What’s trending in the IoT space

What’s trending in the IoT space

Internet of Things (IoT) is still a fairly new concept. According to Accenture, as recent as 2014, 87 percent of consumers were not aware about this technology. In 2016, 19 percent of government professionals and businesses admitted to never having heard of Internet of Things, while 18 percent were only vaguely familiar with the concept, according to research from the Internet of Things Institute.

The Internet of Things is preparing to transform our lives and the way we live, from they way we work to entertainment to transportation – it is gradually taking over all aspects of our lives. As this new wave of technology crawls toward mass adoption, a handful of trends have started to emerge with the space. 

Security:

Internet of Things is all about connectivity. As more and more devices get connected to each other, their network and data will get connected too. In such a situation, ensuring top-notch security of those devices will be of paramount importance for organisations within the industry.

Recruiting Remains a Challenge:

There is a vast difference between internet of things and regular internet. An average person uses internet on a daily basis and a normal college tech graduate can work for tech companies ranging from the best to the mediocre. However, companies with IoT Initiatives launching digital transformation projects including smart cities are facing a challenge.  

Finding well qualified people to secure and work for the internet of things still remains a challenge. 45 percent of IoT companies struggle to find security professionals, according to a TEKsystems survey. 30 percent report having difficulty finding digital marketers.

Consolidation is on fire: 

Under Armour is buying tons of companies. Intel Wind River purchased Arynga. AppDirect bought AppCarousel. Nokia has acquired wearables company Withings. The trend has started and acquisition in the IoT should continue through the remainder of 2017.

Data will wear the crown:

Among other things, IoT is all about data. The technology is generating tons of data, so now organisations are figuring out ways on how to monetize it. From connected cars, to connected home, to smart buildings and smart cities, every minute aspect of our lives will be affected increasing ability of businesses, governments and consumers to connect to and control everything around them.

There is a lot more than just sale:

Each category of the IoT is pushing to get devises into the market. However, the twist is that manufacturers are channeling their focus on the services that prop up those devices to enhance their businesses. To achieve this, organisations are developing service delivery platforms and ecosystems in order to get more mileage out of the devices beyond the sale.

IoT companies need places to show off their products:

App stores, marketplaces, and catalogs are the current favourites and should fill up with more devices as companies ramp up production.

Improvement in Health Sector:

With the onset of IoT, drug delivery and management will improve a lot. From supply change management of drugs to consumer safety to ‘smart pills’, there are multiple areas where IoT can obliterate fraudulent practices and improve patient lives.

While very early, the potential of sensor-driven computing is enormous. Think Google Now for
manufacturers, industrial robots and smart cities. It could be an automated world beyond our
current imagination.
John Greenough and Jonathan Camhi of BI Intelligence have put together an essential report on
the IoT after conducting months of research on these trends. The report elaborates on the current
scenario and the exciting future prospects of Internet of Things. Here are some insights that the
report gives on the future of Internet of Things:
IoT devices connected to the Internet will more than triple by 2020, from 10 billion to 34 billion.
Nearly $6 trillion will be spent on IoT solutions over the next five years.
Businesses will be the top adopter of IoT solutions because they will use IoT to –
Lower operating costs;
Increase productivity; and
Expand to new markets or develop new product offerings.
It is an exciting time to be an investor and entrepreneur in the Internet of Things and hardware
sector. From improving our homes to improving healthcare to making our jobs more efficient,
these are areas that will truly improve the quality of our lives.

Connection between DevOps and Internet of Things

DevOps

Simply put, DevOps is a firm handshake between Operations people and Developer people. It is a shift in mindset, tighter integration and better collaboration. It unites and helps development and operations teams be more efficient, innovate faster, and deliver high-value work. DevOps is a set of practices that automates the processes of software development and IT teams, in order that they can build, test, and release software faster and more reliably. The concept was founded with the intention to build a culture of collaboration between teams that historically functioned independently. It influences the way that you build systems, organise teams and even the structure of the system that you build.

Internet of Things (IoT)

It is the inter-networking of physical devices – any device with an on and off switch can be connected to the internet. It is the ability to transfer data over a network without requiring human-to-computer or human-to-human interaction.

Connection between DevOps and Internet of Things (IoT)

DevOps is more than just a buzzword — this concept has the potential to accelerate system development, ensure the quality of the systems, and optimise its reliability in the field. As the softwares for IoT advance, the management of those systems will need to advanced along with it. Updating the software will simultaneously require updating the firmware.

The connection between DevOps and IoT is not very apparent as of now. If you ask an IoT system manager where DevOps fit in, they might not be able to explain. The connection between the two concepts, though, is pretty straight forward.

When an Internet of Things application or software is tested, a DevOps systems need to consider the security around the information coming to those sensors. Simply put, these are points-of-entry that can be easily compromised. For example, somebody can hack into your account and send out misleading data. DevOps job is to minimise this exposure through security testing of the IoT application.

Continuous integration 
This ensures that Internet of Things applications and DevOps are considered a holistic application or system. The components of both these concepts are interdependent on each other including those that aren’t in your direct control like sensors embedded in machines (e.g., a jet engine). Continuous integration is a bit of a leap from traditional approached to DevOps, where all components are under direct control.

Deployment 

This is one of the most important components of IoT and DevOps. This is because the applications need to reside on a platform and work and play with remote devices. This complicates the processes of deployment, in that there could be updates to the application that lives on a centralised cloud platform, and maybe even firmware updates that inhabit on a remote sensor or device.

Take an example: think about a mobile application that operates Wi-Fi enabled closets. When the app is updated on both iOs and Android devices, they made need to push an update to the closets as part of the deployment, which assumes the firmware update is required to support the changes to the app. It is essential that all this happens at the same time. Updating just one of the two could result in system failure and beat the purpose of having an app to control closets.

While we’re still playing around with DevOps and IoT, it’s clear that the connections can be made, and we can indeed benefit from the marriage of IoT and DevOps.  Those who build IoT applications now, such as IoT software providers, already understand this connection and continue their progression toward synergy. Enterprises are new to DevOps and have yet to get on board. It’s just a matter of time until one won’t exist without the other.

Scope and Career Opportunities in Internet of Things

What is the Internet of Things?

A multi-billion dollar industry, the Internet of Things is one of the most revolutionary technology trends in our current times and is poised to explode. Basically, it is the fusion of the physical world with the real world.

To gain the ability to interact with the external environment and acquire a unique online identity, everyday objects are embedded with technology — like sensors and Wi-Fi. This infinite network of “smart” devices promises a range of benefits for businesses, individuals and society at large, including reduced waste, increased safety, and improved quality of life.

Here’s what it is – the ultimate goal of IoT is to take all things that we use in our daily lives over a network that can be easily accessed across the world over the internet. This implies that every object or gadget we use will identify with this network and information will be accessible via our laptops, smart phones, tablets and smart watches.

The Internet of Things (IoT) is the latest buzz among consumers and businesses. And going by the logic of demand and supply, there is a high demand of people with the right skill set for this market.  

Scope of Internet of Things

What IoT does is, it connects devices embedded in various systems to the internet. This connectivity helps the user take more data from more places and ascertains many ways of increasing efficiency and enhancing safety. When our devices can represent themselves digitally, they can be controlled from any part of the world.

Through IoT analytics and IoT security, organisations can improve their performance tenfolds and deliver better results. With refined tracking of devices/objects using sensors and connectivity, businesses in numerous industries can garner the benefits of Internet of Things by making informed decisions, with the support of interactional and transactional data at their fingertips.

Energy & Utilities

Companies in the energy and utility sector are among the early adopters of this technology. Improved customer services, reduced risk and increased operational efficiency are some of the benefits these organisations have gained from IoT.

Healthcare

In the healthcare industry, IoT has enabled doctors to communicate with their patients in more sophisticated ways. For instance, innovations like Patient Monitoring Systems and Pill Cameras have revolutionised the way healthcare is delivered. Additionally, wearable smart devices that track and monitor an individual’s physical activities have been well accepted by the consumer.

Industrial & Manufacturing

Industrial and Manufacturing industries are yet another industries to benefit from Internet of Things. They are able to monitor the functioning of various machinery parts and report any event of break down. With this, they are able to shield their people and assets. In addition to that, IoT also helps in assessing the attributes of the end products ensuring the established quality standards are met.

Transportation

The owners of companies in the transportation industry are able to monitor and locate their vehicles better. The ability to assign orders automatically saves them time, fuel costs and also enhances the customer experience. In future. cars are likely to become data collection hubs by delivering real-time information about traffic and more.

Public Sector

To address the problems arise in the public sector, there are various applications such as smart traffic systems, energy-efficient streetlights, garbage bins, etc. For example, smart garbage bin will notify when the bin is ready to be emptied, avoiding overflow of trash. More such applications are expected to address many of the constraining problems prevalent in crowded megacities.

Career Opportunities in Internet of Things

There are a whole bunch of new generation jobs for a new generation of technology.

An Internet of Things Solutions Architect is someone that works in the IoT space and controls the general business strategy of a company that works in this area. They coordinate the development and research teams, make high-level project decisions, and champions IoT strategies within the company.

On the engineering level, there is an increased demand for hardware and software engineers, especially in the area of IoT solutions development. Since there is an increase in demand for the right people; depending on the organisation these engineers can also expect a huge bump in their packages for their special skills.

Data scientists and data security professionals are also in huge demand. Data scientists work with huge data. They take this data and analyse it to spot trends or find important insights it might contain. As more devices become connected to the Internet, the ability for security applications to adapt and facilitate these connections also increases. This is where data security professionals come into the picture. This implies more opportunities for organisations that emphasise on security, as well as for network security professionals to find rewarding employment.

Skilled professionals with an entrepreneurial spirit can look at starting a new company with the explicit purpose of developing for the IoT. There is a significant change in how the consumer thinks about connect devices with the onset of Internet of Things. This means existing companies are vigorously finding new and enticing ways to incorporate the IoT into their own business models.

The IoT-enabled transformation has grown incredibly in the last couple of years and will continue to do so. In the near future, we were supposed to have a home full of Internet-enabled devices that would cater our every need. The Internet of Things enables innovators to think outside the box and literally reinvent the wheel, as it becomes an integral part of our lives and makes our lives more sophisticated than ever before.

Which programming language should I pick up to make a career in artificial intelligence?

Which programming language should I pick up to make a career in artificial intelligence?

Has the world of artificial intelligence got your attention? With the recent onset of some beautiful examples of AI like Siri, you know that it is possible to create such software and systems. However, depending on what the goal is, you will need a different set of knowledge to achieve it. Certain systems of AI require a higher level of AI knowledge like decision trees, logistic regression, continuous numeric prediction, etc. Other projects can be accomplished with the knowledge of programming language. Now if you’re wondering which language would be appropriate, here’s a rundown of the best options.

Python

Python is one of the widely used programming languages in the field of Artificial Intelligence thanks to its clean grammar and syntax, fluency, and nice design. The modular programming and language interoperability can seamlessly be used with the data structures and other frequently used AI algorithms. Some of the features that make Python popular for artificial intelligence programming are:

  • A bunch of testing frameworks
  • Documentation generation
  • Correct data structures
  • The balance of high-level and low-level programming

Java

Java is another great programming language to master. It is a good choice because it offers an easy way to code algorithms and algorithms are an integral part of artificial intelligence. For example search algorithms, natural language processing algorithms and neural networks. Java provides high-level features needed to work on AI projects.

Java allows scalability which is an important feature for AI. Other than that debugging ease, a good user interaction, easy work with big projects, facilitated visualisation, incorporation of Swing and SWT are some of its other prominent features. The Java community is also a plus point as there will be someone to help you with your queries and problems.

Lisp

Lisp is the first computer language used for artificial intelligence. Lisp simplifies complex programs and makes it easy to write them. It is extendable and flexible. It also boasts of features such as fast prototyping and macro utility that are useful in creating artificial intelligence systems.

Some of the features that make Lisp a popular programming language are:

  • Condition system
  • Powerful object system
  • Dynamic typing

The Lisp language is mostly used in the Machine Learning and is used in major AI projects, such as Macsyma, DART, and CYC. It’s excellent prototyping capabilities and its support for symbolic expressions is what makes Lisp a good choice for AI.

Prolog

When it comes to usefulness and usability, Prolog stands along with Lisp. Prolog is one of the programming languages for basic mechanisms that can provide flexible frameworks for artificial intelligence programming. Prolog is extensively used when programming expert systems for AI. For example, it offers pattern matching, automatic backtracking, and tree-based data structuring mechanisms.

It is an interactive symbolic language that is widely used for non-numerical programming. Apart from Artificial intelligence, in general, some of these include:

  • Natural language processing
  • Expert systems
  • Theorem proving

C++

Known as the fastest programming language in the world, C++ is extremely useful for AI projects that are time-sensitive. Its ability to talk at the hardware level enables developers to improve their program execution time. One of the programs that use C++ extensively is search engines. Games in artificial intelligence are mostly coded in this language for faster execution and response time. Other than that, it used for statistical AI techniques like the ones found in neural networks. Algorithms can also be written extensively in the C++ for speed execution

The selection of a programming language for your AI project depends on the sub-field. Before making a selection it is important to ensure that the language will be utilised extensively and not partially. From all the above-mentioned languages, Python seems to be crawling its way to the top as it is viable to use for most of the AI sub-fields.

Machine learning and AI courses have high demand

Machine learning and AI courses have high demand

In the last ten years, Artificial Intelligence has come on in a big way. It is no longer restricted to the pages of science fictions. Instead, it is a part of our everyday lives – whether it is determining our Facebook feed or correcting our wrong spellings on Google search.

Some of the biggest breakthroughs have been in machine learning. It is now all about supplying the computers with a huge amount of data and letting them learn to interpret it on their own. This is similar to a young mind encountering the world for the first time – spotting patterns and making connections between various elements. The result of this is a software that can manage a huge amount of data that can yield great insight which was too difficult for humans to process themselves. Once the data has been converted to a language that machines can understand, the scope of what they can then do with that data is unlimited.

Artificial Intelligence and Machine Learning have great potential in the current business scenario. Banks, for one, are utilising this technology to get better insights into transaction data that help them manage risk and prevent fraud. The way these algorithms work is they gather a full understanding of the user behaviour by observing millions of transactions. This helps them understand what normal looks like and any discrepancy is immediately spotted. Some of this data has been in the hands of businesses for decades, but companies didn’t know how to effectively use them, which has changed now.

This availability and use of data have changed how business is done. The data put in the machine learning systems is governed so that it can be converted into useful information for business. Here, it is important to note that the quality of the output from machine learning systems depends on the quality of data they are fed with.

One of the best examples of this is Google Translate. Over the years the more user data that has been fed into it, the better it has become at detecting grammar and correct phrases. The translation now is a lot more enhanced and coherent than compared to the earlier times.

Businesses are now catching up and experimenting with this sort of data-driven AI. Interest in machine learning is on a rise because the capability has improved and these advances coincide with a more data-savvy world. Companies are capturing and generating all sorts of data because effective use of them gives insights into behaviour that plays a key role in growth.

With the growing frequency and the apparent ubiquity of online communication & transactions, tons of data is being generated each day. It is almost impossible for humans to gauge emerging patterns from this data and gather insights. This is where Machine learning comes into play. More and more organisations are looking for people who can construct algorithms that can learn and respond to large databases and make effective predictions. As data grows in volume and becomes more complex, the applications of machine learning and artificial intelligence are becoming widespread and all pervasive. Some of the key applications include:

  • Recommender & Predictor Systems for Retail & E-Commerce
  • Predictive Techniques for Autonomous Vehicles
  • Trading Patterns & Efficiencies in Global Financial Markets
  • Fraud Prediction & Detection for Banks
  • Transportation Management & Traffic Shaping for Smart Cities

This has made machine learning the number one in-demand skill globally amongst programmers. Depending on their experience and capabilities, Machine Learning specialists usually get a substantial hike.

This constant growth in the practical application of artificial intelligence fuels high demand for machine learning engineers, data scientists, machine learning researchers, and other related professions. In addition to this, organisations operating in different verticals like voice recognition, cyber security, image recognition etc are on the look out for the right people with the skills and knowledge.

To stay on the top, companies need specialists and they need them now. Organisations cannot wait for universities to produce fresh graduates after five years. Individuals who are good at maths, physics and statistics and being picked up and trained in machine languages like Lisp, Prolog, etc.

There are multiple courses available online and other independent courses. Individuals interested can always opt for these courses if this field is something they want to work in.

What is Artificial Intelligence? – Scope and Career Opportunities

What is Artificial Intelligence? – Scope and Career Opportunities

Quoting the father of Artificial Intelligence, John McCarthy, AI is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

Simply put, Artificial Intelligence is a method of making a computer or a software think intelligently – to “think” like a human would and mimic the way they would act.

AI is polished by understanding how human brain thinks, learns, decides, and works when faced with a problem. The conclusions of this study are used as a basis for developing intelligent software and systems. An ideal characteristic of an Artificial Intelligence system is its ability to rationalise and take actions that have the best chance of achieving a specific goal. The term can be applied to any software or system that has attributes that can help learn and solve problems, just like a human mind.

To simplify, some of the activities computers with artificial intelligence are designed for, include:

  • Learning
  • Planning
  • Problem solving
  • Speech/Face recognition

The objectives of artificial intelligence such as learning, reasoning, perception and machines are all wired using a cross-disciplinary approach that is based on mathematics, computer science, linguistics, psychology, and more.

To understand this better, computers that play chess, self-driving cars are examples of machines with artificial intelligence. Their functionality includes weighing the outcomes of actions they take that will impact the end result. With respect to self-driving cars, the system must consider all external data and compute it to act in a way that stops the collision.

Scope of Artificial Intelligence

The ultimate goal of artificial intelligence is to create computer programs that can solve problems and achieve goals like humans would. There is scope in developing machines in robotics, computer vision, language detection machine, game playing, expert systems, speech recognition machine and much more.

The following factors characterise a career in artificial intelligence:

  • Automation
  • Robotics
  • The use of sophisticated computer software

Individuals considering pursuing a career in this field require specific education based on the foundations of math, technology, logic and engineering perspectives. Apart from these, good communication skills (written and verbal) are imperative to convey how AI services and tools will help when employed within industry settings.

Subjects an AI candidate must study

  • Studying mathematics, especially mathematical logic is crucial when considering a career in artificial intelligence.
  • Learning about sciences, e.g. physics or biology is also helpful. Knowledge of psychology and the physiology of the nervous system is helpful with respect to biological approaches to AI.
  • Knowing programming languages like C, Lisp and Prolog will be beneficial too. Knowledge of basic machine language is a must.

Career Opportunities in AI

Game Programmer

Video games are widely loved. However, in the current scenario, games require highly intelligent enemies to keep the players challenged. This is where a software engineer or a game programmer comes into play. Companies are on the lookout for candidates who are well-educated, thorough with the basics of AI and can design games that keep the user engaged.

Robotic Scientist

Not only are robots cool, they are also gradually taking over the industrial world. This field needs engineers or programmers that can program these robots to solve problems like a human would.

Having a master’s in robotic engineers and a license from the state can help a great deal for a career in this field.

Software Engineer (Face Recognition Software)

If you’re looking for a career that helps out other humans then a career as a software engineer working on face recognition software would be apt. Many companies, including security companies, police departments, casinos, and even Google are utilising face recognition to understand the people benefiting from their service.

Search Engine Manager

One of the highest paying companies from face recognition, Google also uses AI in other interesting ways. The organisation hires people with artificial intelligence degrees to manage their massive search engine. Users search for a variety of things and Google Search should be able to predict what the users are searching for despite the spelling and grammatical errors for the searched phrase. This is where knowledge and study of artificial intelligence come into play.

Government Sector

Getting a job in the private sector can be difficult considering the intense competition and the companies seek out only the best of the best. However, the government sector too requires the help of highly skilled artificial intelligence employees to work on various projects. Apart from the salary, a job in the government sector comes with excellent benefits and retirement packages.

Artificial intelligence plays an increasingly large role in running the world around us and it continues to advance and improve the quality of life across multiple industry settings. Any individual looking for a career in this field and willing to put in the effort to gain a high level of education will be quickly accepted into this constantly growing and high paying industry.

Jobs & Responsibilities of Hadoop Professional

If you’re wondering whether a certification can make a difference in getting a job as a Hadoop developer, Hadoop Architect or a Hadoop admin – the answer is Yes. Hadoop jobs are highly lucrative and they come with a price of knowing the technology in depth. Many industries are looking to invest in professionals who are skilled.

Before getting into specifics, here are some general skills expected from Hadoop Professionals

  • First and foremost, the ability to work with huge volumes of data so as to derive Business Intelligence
  • The ability to analyse data, derive insights and suggest data-driven strategies
  • Good knowledge of object-oriented programming languages like Java, C++, Python
  • Database theories, structures, categories, properties, and best practices
  • Must have basic knowledge of the software like installing, configuring, maintaining and securing Hadoop
  • Strong analytical thinking and ability to quickly adapt will surely come in handy

Hadoop Developer

The Roles and Responsibilities include:

Hadoop Developers are mainly software programmers with an extra skill of working in the Big Data Hadoop Domain. One of their primary roles includes coding. They are masters at designing concepts that are used when creating extensive software applications. Additionally, they are also adept at computer procedural languages.

A Hadoop Developer’s work routine would include:

  • Developing, designing and documenting Hadoop Applications
  • Seamlessly managing and monitoring Hadoop Log Files
  • Design, build, install, and configure Hadoop
  • Make hard-to-grasp technical requirements into simple and clear designs
  • Developing MapReduce coding that runs easily on Hadoop cluster
  • Designing scalable web services for data tracking
  • Testing software prototypes and supervise its smooth transfer to operations
  • Working knowledge of HDFS, Cloudera, HortonWorks, MapR, Pig Latin Scripts, HBase, HiveQL, Flume and Scoop

Hadoop Architect

The Roles and Responsibilities include:

A Hadoop Architect ensures that the proposed Hadoop Solution gets transpired the way it is outlined to be. S/he takes care of the needs of the organisation and execute their responsibilities by seeing through the gap between Big data Scientists, Big data Developers, and Big data Testers.

A Hadoop Architect’s work routine would include:

  • Planning and designing big data system architectures
  • Creating requirement analysis
  • Responsible for selecting the right platform
  • Designing Hadoop applications and work on their development
  • Ensure successful flow of development life cycle
  • Working knowledge of Hadoop Architecture, Hadoop Distributed File System (HDFS), Cloudera, HortonWorks, MapR, Java MapReduce, HBase, Hive and Pig.

Hadoop Administrator

The Roles and Responsibilities include:

The primary role of a Hadoop Administrator is to ensure that the Hadoop frameworks function smoothly with minimum roadblocks. Overall roles and responsibilities are similar to that of a System Administrator. Thorough knowledge of the hardware ecosystem and Hadoop Architecture is a must.

A Hadoop Administrator’s work routine would include:

  • Manage and maintain the Hadoop clusters for an uninterrupted job. Also, ensuring that it is secured in a foolproof manner.
  • Do a routine check-up of the entire system and create regular back-ups
  • Ensuring the connectivity and network is always up and running
  • Planning for capacity upgrading or downsizing as and when the need arises
  • Managing the HDFS and ensuring it is working smoothly at all times
  • A good knowledge of HBase and proficiency in Linux scripting, Hive, Oozie and HCatalog is necessary

Hadoop Tester

The Roles and Responsibilities include:

Hadoop networks getting bigger and more complex with each passing day brings up issues with respect to viability, security and making sure that everything works fine without any bugs or issues.  This is where a Hadoop Tester comes in the picture. S/he is responsible for troubleshooting the Hadoop Applications and rectifying any issues that s/he discovers at the earliest.

A Hadoop Tester’s work routine would include:

  • Construct and deploy both positive and negative test cases
  • Find out and report any bugs or performance issues
  • Make sure that the MapReduce jobs are running perfectly
  • Ensure that all Hadoop scripts like HiveQL, Pig Latin are all solid
  • Thorough knowledge of Java to do the MapReduce testing efficiently
  • Understanding of MRUnit, JUnit Testing frameworks is essential
  • Proficiency in Apache Pig and Hive is required
  • Should be comfortable working with Selenium Testing Automation tool

Data Scientist

The Roles and Responsibilities include:

A data scientist is one of the most sought-after roles in the market today and enterprises are willing to hire qualified professionals for attractive packages. What makes a Data Scientist such an attractive prospect in the job market is because this person wears multiple hats over the course of a typical day at the office. In short s/he is part scientist, part artist and part magic.

A Data Scientist’s work routine would include:

  • Data Scientists are basically Data Analysts with more responsibilities
  • They are well versed with different techniques of handling data
  • Should be able to solve real business problems backed by solid data
  • Should be great with mathematics and statistics
  • Develop data mining architecture, data modelling standards and more
  • An advanced knowledge of SQL, Hive and Pig is necessary and ability to work with SPSS and SAS is a huge plus
  • Ability to reason and confirm required actions with strong data and insights

The above mentioned information gives an overall understanding of what to expect in terms of daily tasks when it comes to Hadoop professionals. A lot of these will alter based on the size of the organisation, its business agenda and its requirements. There has never been a better time to master Hadoop. Get started now!