5G stands for fifth generation wireless connectivity based on the IEEE 802.11ac standard of broadband technology, though an official standard is yet to be fixed. It comes with a lot of promises – better bandwidth, faster speed and lower latency that affects (positively) customers and businesses, as well.
Although it isn’t expected to roll out until 2020, a large number of companies have already started prepping up to adopt and incorporate 5G mobile connectivity into their business scopes and operations. As the biggest shift in technology is looming right ahead, business leaders around the world are leaving no stone unturned to fathom the rich impact of 5G on several next-gen techs, like self-driving cars and cloud computing.
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 stop-all.sh and then on start-all.sh
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.
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
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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.
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.
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.
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:
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.
Conclusion
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.
Author’s Bio: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working at Mindmajix.com. 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 savaramravindra4@gmail.com. Connect with him also on LinkedIn and Twitter.
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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.
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.”
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%.
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.
The term Big Data stands for data that is humongous. Large volumes of data are being churned out every day to meet business needs.
Business analytics is the bedrock of an organization. It uses data for proper analysis of business objectives, later on which helps in making better decisions and future profit generation. Also, it aids in determining the actual reasons of failures, re-evaluating risk portfolios, and detecting undergoing fraudulent activities before they swell up to affect business operations.
Keen to have a sweeping view of data science and machine learning as a whole?
Want to crack who is playing tricks with data and what’s happening in and around the budding field of machine learning across industries?
Looking for ways to know how aspiring, young data scientists are breaking into the IT field to invent something new each day?
Hold your breath, tight. The below report showcases few of our intrinsic findings – which we derived from Kaggle’s industry-wide survey. Also, interactive visualizations are on the offer.
On an average, data scientists fall under the age bar of 30 years old, but as a matter of fact, this age limit is subject to change. For example, the average age of data scientists from India tends to be 9 years younger than the average scientists from Australia.
Python is the most commonly used language programs in India, but data scientists at large are relying on R now.
Most of the data scientists are likely to possess a Master’s degree, however those who bags a salary of more than $150K mostly have a doctoral degree under their hood.
Who’s Using Data?
A lot of ways are there to nab who’s working with data, but in here we will fix our gaze on the demographic statistics and the background of people who are working in data science.
What is your age?
To kick start our discussion, according to the Kaggle survey, the average age of respondents was 30 years old subject to some variation. The respondents from India were on an average 9 years younger than those from Australia.
What is your employment situation?
What kind of job title you bag?
Anyone who uses code for data analysis is termed as a data scientist. But how true is this? In the vast realm of data science, there are a series of job titles that can be pegged. For instance, in Iran and Malaysia, the job title of data scientist is not so popular, they like to call data scientists by the name Scientist or Researcher. So, keep a note of it.
How much is your full-time annual salary?
While “compensation and benefits” ranked a little lower than “opportunities for professional developments”, the best part remains it can still be considered a reasonable compensation.
Check out how much a standard machine learning engineer brings home to in the US
What should be the highest formal education?
So, what’s going on in your mind? Should you get your hands on the next formal degree? Normally, most of the data scientists have obtained a full-time master’s degree, even if they haven’t they are at least data analytics’ certified. But professionals who come under a higher salary slab are more likely to possess a doctoral degree.
What are the most commonly used data science methods at work?
Largely, logistic regression is used in all the work areas except the domain of Military and Security, because in here Neural Networks are being implemented extensively.
Which tool is used at work?
Python was once the most used data analytics tool, but now it is replaced by R.
Kaggle is an iconic platform for data scientists, allowing ample scope to connect, understand, discover and explore data. For years, Kaggle has been a diverse platform to drag in hundreds of data scientists and machine learning enthusiasts, and is still in the game.
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