‘in memory’- copying them from the distributed physical storage into far faster logical RAM memory. Spark’s speed of handling advanced data processing tasks such as real time stream processing and machine learning is much more than what could be achieved by Hadoop. Faster dynamic data handling gives Spark the upper hand over Hadoop.
However it must be concluded that these two frameworks are not necessarily mutually exclusive and do not perform exactly the same tasks. In fact using both of them together can actually provide better results than using either one separately.

For more information visit:
http://www.forbes.com/sites/bernardmarr/2015/06/22/spark-or-hadoop-which-is-the-best-big-data-framework/

Hadoop, for many years, was the leading open source Big Data framework but recently the newer and more advanced Spark has taken over. Spark is reported to be 100 times faster although it lacks its own distributed storage system. For this reason many projects involve installing Spark on top of Hadoop, where Spark’s advanced analytics can make use of data stored using the Hadoop Distributed File System (HDFS).
What really gives Spark the edge is speed. Spark handles most of its operations ‘in memory’- copying them from the distributed physical storage into far faster logical RAM memory. Spark’s speed of handling advanced data processing tasks such as real time stream processing and machine learning is much more than what could be achieved by Hadoop. Faster dynamic data handling gives Spark the upper hand over Hadoop.
However it must be concluded that these two frameworks are not necessarily mutually exclusive and do not perform exactly the same tasks. In fact using both of them together can actually provide better results than using either one separately.

For more information visit:
http://www.forbes.com/sites/bernardmarr/2015/06/22/spark-or-hadoop-which-is-the-best-big-data-framework/

Hadoop works on extracting voluminous unstructured data to interpret business performance, customer relationship management etc. Large business houses are at a distinct advantage in handling big data, but with features like Excel reporting in Hadoop, small businesses can also take advantage. Some salient features are:
1. Hadoop is being used with cloud storage platforms to expand storage capabilities.
2. Third party tools and add-ons help tighten up big data security, as big data security is vital.
3. Allowing big data processing through integration with IT systems.
4. Hadoop allows modifications, to extend functionality and accessibility.

To know more: http://www.business2community.com/big-data/hadoop-keeps-even-small-businesses-loop-big-data-analytics-01257559

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/

Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

http://www.dataversity.net/predictions-for-data-science-over-the-coming-years/

There is huge amount of data and searching for ways to use that data can seem terrifying. Although very little of this data is useful, more and more can be extracted for useful patterns and information. This portion of the useful data is called “Big Data”. For now Big Data is a good way for any company to gain advantage but in the coming ten years use of Big Data will be inevitable. Cloud computing and things like Hadoop and NoSQL provide data analyzing tools to several businesses and entrepreneurs to help analyze the right data sets. Experts on Big Data give some useful tips on how to use this technology.
Know the problem you are trying to solve.
The better way to think about the use of Big Data is to consider it as a tool to solve challenges. Once the problem is identified the data can be used to find a solution.
Start small and grow.
It is a good idea to run a trial analysis and see if it solves the problem. If it doesn’t then you haven’t risked much and if it’s a success you will come out with useful data.
Choose the right data.
 Although it can be difficult to find data, data should be combined from different places to obtain the most useful ones.
Move Fast.
Big Data is not only about analyzing information but also acting on it in real time so that all parts of the company is moving towards a common target and can make the most out of the available data.
Read More at: http://www.forbes.com/sites/mikemontgomery/2015/05/07/small-businesses-shouldnt-fear-big-data/

In today`s business world, time is a precious resource, so there is increasing demand for a faster system of data analysis. An emerging open source analytics tool called spark is slowly gaining popularity and is being used for getting faster results obtained from big data analysis. Earlier many companies used and relied on Hadoop for processing and analyzing information, which used to take days to arrive at a meaningful inference. The spark analytics technology uses a completely different technique for storing data than Hadoop, with spark data is directly written in the memory rather than storing in disks, which increases the speed of data analysis remarkably.  There are also other interesting technological features for which spark is gaining attention.To know more read:  http://blogs.wsj.com/cio/2015/06/03/spark-a-tool-at-big-datas-cutting-edge-helps-under-armour-perform-faster-analytics/?KEYWORDS=analytics

Big data developments in 2015 show significant change in terms of data storing as well as data analyzing techniques. Measuring data agility is becoming more and more popular, as opposed to the conventional system of storage and management of data resources. The investment and scope of big data projects will be directly related to the impact and of an organization's response to changing trends among consumers, markets and competition. Another big trend organizations will focus on in 2015 is usage of data lakes. Using data lakes, Hadoop and BI systems and cloud computing, real time data analysis and decision making is expected to be fast. So the dynamic change in technology will bring revolution in terms of business decision making. Read more at: http://channels.theinnovationenterprise.com/articles/advancements-in-big-data-in-2015