Why Cloud Computing is the future of enterprise application platform?

Cloud computing to store and manage the Big Data

 

Technology is the marvel of human innovation. It keeps evolving at a rapid pace with the sole aim of simplifying human life. The recent years have been the most remarkable in the history of technology. New innovations have been replacing the old tech and the industry has been in an ever-adapting mode.

 

why-cloud-computing

 

Constant up gradation is the only way one can survive and thrive in this competitive Digital Age, especially when one is in charge of running an enterprise. Any organization, big or small deals with a bulk load of data regularly. The popular term used to denote massive volumes of data in an enterprise is Big Data. Now, there was a time when local servers were used to store the data and run it, but that changed with the introduction of Cloud computing.

In simple terms, Cloud computing allows organizations to manage their data in a more cost-effective and efficient manner. This has led many to move on to the Cloud, and it is quite the trend in the tech world.
With more organizations adapting to and adopting cloud services and tools, experts are of the view that very soon Cloud computing will replace the traditional enterprise application platforms. But before we delve any further let’s brush up on the basics of cloud technology and the ones related to it.

Cloud Computing and Big Data

Cloud computing can be defined as a technology that stores, manages and processes bulk load of data on remote servers. There is no use of physical drives or local servers.
Big Data, on the other hand, is the massive volume of structured and unstructured data processed and managed by an organization for further analysis. The smooth running of any organization depends on the successful storing and processing of the data, as it is directly related to the core functions.

Why Do You Need To SwitchOver To Cloud Computing?

Using Cloud computing to store and manage the Big Data comes with its own set of advantages. With the collating, quantifying and processing of the data well taken care of, managing the business becomes easier. Here are a few advantages that you get to enjoy through Cloud computing:

  • Cost-Effective: The key benefit of Cloud computing is that it helps in cost-cutting. There is no need to spend money on building and maintenance of infrastructure for managing Big Data. The cloud space needs to be bought from service providers or vendors. So, all data related maintenance, back-ups, disaster management is taken care of. It gives you ample time to focus on the core business and saves your money that otherwise would have been spent on skills and resources.
  • Flexibility: Cloud computing gives you sufficient room to adjust and adapt to fulfill your purpose in case it changes with time. It helps you to utilize your resources in the right manner. You can optimize your resources as per your need.
  • Accessibility: Unlike the old methods, Cloud technology allows you to access the Big Data from any place at any time from any device. It definitely improves operations and data analytics.
  • Integration: Integration is another contributing side of cloud computing. Assimilation of new data sources and managing huge volumes of data becomes very easy. There is no shortage of storage which comes as a boon when you want to keep up with the increase in Big Data.

Anyone Can Achieve Big Data Analytics through Cloud Computing

Setting up and maintaining an on-premise Big Data infrastructure demands skilled resources and a significant amount of money. This becomes an issue for small or mid-level businesses as they don’t have the financial strength to afford that. But with cloud computing, anyone can enjoy the benefits of Big Data infrastructure without having to build or maintain themselves.
Availing cloud technology allows them to pay for the resources that they need at that time. As soon as the purpose is fulfilled you can drop the extra load just like that. Everything happens quicker when you are working in the cloud. Even the expansion of data platforms takes significantly less time.

The Shortcomings of Cloud Computing

The ones who have already spent a big chunk of the finances in building their own Big Data infrastructure might face some difficulty in transferring the data to the cloud. For many, it becomes too difficult to carry the burden of the extra cost.
In other cases, the people taking care of the already existing infrastructure, express displeasure while handing over the duties to the third party service provider. In that case, the heads of the organization need to convey the long term benefits of cloud computing to the employees.
Other concerns related to administration and data security are also deterrent factors that prevent organizations from shifting to cloud technology. However, that should be the least of the concerns for anyone as the majority of the cloud vendors provide cloud platforms that endure total security to data and other company information.

Cloud Computing and Data Analytics

With time there is a high chance that a company will experience a hike in the volume of data. If the infrastructure is not able to match up to the data demand then the analytics takes a direct hit. It causes the performance to falter due to the slowing down of analytics tools. That’s why it is very important that you transfer the analytics to the cloud along with the Big Data.
Building the Big Data analytics platform in the cloud allows the organization to leverage the stored cloud data for analytics. This process enables faster accessibility of the Big Data. That way the user can make use of it easily during the time of need.

Conclusion

After considering both the advantages and shortcomings of cloud computing, it cannot be denied that positive does outweigh the negatives. The trend of shifting data to the cloud will gradually make all the older methods go obsolete. The benefits of cloud computing are hard to ignore and the evidence is clear in its rising popularity. One can very well say that Cloud computing is the future of enterprise application platforms.

How Data Science will Transform Web Development

Influence of Data Science on Web Development

The whole ideology behind technology is that it is subject to constant change. It has to be dynamic so as to pave way for something more optimized, beneficial, and less demanding. It is an inevitable path of progress, which has undoubtedly led us to where we are right now. The internet has been one of the turning points of this century. Its implications via technologies like data science are rippling through every sector conceivable including web development.

How Data Science will transform web development

Enter the concept of data science in web development

Our reliance on web apps has increased over the years. This is because of the ease and convenience they bring to us in the long run. Web apps are software-applications that run with the help of the internet. They do things for you so that you can cut down on doing them manually.

So far, web apps have had a certain way of functioning. Apps were based on focus groups, surveys, and specific educated guesses. This would help programmers have an idea what the masses want. But this traditional method of web app development is soon going to belong to the archived files of modern day technological history.

Developers now have zettabytes of data at their disposal, owing to IoT (Internet of Things). People are constantly plugged in to the net and this generates data every second of every minute. This generated data is carefully analysed which then transforms into precious insights that can be acted upon. Here are some ways applying data science would transform web development for the better

1. Software production

Traditionally, which is also how things happen currently, a software development programmer would decide what goes on in the app menu, based on his or her understanding of what the users would like and appreciate. Data science, on the other hand, would generate data from similar apps and would then suggest what seems to be opted by the users more. Imagine it to be the Autocomplete feature going industrial, in terms of web development.

2. Upgrades would be automatic

Upgrades would no longer be something commanded by a team of programmers. Machine learning would compile the data generated by users using a certain app and introduce version upgrades as and when it seems appropriate. It is like the app learns from you and betters itself to help you better.

3. Personalization to the max

Apps will now have a more accurate AI (Artificial Intelligence) system integrated into its functionality. This would mean that your apps understand you better. Already, your smartphone gives you suggestions based on your location, previous searches, past likes, etc.

Your apps will now interact with you in a more customized manner as it will learn, based on the information collected from the other apps. How users use an app and would contribute to how you use it and the data generated from your usage would improve your user experience even further.

4. Foreseeing the impact

It is hard to estimate how far-reaching the impact of Data science would be in our day to day lives, but one thing remains of certainty that nothing would be the same as it is now. Factors like speed and functionality and security would perhaps remain the same, but what would be interesting to see is how an app ends up adapting to your interests.

These are some of the interesting ways in which data science is expected to transform the web development sector.

Which language is better for Data Science? R or Python?

R or Python? Which is the better for Data Science

Data Science has gained a lot of popularity in recent years and the amount of people opting for a career in data science has increased significantly.  Data science is basically an amalgam of data interpretation, algorithm design, and use of technology to solve problems.

Data scientists are provided with data which they have to mine in order to gain invaluable insights, to solve complex problems, and to provide direction to stakeholders. A data scientist should be able to dive into the given data to effectively draw helpful analysis for business decision making. This is the chief KRA of this job, thus making it slightly complex when compared to others.

R or Python for Data Science

 

If you are looking to enter the field of data science, it is important that you equip yourself with the right technical skills so that you can add value in real life corporate scenarios and carve out a lucrative career path for yourself.

Once such question that requires considerable thought is ‘which is the best language for data science, R or Python?’

In this post, we have tried our best to answer the question for you…

R or Python?

R is the best-suited choice for data scientists over the past years because the functionality of R was designed keeping data science in mind. Also, it is compatible with languages like C++ and Java. R is used by Google and hence, it is considered a reliable data science language.

When it comes to Python, it is relatively new in the field of data science and has yet to make a lasting mark. However, Python provides many benefits which have led to data scientists to choose Python as their language of choice in data science.

The financial sector switched its data science language to Python after Bank of America started using Python, mainly because it is more versatile and easier to implement. However, some people still prefer R because of its long heritage.

How to Decide

There are a lot of factors which can help you decide which programming language is best suited for you. Data science requires immense programming language proficiency and hence it is important that you choose wisely.

1. Versatility

R is considered the best by many because of all the customized packages it offers especially for data science. Python, however, is easy to learn and has a more refined syntax. In case a person is just starting out as a data scientist, it is better to opt for python.

2. Data visualization support

Data visualization is of utmost importance in data science and it is at this stage that R proves to be a better alternative than Python. Options like ggplot2 and googleVis visualization tools simply expand the utility value of visualization. Python is not as versatile as R in this matter, but it still provides visualization tools like Plot.ly or Matplotlib.

3. Deep Learning support

Data scientists are required to know about deep learning in order to mine data and Python is the more superior language here. Python provides data scientists with various packages like Theano and TensorFlow, which makes it one of the best languages for deep learning. R can also use some of these packages but Python does have an upper hand here

To wrap up, we have provided all the important factors which can help you decide which programming language will suit you better as a data scientist. Do let us know which will be your final choice. We would love to hear from you.

5 Skills Every Data Scientist Should Learn

How to become a Data Scientist?

Data Science is providing tremendous business value such as precision in forecasting and gaining insights on what can be beneficial for the customer. Take for instance streaming sites like YouTube etc. Data science is being used to mine data about what kind of videos attract users’ interests. This helps the site to recommend similar videos based on their specific choices and interests.

5 skills every Data Scientist should have

In today’s world, where the competition is immense, data scientists are more in demand than ever. If you are interested in entering this field and want a list of skills that you need to master then you are at the right place. We have listed 5 essential skills you need to master in order to be a good Data Scientist.

Data Scientist skills

1. High-level understanding of Python, Hadoop, and SQL

As a data scientist, it is essential for you to be good at programming languages like Python, Hadoop, and SQL. The data provided to a data scientist is usually in form of large data sets and being able to understand and decode it requires good programming knowledge.

Only after you have understood the dataset can you mine data and identify peculiar characteristics and patterns. The work of a data scientist is more applied than theoretical.

5 skills every Data Scientist should learn

 

2. Strong Decision-Making Prowess

A data scientist should have good intuition and decision making skills to identify the product and have a concrete knowledge of the complex system and data. When a data scientist has a good product intuition, he can generate general hypothesis on how he can improve the product, and get good insights.

It is also necessary for a data scientist to know how to define metrics of the product so that he knows what he can do to make it better.

3. Ability to work well in teams

Data science is not an easy job, and it is definitely not something one can do alone. A data scientist needs to have good teamwork spirit if at all he wants to do his job as per expected outcomes. Be it ETL specialists or data analysts, or even stakeholders form the business side, a data scientist will have to collaborate with many teams from time to time. Having a good team spirit makes workflow smoother.

4. Good Communication Skills

Even if a person is good in data science, and has obtained excellent insights, it is essential for him to know how to communicate his findings in a perfect manner. All the insights obtained from such deep-end and thorough research will be of no use if it is not communicated effectively. Different business units must be able to derive the much-needed data to make better business decisions.

5. Excellent Quantitative Analysis

A data scientist must mine data and identify peculiar characteristics and behavior from the given data. Hence, quantitative analysis becomes an essential skill for a data scientist as the datasets provided are very large. In order to efficiently identify products and their behavioral characteristics, a data scientist needs to have quantitative analysis as his expert skill.

Thus, if you want to become the most sought-after data scientist in the industry, then these are the five key skills you must possess to be good at your job.

7 Tips To Start Your Career In Data Science

How to start your career in Data Science

Data science is known to power up business value across industries like financial, healthcare, and technology. Professionals in this field empower the management to make better data-backed decisions and help unlock opportunities previously hidden. If you want to start as a data science professional then this field is extremely lucrative for you. However this move can reap rewards only when you approach your career in a strategic manner.

Interested to know of some handy tips when you are starting a career in data science? Then this post will help you

career in data science

1. Examine the multiple roles in data science

The first thing you need to do is examine the roles that are available in the data science sector. Some of the key roles include machine learning specialist, data engineer, data visualization specialist and others.

Choosing the correct specialty is extremely important. You need to match your role with the work experience and the background that you have in your career. Hence, it would be wise to gather information regarding each and every role of the industry and match your qualifications and expertise to select the best one for yourself.

2. Obtain skills for the role

The next thing you should do is to start looking for the educational courses that can help you prepare for the role you have selected. For that, you need to take a course that suits your interest. The theories can help you learn the skills and prepare for the challenges of the career. But make sure you are selecting a reliable course to obtain the skills.

7 Tips To Start Your Career In Data Science

3. Get in touch with industry specialists

Surrounding yourself with experienced professionals is a great move to keep yourself motivated and grow continuously. Though it can seem a little difficult at the beginning, you will eventually start interacting with more and more specialized people.

You can start by interacting with the specialists on social media and indulge in a valuable technical discussion in your niche.

4. Merge theory with a practical approach

Data science is not only about technical theories. The practical approach is extremely important if you are looking for a long term run in the industry. You need to grow your practical knowledge of the work that is required in the industry. For that, you need to spend more time in practical applications through different working opportunities.

5. Work on your communication capabilities

Just because you are in a technical field, doesn’t mean that you should stop yourself from obtaining better communication capacity. The data science industry offers an immense level of positive career growth to the professionals with strong communication skills.

6. Gather resources for current trends

To stay ahead in the game, you need to present yourself as a modern and well updated professional. Only then, you can expect companies to put their faith in you. So, keep on gathering resources regarding the current trends. Also, join a peer group online where you can share knowledge about data science tools and resources – Reddit is a great place to start with. Also, attend webinars and online sessions that help you stay updated on the current trends.

7. Choose the right tool or language

Choosing the right tool or language could be a difficult decision, but we suggest that you use the widely used tool / language to begin with. In this way, it will also be easy to find resources and tutorials online. It is imperative that you understand the concept rather than focusing just the right tool. Start with the coding language that you are familiar with and slowly begin to build on it. If you are new to coding then you should prefer GUI based tools to get started off with.

Challenges will come your way. But amidst all this, it is your motivated approach that will help you in conquering all those challenges. So, keep on trying and making your way towards desired success. If you are an upcoming data science professional then these handy tips will prove to be vital in giving you a good foundation to a successful data science career.

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