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How Big Data Will Impact E-commerce Industry in 2018?

Whatever happens online and offline, it’s because of DATA. As the technology evolves, the ways to gather and measure data also diversifies. The best way to grasp the data world mechanisms is to study and analyze trends in behavior.

How Big Data Will Impact E-commerce Industry in 2018?

Big data is a concentrated accumulation of conventional and digital data from within and outside company operations. The inception of big data has enabled businesses to use huge amounts of data to carry out bigger and more complicated analyses.


However, the pressing issue that people face today is that they have “too much” data – collecting, organizing and understanding data has become quite complicated because we now are inundated with ceaseless numbers, percentages, stats, facts and perceptions.


To be precise, for years, Big Data has been buzzing around the digital front – let’s delve into what it actually means and what promises it holds in 2018 for ecommerce…


E-commerce industries are the biggest consumers of data. They can extract any information from Big Data and predict customer behavior and streamline robust operations.

Here are 4 ways in which big data will change the shape of e-commerce in 2018:

Better shopper analysis

For online success, understanding shopper’s behavior is more than important. Harness big data; it offers information on trends, customer choices and spikes in demands. It is the key to successful marketing.


Throughout this year and more, big data analytics will continue tracking shopper behaviors and fine-tuning your marketing strategies based on that.


Flawless customer service

 Figures regarding dissatisfied customers and frail customer service are alarming. Truly speaking, more than 90% of unhappy customers won’t like to do business with a company that has turned their expectations down, owing to poor customer service. Therefore, for ecommerce success, utmost focus on customer service is downright important.


This year, expect data analytics to improve customer experience, while giving more focus to predictive monitoring. This will aid companies in identifying crucial issues and resolve them before even a customer gets involved.

More secure and easy online payment options

Since big data came into our lives, several things, like online payments got easier and more secure. How?


  • Big data incorporates various payment functions in a single centralized platform. It helps in making the process easier, as well as reduces fraud risks.
  • The advanced analytics are powerful enough to identify threats and structure proactive solutions to combat potent risks.
  • Big data helps in detecting money laundering transactions.
  • Productive data analytics allows e-commerce chains to cross sell and upsell.

Mobile commerce evolution

Day by day, the use of smartphones is increasing. The use of desktop computers is soon becoming obsolete. Big data is making impossible things possible, especially in the world of smartphones and ecommerce. Companies can now very easily gather data from multiple sources and analyze customer trends through mobile technology. Google has pioneered a wave of technologies, giving preference to mobile friendly and highly responsive sites. They bring in higher traffic to their pages. Hence an instant hit!


As closing thoughts, ecommerce companies wholeheartedly thanks Big Data for the way it has simplified the process of online shopping. For more big data inspiration and blogs, follow DexLab Analytics.


Our advanced Big Data certification in Delhi NCR is excellent. Hone your skills with big data hadoop training and soar to success.

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If Big Data is the Problem, Then Hadoop is the Solution

If Big Data is the Problem, Then Hadoop is the Solution

A lot of IT professionals and tech nerds are curious to learn about the difference between Big Data and Hadoop. A majority of them are yet to understand the subtle line of distinction between the two. And the increasing prominence and popularity of Big Data Hadoop certification has further added to the confusion.

Importantly, Big Data and Hadoop, the most popular open-source Hadoop program actually ends up complementing each other, in every way. If you think of Big Data as a problem then Hadoop acts like a solution for that problem – yes, they are that much compatible and complementary to each other. While big data is a dubious and complex concept, Hadoop being a simple, open source program that helps in fulfilling a certain creed of objectives of asset, in this case Big Data.

The best way to explain this issue would be by talking about the challenges associated with Big Data and how Hadoop efficiently resolves them – this would be the best way to know the differences between the two.


Challenges with Big Data

Big Data is best defined with 5 characteristics: Volume, Variety, Velocity, Value and Veracity. Here, volume depicts the quantity of data, variety means the kind of data, velocity is the rate at which data is being generated, value points at the usefulness of the data and veracity is the amount of inconsistent data.

Now, let’s talk about two of the emerging problems with Big Data:

  • Storage The archaic storage solutions are not adept enough to store such mammoth amount of data that is being generated every day. Moreover, the variety of data is different, thus the data needs to be stored separately for effective use.
  • Speed of accessing and processing data Though the hard disk capacities have increased manifold, not much development has been done on the front of the speed of accessing or processing data.

But no more, you have to worry about all these issues, as Hadoop is here. It has effectively mitigated all the above-mentioned challenges and made big data powerful as a rock!

What is Hadoop?

Generally speaking, Hadoop is an open source programming platform – it helped big data to get stored in distributed environments so as to be processed in a parallel way. It is composed of two important elements – Hadoop Distributed File System (HDFS) and YARN (Yet Another Resource Negotiator), Hadoop’s processing unit.

Now, let’s see how Hadoop resolves the emerging big data challenges:

  • Storage – With the help of HDFS, Big Data can now be stored in a proper distributed manner. For that, datanode block is used, it’s an efficient storage solution and allows you to specify the size of every block in use. Additionally, it doesn’t only divides the data across different blocks but also replicated all the blocks on the data nodes, thus making way for better storage solution.
  • The speed of accessing and processing data – Instead of relying on traditional methodologies, Hadoop prefers moving processing to the data, which means the processing dynamo is moved across different slave nodes and parallel processing of data is carried on throughout the slave nodes. And the processed results are then shifted into a master node, where a mixing of data takes place and the response arising out of it is sent to the client.

Hence, you can see how big data and hadoop are related to each other, not like alternatives but like complements. So, to climb the ladder of success and be an ace developer or data scientist, Big Data Hadoop certification in Gurgaon is your go-to option. Get Big Data Hadoop certification today from DexLab Analytics.


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Incorporating Hadoop into Adobe Campaign for Advanced Segmentation and Personalization

Big data is the new CRAZE. Reports suggest that investments in big data have surpassed $57 billion in 2017, and are expected to rise by 10% for the next three years.

Incorporating Hadoop into Adobe Campaign for Advanced Segmentation and Personalization

Customers are happy – those who have applied advanced capabilities to predictive analytics, machine learning, customer analytics, customer profiles, inventory management and tracking, and more – as big data implementation across many verticals has resulted in measurable positive results.

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10 Frequently-asked Hadoop Interview Questions with Answers

10 Frequently-asked Hadoop Interview Questions with Answers

A substantial part of the Apache project, Hadoop is an open source, Java-based programming software framework that is used for storing data and running applications on different clusters of commodity hardware. Be it any kind of data, Hadoop acts as a massive storage unit backed by gargantuan processing power and an ability to tackle virtually countless tasks and jobs, simultaneously.

In this blogpost, we are going to discuss top 10 Hadoop interview questions – cracking these questions may help you bag the sexiest job of this decade.

What are the components of Hadoop?

There are 3 layers in Hadoop and they are as follows:

  • Storage layer (HDFS) – Also known as Hadoop Distributed File System, HDFS is responsible for storing various forms of data as blocks of information. It includes NameNode and DataNode.
  • Batch processing engine (MapReduce) For parallel processing of large data sets across a standard Hadoop cluster, MapReduce is the key.
  • Resource management layer (YARN) Yet Another Resource Negotiator is the powerful processing framework in Hadoop system that keeps a check on the resources.

Why is Hadoop streaming?

Hadoop distribution includes a generic application programming interface for drawing MapReduce jobs in programming languages like Ruby, Python, Perl, etc. and this is known as Hadoop streaming.


What are the different modes to run Hadoop?

  • Local (standalone) Mode
  • Pseudo-Distributed Mode
  • Fully-Distributed Mode

How to restart Namenode?

Begin by clicking on and then on


Write sudo hdfs (then press enter), su-hdfs (then press enter), /etc/init.d/ha (then press enter) and finally /etc/init.d/Hadoop-0.20-name node start (then press enter).

How can you copy files between HDFS clusters?

Use multiple nodes and the distcp command to ensure smooth copying of files between HDFS clusters.

What do you mean by speculative execution in Hadoop?

In case, a node executes a task slower, the master node has the ability to start the same task on another node. As a result, the task that finishes off first will be accepted and the other one will be rejected. This entire procedure is known as “speculative execution”.

What is “WAL” in HBase?

Here, WAL stands for “Write Ahead Log (WAL)”, which is a file located in every Region Server across the distributed environment. It is mostly used to recover data sets in case of mishaps.

How to do a file system check in HDFS?

FSCK command is your to-go option to do file system check in HDFS. This command is extensively used to block locations or names or check overall health of any files.


hdfs fsck /dir/hadoop-test -files -blocks –locations

What sets apart an InputSplit from a Block?

A block divides the data, physically without taking into account the logical equations. This signifies you can posses a record that originated in one block and stretches over to another. On the other hand, InputSplit includes the logical boundaries of records, which are crucial too.

Why should you use Storm for Real-Time Processing?

  • Easy to operate simple operating system makes it easy
  • Fast processing it can process around 100 messages per second per node
  • Fault detection it can easily detect faults and restarts functional attributes
  • Scores high on reliability expect execution of each data unit at least for once
  • High scalability it operates throughout clusters of machines

The article has been sourced from


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Flipkart Launches a New Internal Wing AIforIndia to Bet Big on Artificial Intelligence

Flipkart is strengthening its base in the field of Artificial Intelligence, and so far, this year has been treating them well. After pegging fresh influx of funds, appointing a new CEO at the helm of affairs and reportedly thwarting its tailing rival, Amazon in September, Flipkart is all set to enter the most promising arena of artificial intelligence and machine learning, NOW.


Flipkart Launches a New Internal Wing AIforIndia to Bet Big on Artificial Intelligence


In an interview to a leading daily journal, Sachin Bansal, the notable co-founder and Chairman of Flipkart is found saying – “we ready to invest hundreds of millions of dollars” in the AI gambit over the next few years. “This is the next big thing for us, where we are betting big on the use of AI and machine learning to solve problems at Flipkart. India’s problems are unique and we need to apply AI in the ecosystem to solve Indian problems. We believe that some of the focus areas for AI in developed countries cannot be applied for India. At Flipkart, we will solve problems differently because the underlying problems (in India) are different,” he states, adding that they has already started building the needed infrastructure, recruiting a dozen AI buffs and establishing partnerships with crème de la crème educational institutions, including the IITs to give a robust push to its inspiring AI initiative.

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Top 6 Big Data Trends for 2018

Big data is expanding, and by next year almost a majority of businesses will be attracted towards the brighter prospect of this cutting edge technology. Even this year saw an enormous increase in volume, variety, velocity of data, which assures that the next year will witness more data, more numbers.

Top 6 Big Data Trends for 2018

Data science pundits have predicted some of the leading trends that would be in the forefront in the big data revolution 2018. Come, let’s take a look:

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The Impact of Big Data on Marketing

The Impact of Big Data on Marketing

In marketing, the analysis of data is a highly established one but the marketers nowadays have a massive amount of public and proprietary data about the preferences, usage, and behavior of a customer. The term ‘big data’ points out to this data explosion and the capability to use the data insights to make informed decisions. Understanding the potential of big data presents various technical challenges but it also needs executive talent devoted to applying the solutions of big data. Today, the marketers are widely embracing big data and are confident in their use of analytics tools and techniques. Let us learn about the ways in which Big data and analytics can improve the marketing efforts of various businesses around the around.

Locating Prospective Customers

Previously, marketers had to frequently make guesses as to which sector of population comes under their ideal market segment but this is no longer the scenario today. The companies can exactly see who is buying and even extract more details about them with the help of big data. The other details include which buttons they generally click while on a website, which websites they visit frequently, and which social media channels they utilize.

Tracking Impact and ROI

Many retailers have introduced loyalty card systems that track the purchases of a customer, but these systems can also track which promotions and incentives are most effective in encouraging a group of customers or a single customer to make another purchase.

Handling Marketing Budgets

Because big data allows companies to optimize and monitor their marketing campaigns for performance, this implies they can allocate their budget for marketing for the highest return-on-investment (ROI).

Personalizing Offers in Real-Time

Marketers can personalize their offers to customers in real time with the combination of big data and machine learning algorithms. Think about the Amazon’s “customers also bought” section or the recommended list of TV shows and movies from Netflix. The organizations can personalize what promotions and products a particular customer views, even down to sending personalized offers and coupons to the mobile phone of a customer when he walks into a physical location. The role of Personalized Merchandising in the ecommerce industry will continue to increase in the years to come.

Improvement in Market Research

Companies can conduct quantitative and qualitative market research much more inexpensively and quickly than ever before. The tools for online survey mean that customer feedback and focus groups are inexpensive and easy to implement, and data analytics make the results easier to take action.

Prediction of Buyer Behavior and Sales

For the past several years, sales teams, in order to rate their hottest leads, have made use of lead scoring. But, with the help of predictive analytics, a model can be generated and it can successfully predict sales and buyer behavior.



Enhanced Content Marketing

Previously, the return-on-investment for a blog post used to be highly difficult to measure. But, with the help of big data and analytics, the marketers can effortlessly analyze which pieces of content are highly effective at moving leads via a sales and marketing funnel. Even a small firm can afford to use tools for implementing content scoring which can highlight the content pieces that are highly responsible for closing sales.

Optimize Customer Engagement

Data can provide more information about your customers which includes who they are, what they want, where they are, how often they purchase on your site, and how, when they prefer to be contacted, and various other major factors. The organizations can also examine how users interact not only with their website, but also their physical store to enhance the experience of the user.

Tracking Competitors

New tools for social monitoring have made it easy to gather and examine data about the competitors and their efforts regarding marketing as well. The organizations that can utilize this data will have a distinct competitive advantage.

Managing Reputation

With the help of big data, organizations can monitor their brand mentions very easily across different social channels and websites to locate unfiltered testimonials, reviews, and opinions about their company and products. The savviest can also utilize social media to offer service to the customers and create a trustworthy brand presence.

Marketing Optimization

It is quite difficult to track direct ROI and impact with traditional advertising. But, big data can help organizations to make optimal marketing buys across various channels and to optimize their marketing efforts continuously through analysis, measurement, and testing.

What is Needed for Big Data?

At this point, talent and leadership are the major things that big data needs. In most of the companies, the marketing teams don’t have the right talent in place to leverage analytics and data. Apart from people who possess analytical skills to understand the capability of big data and where to use it, companies require data scientists who can extract meaningful insights from data and the technologists who can develop include new technologies. Due to this, there is a high demand for experienced analytics talent today.

Big Data Limitations for Marketing

In spite of all the promise, there exist certain limits to the usefulness of big data analytics in its present state. Among them, the major one is the major one is the analytics tools’ and techniques’ complex “black box” nature which makes it hard to trust and interpret the output of the approaches of big data and to assure others of the accuracy and value of the insights generated by the tools. The difficulty of gathering and understanding data also limits the capability of marketing companies to more fully leverage big data. Beyond this, the marketers are identifying many hurdles to expanding their utilization of big data tools and they include lack of sufficient technology investment, the inability of senior team members to leverage big data tools for decision-making, and the lack of credible tools for measuring effectiveness.


Cloud computing is also playing a major role in marketing with the Cloud Marketing process. Cloud Marketing is a process that outlines the efforts of a company to market their services and goods online via integrated digital experiences. Once the data analytics tools become available and accessible to even the smallest businesses, there will be a much higher impact of big data on the marketing sector as there will be much broader utilization of data analytics. This can only be a boon as organizations enhance their marketing and reach their customers in innovative and new ways.

This article was produced by Savaram Ravindra, a content contributor at Mindmajix and not by the editorial team of DexLab Analytics, a leading Hadoop training institute in Gurgaon.


Author’s Bio: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working at His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at Connect with him also on LinkedIn and Twitter.


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For Long-term Digital Transformation Plan, Big Data is the Key

Big data and business analytics are like two sides of the same coin. Here, though the coin represents digital transformation – but reports from consulting and services firm HCL Technologies are pointing that many companies are not being able to harness these new-age technologies to their fullest capacities resulting in a loss of digital transformation efforts.

For Long-term Digital Transformation Plan, Big Data is the Key

When asked Anand Birje, the corporate vice president and head of HCL’s digital and analytics domain, he has this to say, “Over the past four or five years, enterprises were pushed hard to do anything in the field of analytics, big data and digital transformation. They were being pushed because there was this fear about what their competitors might be doing, so there was this feeling that they had to do something digital.”

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Here’s ALL About Global Hadoop Market and Investment Report 2017

According to a market research report, Global Hadoop market – industry analysis, share, size, growth, trends and forecast, which was once estimated at a value worth USD 1.5 billion in 2012, is now expected to hit $13.95 Billion mark this year, 2017 with a CAGR of 54.9%.

Here’s ALL About Global Hadoop Market and Investment Report 2017

The advent of Hadoop platform stemmed out from the growing urge to manage problems that resulted owing to a lot of data – mostly a concoction of structured and unstructured data – that failed to fit properly in the traditional data storage and management systems, like tables. The play of analytics got intense, more complicated – both computationally and logically – hence the need for Hadoop is more than ever. This is similar to what Google was doing while it was on an endeavor to examine its user behaviors and index web pages, with a view to enhance its own performance algorithms.

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