Big data courses Archives - Page 2 of 18 - DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

Big Data and Its Influence on Netflix

Big Data and Its Influence on Netflix

With resonating hits, like Bird Box and Bandersnatch, Netflix is revolutionizing the entertainment industry – all with the power of big data and predictive analytics.

Big Data Analytics is the heart and soul of Netflix, says the Wall Street Journal. Not only the company relies on big data to optimize its video streaming quality but also to tap into customer entertainment preferences and content viewing pattern. This eventually helps Netflix target its subscribers with content and offers on shows they prefer watching.

2

Committed to Data, Particularly User Data

With nearly 130 million subscribers, Netflix needs to collect, manage and analyze colossal amounts of data – all for the sole purpose of enhancing user experience. Since its inception days of being a mere DVD distributor, Netflix has always been obsessed about user data. Even then, the company had an adequate reservoir of user data and a robust recommendation system. However, it was only after the launch of its incredible streaming service that Netflix took the game of data analytics to an altogether different level. 

In fact, Netflix invested $1 million in a cutting-edge developer company for coming up with an algorithm that increased the accuracy of their already-existing recommendation engine by almost 10%. For this, Netflix can now save $1 billion annually from customer retention.

Netflix Already Knows What You Going to Watch Next

Yes, Netflix is a powerhouse of user behavior information. The content streaming giant knows your viewing habits better than you – courtesy pure statistics, preferably predictive analytics. This is one of the major strengths of Netflix – the way it analyzes data, adjusts algorithms and optimizes video streaming experience is simply incredible.

However, nothing great comes easy. Close monitoring of user viewing habits is essential. Right from how much time each user spends on picking movies to the number of times he/she watches a particular show, each and every data is extremely important. Moreover, conventional calculus helps Netflix in understanding its user behavior trends and necessarily provides them with appropriate customized content.

As closing thoughts, Netflix is a clear-cut answer to how technological advancement has influenced human creativity beyond levels. Powered by big data and predictive analytics, Netflix has surely debunked several lame theories on content preference and customer viewing habits. So, if you are interested in big data Hadoop training in Delhi, this is the time to act upon. With DexLab Analytics by your side, you can definitely give wings to your dreams, specifically data dreams. 

 
The blog has been sourced fromwww.muvi.com/blogs/deciphering-the-unstoppable-netflix-and-the-role-of-big-data.html
 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Big Data Enhances Remote IT Support: Here’s How

Big Data Enhances Remote IT Support: Here’s How

Big data is the backbone of modern businesses. All their decisions are data driven. Firstly, information is aggregated from various sources, like customer viewing pattern and purchasing behavior. After this, the data is analyzed and then actionable insights are generated. Nowadays, most companies rely on some type of business intelligence tool. All in all, information collection is increasing exponentially.

However, in many cases the desire for information has gone too far. The recent scandal involving Facebook and Cambridge Analytica stands as an example. It has left people very insecure about their online activities. Fears regarding violation of privacy are rising; people are worried that their data is being monitored constantly and even used without their awareness. Naturally, everyone is pushing for improved data protection. And we’re seeing the results too – General Data Protection Regulation (GDPR) in EU and the toughening of US Data Regulations is only the beginning.

Although data organization and compliance have always been the foundation of IT’s sphere of activity, still businesses are lagging behind in utilizing big data in remote IT support. They have started using big data to enhance their services only very recently.

2

Advantages of data-directed remote IT support

The IT landscape has undergone a drastic change owing to the rapid advancement of technology. The rate at which devices and software packages are multiplying, Desktop Management is turning out to be a nightmarish task. Big data can help IT departments manage this situation better.

Managing complexity and IT compliance

The key reasons behind maximum number of data breaches are user errors and missing patches. Big data is very useful in verifying if endpoints are on conformity with IT policies, which in turn can help prevent such vulnerabilities and keep a check on networks.

Troubleshooting and minimizing time-to-resolution

Data can be utilized to develop a holistic picture of network endpoints, ensuring the helpdesk process is more competent. By offering deeper insight into networks, big data allows technicians to locate root causes behind ongoing issues instead of focusing on recurring symptoms. The direct effect of this is an increase in first-call-resolution. It also helps technicians to better diagnose user problems.

Better end-user experience

Having in-depth knowledge about all the devices of a network means that technicians don’t have to control an end-user’s system to solve the issue. Also, this enables the user to continue working uninterrupted while the technician takes care of the problem from behind-the-scene. Thus, IT can offer a remedy even before the user recognizes there’s a problem. For example, a team engaged in collection of network data may notice that few devices need to be updated, which they can perform remotely.

Better personalization without damaging control

IT teams have always found it difficult to manage provisioning models, like BYOD (bring your own device) and COPE (corporate owned, personally enabled). But with the help of big data, IT teams can divide end users based on their job roles and also support the various provisioning models without compromising with control. Moreover, they constantly receive feedback, allowing them keep to a check on any form of abuse, unwanted activities and any changes in the configuration of a system.

Concluding:

In short, the organization as a whole benefits from data-directed remote support. IT departments can improve on their delivery service as well as enhance end-user experience. It gives users more flexibility, but doesn’t hamper security of IT teams. Hence, in this age of digital revolution, data-driven remote support can be a powerful weapon to improve a company’s performance.

Knowing how to handle big data is the key to success in all fields of work. That being said, candidates seeking excellent Big Data Hadoop training in Gurgaon should get in touch with DexLab Analytics right away! This big data training center in Delhi NCR offer courses with comprehensive syllabus focused on practical training and delivered by professionals with excellent domain experience.

 
Reference: https://channels.theinnovationenterprise.com/articles/how-big-data-is-improving-remote-it-support
 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Big Data and Its Use in the Supply Chain

Big Data and Its Use in the Supply Chain

Data is indispensable, especially for modern business houses. Every day, more and more businesses are embracing digital technology and producing massive piles of data within their supply chain networks. But of course, data without the proper tools is useless; the emergence of big data revolution has made it essential for business honchos to invest in robust technologies that facilitate big data analytics, and for good reasons.

Quality Vs Quantity

The overwhelming volumes of data exceed the ability to analyze that data in a majority of organizations. This is why many supply chains find it difficult to gather and make sense of the voluptuous amount of information available across multiple sources, processes and siloed systems. As a result, they struggle with reduced visibility into the processes and enhanced exposure to cost disruptions and risk.

To tackle such a situation, supply chains need to adopt comprehensive advanced analytics, employing cognitive technologies, which ensure improved visibility throughout their enterprises. An initiative like this will win these enterprises a competitive edge over those who don’t.

2

Predictive Analytics

 A striking combination of AI, location intelligence and machine learning is wreaking havoc in the data analytics industry. It is helping organizations collect, store and analyze huge volumes of data and run cutting edge analytics programs. One of the finest examples is found in drone imagery across seagrass sites.

Thanks to predictive analytics and spatial analysis, professionals can now realize their expected revenue goals and costs from a retail location that is yet to come up. Subject to their business objectives, consultants can even observe and compare numerous potential retail sites, decrypting their expected sales and ascertain the best possible location. Also, location intelligence helps evaluate data, regarding demographics, proximity to other identical stores, traffic patterns and more, and determine the best location of the new proposed site.

The Future of Supply Chain

Talking from a logistic point of view, AI tools are phenomenal – IoT sensors are being ingested with raw data with their aid and then these sensors are combined with location intelligence to formulate new types of services that actually help meet increasing customer demands and expectations. To prove this, we have a whip-smart AI program, which can easily pinpoint the impassable roads by using hundreds and thousands of GPS points traceable from an organization’s pool of delivery vans. As soon as this data is updated, route planners along with the drivers can definitely avoid the immoderate missteps leading to better efficiency and performance of the company.

Moreover, many logistics companies are today better equipped to develop interesting 3D Models highlighting their assets and operations to run better simulations and carry a 360-degree analysis. These kinds of models are of high importance in the domain of supply chains. After all, it is here that you have to deal with the intricate interplay of processes and assets.

Conclusion

 Since the advent of digital transformation, organizations face the growing urge to derive even more from their big data. As a result, they end up investing more on advanced analytics, local intelligence and AI across several supply chain verticals. They make such strategic investments to deliver efficient service across the supply chains, triggering higher productivity and better customer experience.

With a big data training center in Delhi NCR, DexLab Analytics is a premier institution specializing in in-demand skill training courses. Their industry-relevant big data courses are perfect for data enthusiasts.

 
The blog has been sourced fromwww.forbes.com/sites/yasamankazemi/2019/01/29/ai-big-data-advanced-analytics-in-the-supply-chain/#73294afd244f
 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Big Data to Cure Alzheimer’s Disease

Big Data to Cure Alzheimer’s Disease

Almost 44 million people across the globe suffer from Alzheimer’s disease. The cost of the treatment amounts to approximately one percent of the global GDP. Despite cutting-edge developments in medicine and robust technology upgrades, prior detection of neurodegenerative disorder, such as Alzheimer’s disease remains an upfront challenge. However, a breed of Indian researchers has assayed to apply big data analytics to look for early signs of the Alzheimer’s in the patients.

The whip-smart researchers from the NBRC (National Brain Research Centre), Manesar have come up with a fierce big data analytics framework that will implement non-invasive imaging and other test data to detect diagnostic biomarkers in the early stages of Alzheimer’s.

The Hadoop-powered data framework integrates data from brain scans in the format of non-invasive tests – magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI) and neuropsychological test results – by employing machine learning, data mining and statistical modeling algorithms, respectively.

2

The framework is designed to address the big three Vs – Variety, Volume and Velocity. The brain scans conducted using MRS or MRI yields vast amounts of data that is impossible to study manually or analyze data of multiple patients to determine if any pattern is emerging. As a result, machine learning is the key. It boosts the process, says Dr Pravat Kumar Mandal, a chief scientist of the research team.

To know more about the machine learning course in India, follow DexLab Analytics. This premier institute also excels in offering state of the art big data courses in Delhi – take a look at their course itinerary and decide for yourself.

The researchers are found using data about diverse aspects of the brain – neurochemical, structural and behavioural – accumulated through MRS, MRI and neuropsychological mediums. These attributes are ascertained and classified into collectives for clear diagnosis by doctors and pathologists. The latest framework is regarded as a multi-modalities-based decision framework for early detection of Alzheimer’s, clinicians have noted in their research paper published in journal Frontiers in Neurology. The project has been termed BHARAT and has been dealing with the brain scans of Indians.

The new framework integrates unstructured and structured data, processing, storage, and possesses the ability to analyze volumes and volumes of complex data. For that, it leverages the skills of parallel computing, data organization, scalable data processing and distributed storage techniques, besides machine learning. Its multi-modal nature helps in classifying between healthy old patients with mild cognitive impairment and those suffering from Alzheimer’s.

Other such big data tools for early diagnostics are only based on MRI images of patients. Our model incorporates neurochemical-like antioxidant glutathione depletion analysis from brain hippocampal regions. This data is extremely sensitive and specific. This makes our framework close to the disease process and presents a realistic approach,” says Dr Mandal.

As endnotes, the research team comprises of Dr Mandal, Dr Deepika Shukla, Ankita Sharma and Tripti Goel, and the research is supported by the adept Ministry of Department of Science and Technology. The forecast predicts the number of patients diagnosed with Alzheimer is expected to cross 115 million-mark by 2050. Soon, this degenerative neurological disease will pose a huge burden on the economies of various countries; hence it’s of paramount importance to address the issue now and in the best way possible.

 

The blog has been sourced from www.thehindubusinessline.com/news/science/big-data-may-help-get-new-clues-to-alzheimers/article26111803.ece

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Big Data: 4 Myths and 4 Methods to Improve It

The excitement over big data is beginning to tone down. Technologies like Hadoop, cloud and their variants have brought about some incredible developments in the field of big data, but a blind pursuit of ‘big’ might not be the solution anymore. A lot of money is still being invested to come up with improved infrastructure to process and organize gigantic databases. But the costs sustained in human resources and infrastructure from trying to boost big data activities can actually be avoided for good – because the time has come to shift focus from ‘big data’ to ‘deep data’. It is about time we become more thoughtful and judicious with data collection. Instead of chasing quantity and volume, we need to seek out quality and variety. This will actually yield several long-term benefits.

2

Big Myths of Big Data

To understand why the transition from ‘big’ to ‘deep’ is essential, let us look into some misconceptions about big data:

  1. All data must be collected and preserved
  2. Better predictive models come from more data
  3. Storing more data doesn’t incur higher cost
  4. More data doesn’t mean higher computational costs

Now the real picture:

  1. The enormity of data from web traffic and IoT still overrules our desire to capture all the data out there. Hence, our approach needs to be smarter. Data must be triaged based on value and some of it needs to be dropped at the point of ingestion.
  2. Same kind of examples being repeated a hundred times doesn’t enhance the precision of a predictive model.
  3. Additional charges related to storing more data doesn’t end with the extra dollars per terabyte of data charged by Amazon Web Services. It also includes charges associated with handling multiple data sources simultaneously and the ‘virtual weight’ of employees using that data. These charges can even be higher than computational and storage costs.
  4. Computational resources needed by AI algorithms can easily surpass an elastic cloud infrastructure. While computational resources increase only linearly, computational needs can increase exponentially, especially if not managed with expertise.

When it comes to big data, people tend to believe ‘more is better’.

Here are 3 main problems with that notion:

  1. Getting more of the same isn’t always useful: Variety in training examples is highly important while building ML models. This is because the model is trying to understand concept boundaries. For example, when a model is trying to define a ‘retired worker’ with the help of age and occupation, then repeated examples of 35 year old Certified Accountants does little good to the model, more so because none of these people are retired. It is way more useful if examples at the concept boundary of 60 year olds are used to indentify how retirement and occupation are dependent.
  2. Models suffer due to noisy data: If the new data being fed has errors, it will just make the two concepts that an AI is trying to study more unclear. This poor quality data can actually diminish the accuracy of models.
  3. Big data takes away speed: Making a model with a terabyte of data usually takes a thousand times more time than preparing the same model with a gigabyte of data, and after all the time invested the model might fail. So it’s smarter to fail fast and move forward, as data science is majorly about fast experimentation. Instead of using obscure data from faraway corners of a data lake, it’s better to build a model that’s slightly less accurate, but is nimble and valuable for businesses.

How to Improve:

There are a number of things that can be done to move towards a deep data approach:

  1. Compromise between accuracy and execution: Building more accurate models isn’t always the end goal. One must understand the ROI expectations explicitly and achieve a balance between speed and accuracy.
  2. Use random samples for building models: It is always advisable to first work with small samples and then go on to build the final model employing the entire dataset. Using small samples and a powerful random sampling function, you can correctly predict the accuracy of the entire model.
  3. Drop some data: It’s natural to feel overwhelmed trying to incorporate all the data entering from IoT devices. So drop some or a lot of data as it might muddle things up in later stages.
  4. Seek fresh data sources: Constantly search for fresh data opportunities. Large texts, video, audio and image datasets that are ordinary today were nonexistent two decades back. And these have actually enabled notable breakthroughs in AI.

What all get’s better:

  • Everything will be speedier
  • Lower infrastructure costs
  • Complicated problems can be solved
  • Happier data scientists!

Big data coupled with its technological advancements has really helped sharpen the decision making process of several companies. But what’s needed now is a deep data culture. To make best of powerful tools like AI, we need to be clearer about our data needs.

For more trending news on big data, follow DexLab Analytics – the premier big data Hadoop institute in Delhi. Data science knowledge is becoming a necessary weapon to survive in our data-driven society. From basics to advanced level, learn everything through this excellent big data Hadoop training in Delhi.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Top Things to Know About Scala Programming Language

Top Things to Know About Scala Programming Language

Scalable Language, Scala is a general-purpose programming language, both object-oriented and highly functional programming language. It is easy to learn, simple and aids programmers in writing codes in a simple, sophisticated and type-safe manner. It also enables developers and programmers in being more productive.

Even though Scala is a relatively new language, it has garnered enough users and has wide community support – because it’s touted as the most user-friendly language.

About Scala and Its Features

Scala is a completely object-oriented programming language

In Scala, everything is treated as an object. Even, the operations you conduct are termed as a method call. Scala lets you add new operations to already existing classes – thanks to the implicit classes.

One of the best things about Scala is that it makes it effortlessly easy to interact with Java code. You can easily write a Java code inside Scala class – interesting, isn’t it? The Scala makes way for hi-tech component architectures with the help of classes and traits.

2

Scala is a functional language

No wonder, Scala has implemented top-notch functional programming concepts – in case you don’t know, in functional programming, each and every computation is regarded as a mathematical function. Following are the characteristics of functional programming:

  • Simplicity
  • Power and flexibility
  • Suitable for parallel processing

Not interpreted, Scala is a compiler-based language

As Scala is a compiler based language, its execution is relatively faster than its tailing competitor, Python. The latter is an interpreted language. The compiler in Scala functions just like a Java compiler. It taps the source code and launches Java byte-code that’s executable across any standard JVM (Java Virtual Machine).

Pre-requisites for Mastering Scala

Scala is a fairly simple programming language and there are minimal prerequisites for learning it. If you possess some basic knowledge of C/C++, you can easily start acing Scala. As it is developed upon Java, the fundamental programming functions of Scala are somewhat similar to Java.

Now, if you happen to know about Java syntax or OOPs concept, it would prove better for you to work in Scala.

Basic Scala Terms to Get Acquainted With

Object

An entity which consists of state and behavior is defined as an Object. Best examples – person, table, car etc.

Class

Described as a template or a blueprint for designing different objects that reflects its behavior and properties, a Class is a widely popular term.

Method

It is reckoned as a behavior of a class, where a class may include one or more methods. For example, a deposit can be reckoned as a method of bank class.

Closure

It is defined as any function that ends within the environment in which it’s defined. A closure return value is determined based on the value of one or more variables declared outside the closure.

Traits

These are used to determine object types by mentioning the signature of the supported methods. It is similar to a Java interface.

Things to Remember About Scala

  • Scala is case sensitive
  • When saving a Scala program, use “.scala”
  • Scala execution process begins from main() methods
  • Never can an identifier name start with numbers. For an instance, the variable name “789salary” is not valid.

Now, if you are interested in understanding the intricacies and subtle nuances of Apache Spark in detail, you have to enroll for Scala certification Training Gurgaon. Such intensive Scala training programs not only help you master the programming language but ensure secure placement assistance. For more information, reach us at DexLab Analytics, a premier Scala Training Institute in Gurgaon.

 
The blog has been sourced from ― www.analyticsvidhya.com/blog/2017/01/scala
 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

India and Big Data Analytics: The Statistics and Facts

India and Big Data Analytics: The Statistics and Facts

Data science, big data and analytics industry in India is expected to experience 8X growth hitting $16 billion by 2025 from the current $2 billion, experts say. Out of the terrific annual inflow to the analytics industry, nearly 11% can be ascribed to advanced analytics, data science and predictive analytics and a substantial 11% to big data.

In the next seven years, the Indian analytics industry will expand its horizons further and demand more analytics professionals to join the data bandwagon. Separately, the BI and analytics software market revenue in India will touch Rs 1980 crore in 2018, increasing at a rate of 18% per year. As a result, Indian companies and organizations are shifting their focus from traditional data reporting to augmented analytics tools that will not only enhance the process of data preparation and evaluation but will help predict the future outcomes, successfully.

2

Trends in Analytics

Several sectors across the Indian industry of companies and startups have started embracing data analytics – no wonder, the data analytics landscape in India is growing rapidly, so is the revenue generation.

Contemporary, architecture-oriented data analytics tools are the order of the day. Rightfully so, the companies and budding startups are replacing tactical and traditional data analytics programs for more strategic approaches. The current breed of fast followers is even seeking hefty investments in advanced analytical solutions powered by AI, ML and Deep Learning. It would lessen the time taken to market and sharpen analytics offerings. Focused data management is bringing forth a rapid shift to the hybrid and cloud data management scenario – through iPaaS (Integration Platform as a Service) tools. Data lakes and hubs are also emerging here and there. They are in demand for ingesting and administering multi-structured data. Nevertheless, a lack of talent pool will cost the industry immensely. It can be a major deterring factor towards their seamless adoption.

It’s about time to be data-smart with an excellent data analyst certification from the experts. Headquartered in Delhi, DexLab Analytics is one of the prime data analyst training institutes that will help you stay ahead of the curve, especially data curve!

Statistics of Data Analysis

Geographically speaking, more than 64% of revenue generated from data analytics in India comes from the USA. We are a leading exporter of data analytics to the US, taking figures to as high as $1.7 billion. In the FY18 alone, the revenue generation from the US has increased by 45%. Next, ranks the UK with 9.6% revenue generation. Technically, analytics revenue generation in India has almost doubled from last year – in terms of countries Poland, UAE, New Zealand, Belgium, Romania & Spain. Furthermore, Indian analytics firms are not left far behind in the data game – they contribute 4.7% of analytics revenues to Indian analytics market.

Well, it seems India is doing pretty good in terms of adopting cutting data analytics technology and reaping fetching benefits. If interested in data analytics, don’t stay behind. Reach us at DexLab Analytics and throw your queries right away.

 

The blog has been sourced from ― www.dqindia.com/india-analyzes-big-data-science-analytics-market-india

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Most Popular Big Data Hadoop Interview Questions 2018 (with answers)

Most Popular Big Data Hadoop Interview Questions 2018 (with answers)

Hadoop is at the bull’s eye of a mushrooming ecosystem of big data technologies – its open source, and widely used for advanced analytics pursuits, such as predictive analytics, machine learning and data mining, amongst others. Hadoop is defined as a powerful open source distributed processing framework that’s ideal for processing of data and storing data for big data applications, running across clustered systems.

Below, we’ve put together a comprehensive list of interview questions with answers on Big Data Hadoop, focusing on the various aspects of the in-demand skill. For more, take up our intensive big data hadoop training in Gurgaon

What is the role of big data in enhancing business revenue?

Big data analysis aids businesses in increasing their revenues and hitting notes of success. To explain further, let’s take an example, Walmart, one of the top notch retailers in the world uses big data analytics to increase the sales figure through improved predictive analytics tool, better customized recommendations and new set of products curated observing customer preferences and latest trends. Interestingly, it observed up to 15% increase in online sales for $1 billion in incremental revenue. Like Walmart, LinkedIn, JPMorgan Chase, Facebook, Twitter, Bank of America, Pandora, etc. follow suit.

Mention some companies that use Big Data Hadoop.

  • Yahoo
  • Netflix
  • Adobe
  • Spotify
  • Twitter
  • Amazon
  • Facebook
  • Hulu
  • eBay
  • Rubikloud

Highlight the main components of a Hadoop application.

Hadoop has a wide set of technologies that offers unique advantages for solving crucial challenges. Hadoop core components are given below:

  • Hadoop Common
  • HDFS
  • Hadoop MapReduce
  • YARN
  • Pig
  • Hive
  • HBase
  • Apache Flume, Chukwa, Sqoop
  • Thrift, Avaro
  • Ambari, Zookeeper

What do you mean by Hadoop streaming?

Hadoop streaming is an additional utility function that accompanies Hadoop distribution. Hadoop distribution includes a standard application programming interface, which is used to write Map and Reduce jobs in a number of languages, such as Python, Ruby, Perl, etc. Hadoop streaming is this entire process – here, users can develop and run jobs with any type of shell scripts or executable as the Mappers or Reducers.

Specify the port numbers for NameNode, Task Tracker and Job Tracker.

  • NameNode 50070
  • Job Tracker 50030
  • Task Tracker 50060

What are the four V’S in Big Data?

  • Volume – Scale of data
  • Velocity – Analysis of streaming data
  • Variety – Different forms of data
  • Veracity – Uncertainty of data

2

Distinguish between structured and unstructured data.

Structured data is referred as the data that can be stored in conventional database systems in the form of rows and columns – data, which is stored partially in traditional database systems, is known as semi-structured data – raw or unorganized data is generally termed as unstructured data.

Example of structured data – online purchase transactions

Example of semi-structured data – data in XML records

Example of unstructured data – Facebook & Twitter updates, web logs, reviews

Hope you found these Hadoop interview questions useful; to gain further insights on Big Data Hadoop, please enroll for our big data hadoop training courses – they are adequate and developed considering latest industry demands. 

 

The blog has been sourced fromwww.dezyre.com/article/top-100-hadoop-interview-questions-and-answers-2018/159

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

How Google Is Fighting Floods in India with AI

How Google Is Fighting Floods in India with AI

Of late, the search engine giant, Google made a slew of announcements tailor-made for India focusing on new-age technologies, including Artificial Intelligence and Machine Learning. They are to improve response time to natural disasters, such as flood along with address healthcare challenges.

At their recently conducted annual flagship conference Google For India 2018, the search engine giant also revealed the company is eager to use crisis response and SOS alerts to predict natural disasters. Speaking to the context, Rajan Anandan, VP, India and SEA Sales and Operations at Google stated that technology does come to rescue during extraordinary conditions.

2

He further added, “India has gone online to rally behind the victims of the Kerala and Karnataka floods. Our Crisis Response team turned on SOS alerts on Google Search in English and Malayalam, and activated Person Finder to help people search for family and friends. Locations of flood relief resources like shelters are being shared on Google Maps. Outside of the tech support, Google.org and Googlers are contributing over $1 million to support relief and recovery efforts. And others are also donating towards the Kerala flood relief on Tez.”

Floods are ravaging; especially in countries India, Bangladesh and China. It’s for them that Google considered it’s high time to devise something to prevent disasters happening in these countries. Thus, the team started seeking ways to implement AI for flood prediction. The recent Kerala flood was an eye-opener. Hundreds have lost their lives and thousands are still living in makeshift relief camps. The numbers say more than 7.8 lakh people are said to be living in these camps across Kerala.

To offer help, Goggle has initiated a steady stream of measures to assist the state. It has activated SOS alerts on Google search, which hooked all the response numbers and emergency resources in languages, English and Malayalam.

Talking about the technology launch, Google Technical Project Manager (TensorFlow) Anitha Vijayakumar was found saying, “We have been doing AI research to forecast and reduce the impact of floods… Floods are the most common disaster on the planet, and with adequate warning, we can greatly reduce the impact of floods. The current modelling systems are only physics-based, and the data is not detailed enough, while Google is using a system that combines physics modelling plus AI learning, and combines that with elevation and satellite map data.”

In addition, “We also activated Person Finder in English and Malayalam, to help people search and track family and friends – on last count, there were 22,000 records in person finder. We also extended this information on Google Maps to aid the rescue efforts,” said Rajan Anandan, Vice President of Google (South East Asia and India).

He further added that Google Tez’s (the notable payment) donation drive has so far raised USD 1.1 million towards Kerala Chief Minister Relief Fund. Also, Googlers and Google.org has donated USD 1 Million for recovery schemes and relief measures.

Now, that you are reading this blog, it means you are interested in the broad scopes of artificial intelligence and power it brings with it. Get enroll in Big Data Hadoop training in Gurgaon; DexLab Analytics offer state of the art Big Data Hadoop certification courses that’ll take you a step closer to fulfilling your dreams.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Call us to know more