How Do Businesses Perceive Big Data Analytic Tools and Hadoop?

[Ann_I_]

Hello every one, I am a final year computer science undergraduate student. I am currently researching on the strengths and weaknesses of Big Data Analytic Tools and how they are perceived by businesses. This research is a part of my university course work and I thought I could use this medium to collect data so as to be able to get going with my research.

Questions:

1)How is Hadoop perceived by businesses: Do you think that it is a good tool?

3)What characteristics does Hadoop have that makes you think so?

4)Is Hadoop frequently used in businesses today?

5)What kinds of businesses uses Hadoop?

6)Are there any benefits or problems associated with Hadoop?

7)Would you recommend any alternative tool for big data analytics if so which would you recommend and why?

8)Which distribution of Hadoop do you consider best for data analytics and why?

Kindly share your thoughts on the above questions to help me get started. Thanks in advance.

[PONRAM]

1)How is Hadoop perceived by businesses: Do you think that it is a good tool?

Hadoop is an Open source and highly affordable tool 

[PONRAM]

3)What characteristics does Hadoop have that makes you think so?

1.Distributed meta data

2.Low latency

3.High Availability

4.Support All application

5.Cross Platform Support

[PONRAM]

4)Is Hadoop frequently used in businesses today?

Yes Since Hadoop  offers competitive service than any other commerical package. Its wide range of developer community makes hadoop a super hit in 

Business Industry

[PONRAM]

5)What kinds of businesses uses Hadoop?

Social Media Data

Web media data

sensor array data 

geolocation data

market analysis and customer behavior data

are some of business groups using hadoop in large quantum

[PONRAM]

6. The Main Benefits are it can be customized easily as per your suit and cost effective.

[PONRAM]

I Will recommend Big Query from google

[PONRAM]

It would be Cloudera 

[Iman_S_Intel]

One of the new solutions for data analytics is the "Intel® Data Analytics Acceleration Library" (Intel DAAL)

You can learn more about it through this forum:

https://software.intel.com/en-us/forums/topic/544653

[yaswanth_k_]

some of the skills you may required for "BIG DATA"

1. Apache Hadoop

Sure, it’s entering its second decade now, but there’s no denying that Hadoop had a monstrous year in 2014 and is positioned for an even bigger 2015 as test clusters are moved into production and software vendors increasingly target the distributed storage and processing architecture. While the big data platform is powerful, Hadoop can be a fussy beast and requires care and feeding by proficient technicians. Those who know there way around the core components of the Hadoop stack–such as HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN–will be in high demand.

2. Apache Spark

If Hadoop is a known quantity in the big data world, then Spark is a black horse candidate that has the raw potential to eclipse its elephantine cousin. The rapid rise of the in-memory stack is being proffered as a faster and simpler alternative to MapReduce-style analytics, either within a Hadoop framework or outside it. Best positioned as one of the components in a big data pipeline, Spark still requires technical expertise to program and run, thereby providing job opportunities for those in the know.

3. NoSQL

On the operational side of the big data house, distributed, scale-out NoSQL databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBM DB2. On the Web and with mobile apps, NoSQL databases are often the source of data crunched in Hadoop, as well as the destination for application changes put in place after insight is gleaned from Hadoop. In the world of big data, Hadoop and NoSQL occupy opposite sides of a virtuous cycle.

4. Machine Learning and Data Mining

People have been mining for data as long as they’ve been collecting it. But in today’s big data world, data mining has reached a whole new level. One of the hottest fields in big data last year is machine learning, which is poised for a breakout year in 2015. Big data pros who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalization systems are in super high demand, and can command top dollar in the job market.

5. Statistical and Quantitative Analysis

This is what big data is all about. If you have a background in quantitative reasoning and a degree in a field like mathematics or statistics, you’re already halfway there. Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata, and you’ve got this category locked down. In the past, most quants went to work on Wall Street, but thanks to the big data boom, companies in all sorts of industries across the country are in need of geeks with quantitative backgrounds.

6. SQL

The data-centric language is more than 40 years old, but the old grandpa still has a lot of life yet in today’s big data age. While it won’t be used with all big data challenges (see: NoSQL above), the simplify of Structured Query Language makes it a no-brainer for many of them. And thanks to initiatives like Cloudera‘s Impala, SQL is seeing new life as the lingua franca for the next-generation of Hadoop-scale data warehouses.

7. Data Visualization

Big data can be tough to comprehend, but in some circumstances there’s no replacement for actually getting your eyeballs onto data. You can do multivariate or logistic regression analysis on your data until the cows come home, but sometimes exploring just a sample of your data in a tool like Tableau or Qlikview can tell you the shape of your data, and even reveal hidden details that change how you proceed. And if you want to be a data artist when you grow up, being well-versed in one or more visualization tools is practically a requirement.

8. General Purpose Programming Languages

Having experience programming applications in general-purpose languages like Java, C, Python, or Scala could give you the edge over other candidates whose skill sets are confined to analytics. According to Wanted Analytics, there was a 337 percent increase in the number of job postings for “computer programmers” that required background in data analytics. Those who are comfortable at the intersection of traditional app dev and emerging analytics will be able to write their own tickets and move freely between end-user companies and big data startups.

9. Creativity and Problem Solving

No matter how many advanced analytic tools and techniques you have on your belt, nothing can replace the ability to think your way through a situation. The implements of big data will inevitably evolve and new technologies will replace the ones listed here. But if you’re equipped with a natural desire to know and a bulldog-like determination to find solutions, then you’ll always have a job offer waiting somewhere.

[yaswanth_k_]

Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers". What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered "big" one year becomes ordinary later. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.

[agnes_m_]

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