Category Archive: Hadoop

Top 10 Nifty Tools to Manage Big Marketing Data for Companies

Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.

Top 10 Nifty Tools to Manage Big Marketing Data for Companies

However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:


Big Data Salary Report 2017: A Gateway to a Great Career in Analytics

Big Data Salary Report 2017

In the US, big data engineer salaries are predicted to range between $135000 and 196000 in 2017, an increase of 5.8% from 2016 salary structure.  


In India, big data professionals are predicted to earn salaries in the range of 9.8L INR to 13.10L INR, increasing 6.4% over 2016 salary level.


Hadoop 2017: The Survivor and Not the Casualty

Hadoop 2017: The Survivor and Not the Casualty


Most people decipher – Hadoop and Big Data are the two sides of the same coin. Adding the fascinating word to your resume leads to better opportunities and higher pay structure. But what the future holds for Hadoop? Is it dismal or encouraging?


The Future Is In Our Face! How Facial Recognition Will Change Marketing Innovation

Most us are taking a lot of technological marvels around us for granted these days. We have casually taken note of the things like how our smartphones now help us assign photos of people or organize them, or how Facebook usually knows the right people’s faces to tag. However, it has only happened recently, which most people have not realized, that this technology is not so much of a “cool trick” and will actually shape the way people are conducting their business endeavours.

big data facial recognition

These latest technological developments are already being tested out in several different industries and for a lot of different purposes. Like for instance, security scanners at the airports are now making use of this technology to allow the e-passport holders clear their customs faster. And with the further development in facial recognition technology, the border and customs officers will be able to recognize and weed out travellers with fake passports better.


Credit Risk Managers Must use Big Data in These Three Ways

While the developed nations are slowly recovering from the financial chaos of post depression, the credit risk managers are facing growing default rates as household debts are increasing with almost no relief in sight. As per the reports of the International Finance which stated at the end of 2015 that household debts have risen to by USD 7.7 trillion since the year 2007. It now stands at the heart stopping amount of a massive USD 44 trillion and the amount of debts increased in the emerging markets is of USD 6.2 trillion. The household loans of emerging economies calculating as per adult rose by 120 percent over the period and are now summed up to USD 3000.


Credit risk managers must use Big Data in these three ways


To thrive in this market of increasing debts, credit risk managers must consider innovative methods to keep accuracy in check and decrease default rates. A good solution to this can be applying the data analytics to Big Data.


Why the Job Market is Going Gaga over Big Data

We will start off this post with a little bit of trivia.

  • The advertised median salary on offer for technically inclined professionals with expertise in Big Data, which today is a highly sought-after skill is no less than $124,000 inclusive of compensation and bonuses.
  • Cisco, IBM and Oracle together had 26,488 positions that were open during the previous year which required expertise in Big Data.
  • EMC or Dell required 25.1% of all positions in Big Data to have analytics tracks.
  • Data Warehousing, VMWare and developing programming expertise in Python are the fastest growing skill sets that are in demand by companies that are on an expansion of their development teams in Big Data.

why the job market is going gaga over big data


Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Following a massive explosion in the world of data has made the slow paced statisticians into the most in-demand people in the job market right now. But why are all companies whether big or small out for data analysts and scientists?

Companies are collecting data from all possible sources, through PCs, smart phones, RFID sensors,
gaming devices and even automotive sensors. However, just the volume of data is not the main factor that needs to be tackled efficiently, because that is not the only factor that is changing the business environment, but there is the velocity as well as variety of data as well which is increasing at light speed and must be managed with efficacy.

Why data is the new frontier to boost your sales figures?

Earlier the sales personnel were the only people from whom the customers gathered data about the products but today there are various sources from where customers can gather data so people are no longer that heavily reliant on the availability of data.


Things To Be Aware Of Regarding Hadoop Clusters

Hadoop is being increasingly used by companies of diverse scope and size and they are realizing that running Hadoop optimally is a tough call. As a matter of fact it is not humanly possible to respond to the changing conditions in real time as these may take place across several nodes in order to fix dips in performance or those that are causing bottlenecks. This performance degradation is exactly what needsto be critically remedied in cases where Hadoop is deployed on large scales where Hadoop is expected to deliver results critical to your business in the proper time. The following three signs signal the health of your Hadoop cluster.

hadoop clusters

  • The Out of Capacity Problem

The true test of your Hadoop infrastructure comes to fore when you are able to efficiently run all of your jobs and complete them within adequate time. In this it is not rare to come across instances where you have seemingly run out of capacity as you are unable to run additional application. However monitoring tools indicate that are not making full use of processing capability or other resources. The primary challenge that now lies before you is to sort out the root cause of the problem you have. Most often you will find them to be related to the YARN architecture that is used by Hadoop.YARN is static in nature and after the scheduling of jobs the process of adjusting system and network resources. The solution lies in configuring YARN to deal with worst case scenarios.

  • Jobs with High Priority Fail to Finish on Time

All jobs running on clusters are not equally important and there may be present jobs with critical importance that must be completed within a given time frame. And one might find himself in a situation where jobs of high priority are not finishing within the stipulated deadlines.Troubleshooting such problems may be begun by checking parameters or configuration setting that have been modified in the recent past. You may also ask other users of the same cluster whether they have tweaked with settings or applications. This approach is time consuming and not all users will necessary provide all of the information. Up- front planning holds the key to resolve such sorts of resource contention.

  • You Cluster Halts Occasionally

In order to solve problems of this type node monitoring tools often fail to make the grade as their visibility cannot be broken down to the level of users, tasks or jobs. An alternative approach to resolve the problem remains that tools like iostat which monitor all of the processes that use disks significantly. Still you need to anticipate spikes in the usage of disks through such methods and it may not be completed by relying solely on human interaction and technology must be used. It is advisable that you invest in tools that automatically correct any contention problem even while jobs are in progress. Hadoop’s value may be maximized through anticipation of, reacting swiftly to and making real tiomedecisions.


Will Spark Replace Hadoop?

I hope this post will help you to answer some questions related to Apache spark that might be coming into your mind these days related to Spark in Big Data Analytics.

Apache Spark

It is a framework for performing analytics on a distributed cluster, It uses in memory computation over map reduce for better performance and speed. It runs on the top of Hadoop cluster and access Hadoop file system. It can process structured data stored in hive and streaming data from flume.

will spark replace hadoop

Will Spark replace Hadoop?

– Hadoop is a distributed, parallel processing framework that has been used for Map Reduce jobs. These jobs take minutes to hours for completion. Spark has come up as an alternative approach to traditional Map reduce model that can be used for real time data processing and fast interactive queries that complete quite fast. Thus, Hadoop supports both Map Reduce and Apache Spark.

Spark uses in memory storage whereas Hadoop cluster stores data on disk. Hadoop uses replication policy for fault tolerance mechanism whereas Spark uses Resilient Distributed Datasets for fault tolerant mechanism.

Spark features:

1.) Speed – It completes job running in memory on Hadoop Clusters 100 times faster, on disk 10 times faster. It Stores intermediate data in memory using concept of Resilient Distributed Dataset It removes unnecessary read and write on disk for intermediate data.

2.) Easy to use – It allows you to develop your code in JAVA, Scala and Python.

3.) SQL, Complex Analytics and Streaming –Spark supports SQL like features, Complex Analytics likemachine learning.

4.) Runs Everywhere – Spark runs on Hadoop, Mesos, standalone, or in cloud. It can access data in diverse data sources like HDFS, HBase, Cassandra and S3.

Spark Use Cases –

Insurance – optimize claim process by using Spark’s machine learning capabilities to process and analyze all claims being filed.

Retail – Use spark to analyze point of sale transaction data and coupon usage.Used for Interactive Data Processing and Data Mining.


How Data Scientists Take Their Coffee Every Morning

To a data scientist we are all sources of data, from the very moment we wake up in the morning to visit our local Starbucks (or any other local café) to get our morning coffee and swipe the screen of our tablets/iPads or smart phones to go through the big headlines for the day. With these few apparently simple regular exercises we are actually giving the data scientists more data which in-turn allows them to offer tailor-made news articles about things that interest us, and also prepares our favorite coffee blend ready for us to pick up every morning at the café.

How Data Scientists Have Their Coffee

The world of data science came to exist due to the growing need of drawing valuable information from data that is being collected every other day around the world. But is data science? Why is it necessary? A certified data scientist can be best described as a breed of experts who have in-depth knowledge in statistics, mathematics and computer science and use these skills to gather valuable insights form data. They often require innovative new solutions to address the various data problems.

As per estimates from the various job portals it is expected that around 3 million job positions are needed to be fulfilled by 2018 with individuals who have in-depth knowledge and expertise in the field of data analytics and can handle big data. Those who have already boarded the data analytics train are finding exciting new career prospects in this field with fast-paced growth opportunities. So, more and more individuals are looking to enhance their employability by acquiring a data science certification from a reputable institution. Age old programs are now being fast replaced by new comers in the field of data mining with software like R, SAS etc. Although SAS has been around in the world of data science for almost 40 years now, but it took time for it to really make a big splash in the industry. However, it is slowly emerging to be one the most in-demand programming languages these days.What a data science certification covers?

This course covers the topics that enable students to implement advanced analytics to big data. Usually a student after completion of this course acquires an understanding of model deployment, machine language, automation and analytical modeling. Moreover, a well-equipped course in data science helps students to fine-tune their communication skills as well.

Things a data scientist must know:

All data scientists must have good mathematical skills in topics like: linear algebra, multivariable calculus, Python and linear algebra. For those with strong backgrounds in linear algebra and multivariable calculus it will be easy to understand all probability, machine learning and statistics in no time, which is a requisite for the job.