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

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

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

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