Highly scalable general-purpose programming language, Scala is a wonder tool for newbie developers as well as seasoned professionals. It provides both object-oriented and functional programming assistance. The key to such wide level popularity of Scala lies in the sudden explosive growth of Apache Spark, which is actually written in Scala – thus making later a powerful programming language best suited for machine learning, data processing and streaming analytics.
Below, we have enumerated top three reasons why you should learn Scala and tame the tides of success:
For Better Coding
The best part is that you would be able to leverage varied functional programming techniques that will stabilize your applications and mitigate challenges that might arise due to unforeseen side effects. Just by shifting from mutable data structures to immutable ones, and from traditional methods to purely functional strategies that have zero effect on their environment, you can stay rest assured that your codes would be more stable, safer and easy to comprehend.
Inarguably, your code would be simple and expressive. If you are already working on languages, such as JavaScript, Python or Ruby, you already know about the power of a simple, short and expressive syntax. Hence, use Scala to shed unnecessary punctuations, explicit types and boilerplate code.
What’s more, your code would support multiple inheritance and myriad capabilities, and would be strongly-typed. Also, in case of any incompatibilities, it would be soon caught even before the code runs. So, developers in both dynamic and statically typed languages should embrace Scala programming language – it ensures safety with performance along with staying as expressive as possible.
To Become a Better Engineer
He who can write short but expressive codes as well as ensure a type-safe and robust-performance application is the man for us! This breed of engineers and developers are considered immensely valuable, they impress us to the core. We suggest take up advanced Scala classes in Delhi NCR and take full advantage of its high-grade functional abilities. Not just learn how to deliver expressive codes but also be productive for your organization and yourself than ever before.
Mastering a new programming language or upgrading skills is always appreciable. And, when it comes to learning a new language, we can’t stop recommending Scala – it will not only shape your viewpoint regarding concepts, like data mutability, higher-order functions and their potential side effects, but also will brush your coding and designing skills.
It Enhances Your Code Proficiency
It’s true, Scala specialization improves your coding abilities by helping you read better, debug better and run codes pretty faster. All this even makes you write codes in no time – thus making you proficient, and happy.
Now, that you are into all-things-coding, it’s imperative to make it interesting and fun. Scala fits the bill perfectly. If you are still wondering whether to imbibe the new-age skill, take a look at our itinerary on advanced Scala Training in Delhi displayed on the website and decide for yourself. The world of data science is evolving at a steadfast rate, and it’s high time you learn this powerful productive language to be on the edge.
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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.
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:
All data must be collected and preserved
Better predictive models come from more data
Storing more data doesn’t incur higher cost
More data doesn’t mean higher computational costs
Now the real picture:
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.
Same kind of examples being repeated a hundred times doesn’t enhance the precision of a predictive model.
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.
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:
Getting more of the same isn’t alwaysuseful: 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.
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.
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:
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.
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.
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.
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.
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In marketing, the analysis of data is a highly established one but the marketers nowadays have a massive amount of public and proprietary data about the preferences, usage, and behavior of a customer. The term ‘big data’ points out to this data explosion and the capability to use the data insights to make informed decisions. Understanding the potential of big data presents various technical challenges but it also needs executive talent devoted to applying the solutions of big data. Today, the marketers are widely embracing big data and are confident in their use of analytics tools and techniques. Let us learn about the ways in which Big data and analytics can improve the marketing efforts of various businesses around the around.
Locating Prospective Customers
Previously, marketers had to frequently make guesses as to which sector of population comes under their ideal market segment but this is no longer the scenario today. The companies can exactly see who is buying and even extract more details about them with the help of big data. The other details include which buttons they generally click while on a website, which websites they visit frequently, and which social media channels they utilize.
Tracking Impact and ROI
Many retailers have introduced loyalty card systems that track the purchases of a customer, but these systems can also track which promotions and incentives are most effective in encouraging a group of customers or a single customer to make another purchase.
Handling Marketing Budgets
Because big data allows companies to optimize and monitor their marketing campaigns for performance, this implies they can allocate their budget for marketing for the highest return-on-investment (ROI).
Personalizing Offers in Real-Time
Marketers can personalize their offers to customers in real time with the combination of big data and machine learning algorithms. Think about the Amazon’s “customers also bought” section or the recommended list of TV shows and movies from Netflix. The organizations can personalize what promotions and products a particular customer views, even down to sending personalized offers and coupons to the mobile phone of a customer when he walks into a physical location. The role of Personalized Merchandising in the ecommerce industry will continue to increase in the years to come.
Improvement in Market Research
Companies can conduct quantitative and qualitative market research much more inexpensively and quickly than ever before. The tools for online survey mean that customer feedback and focus groups are inexpensive and easy to implement, and data analytics make the results easier to take action.
Prediction of Buyer Behavior and Sales
For the past several years, sales teams, in order to rate their hottest leads, have made use of lead scoring. But, with the help of predictive analytics, a model can be generated and it can successfully predict sales and buyer behavior.
Enhanced Content Marketing
Previously, the return-on-investment for a blog post used to be highly difficult to measure. But, with the help of big data and analytics, the marketers can effortlessly analyze which pieces of content are highly effective at moving leads via a sales and marketing funnel. Even a small firm can afford to use tools for implementing content scoring which can highlight the content pieces that are highly responsible for closing sales.
Optimize Customer Engagement
Data can provide more information about your customers which includes who they are, what they want, where they are, how often they purchase on your site, and how, when they prefer to be contacted, and various other major factors. The organizations can also examine how users interact not only with their website, but also their physical store to enhance the experience of the user.
Tracking Competitors
New tools for social monitoring have made it easy to gather and examine data about the competitors and their efforts regarding marketing as well. The organizations that can utilize this data will have a distinct competitive advantage.
Managing Reputation
With the help of big data, organizations can monitor their brand mentions very easily across different social channels and websites to locate unfiltered testimonials, reviews, and opinions about their company and products. The savviest can also utilize social media to offer service to the customers and create a trustworthy brand presence.
Marketing Optimization
It is quite difficult to track direct ROI and impact with traditional advertising. But, big data can help organizations to make optimal marketing buys across various channels and to optimize their marketing efforts continuously through analysis, measurement, and testing.
What is Needed for Big Data?
At this point, talent and leadership are the major things that big data needs. In most of the companies, the marketing teams don’t have the right talent in place to leverage analytics and data. Apart from people who possess analytical skills to understand the capability of big data and where to use it, companies require data scientists who can extract meaningful insights from data and the technologists who can develop include new technologies. Due to this, there is a high demand for experienced analytics talent today.
Big Data Limitations for Marketing
In spite of all the promise, there exist certain limits to the usefulness of big data analytics in its present state. Among them, the major one is the major one is the analytics tools’ and techniques’ complex “black box” nature which makes it hard to trust and interpret the output of the approaches of big data and to assure others of the accuracy and value of the insights generated by the tools. The difficulty of gathering and understanding data also limits the capability of marketing companies to more fully leverage big data. Beyond this, the marketers are identifying many hurdles to expanding their utilization of big data tools and they include lack of sufficient technology investment, the inability of senior team members to leverage big data tools for decision-making, and the lack of credible tools for measuring effectiveness.
Conclusion
Cloud computing is also playing a major role in marketing with the Cloud Marketing process. Cloud Marketing is a process that outlines the efforts of a company to market their services and goods online via integrated digital experiences. Once the data analytics tools become available and accessible to even the smallest businesses, there will be a much higher impact of big data on the marketing sector as there will be much broader utilization of data analytics. This can only be a boon as organizations enhance their marketing and reach their customers in innovative and new ways.
Author’s Bio: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working at Mindmajix.com. His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at savaramravindra4@gmail.com. Connect with him also on LinkedIn and Twitter.
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As digitization grows in size and width, more and more companies are seeking ways to modify their digital lending services. It would be effective as well as profitable for both borrowers and lenders. And as a topping on the cake, companies resort to Artificial Intelligence and Big Data as they believe they are the future powerhouse of loans.
Originally banks being the lenders make the lending decision based on a loan applicant’s credit score – which is a 3-digit number collected from renowned credit bureaus, like Equifax and Experian. Credit scores are obtained from large piles of data, such as credit history length, payment history and credit line amounts, and are used to decide whether the applicants would be able to repay their debts. They are also good to determine the interest rate of loans.
Low credit score is an indication that you are a risky borrower, which may end up in rejection of your loan application or else you have to pay excessively higher interest rate.
However, according to digital lending platforms, this kind of information isn’t enough – they fail to draw the actual picture of the loan applicant’s credit worthiness. Rather, it is advisable to include hundred other data points in the scrutiny process, and they don’t have to be based on financial interactions alone. Include educational certifications, employment documents, and even you can take help from minor information, like your nap time, website browsing preferences, chatting habits and so on.
The mechanism of Peer-To-Peer Lending
At times, the concept of Big Data is downright challenging – it creates more confusion than clearing things out. Even Artificial Intelligence is included in this, though marketing teams of countless companies are relying on this advanced technology to enhance profitability and efficiency in operations – pundits from the online lending industry believes AI can actually change the way fintech companies perform.
For example, Upstart, a California-based Peer-to-Peer online lending company uses the power of AI to process loans. It implements machine learning algorithms to perform underwriting decisions. Machine Learning possesses the ability to analyze and coordinate large chunks of customer data to draw patterns that would remain unnoticed if done manually through human analysts.
According to Upstart, this process eventually works out well for people with limited credit history, lower income level and young borrowers. The company has also initiated an automation of 25% of its less risky loans to keep future prospects in mind.
Another Chicago-based startup Avant is harnessing machine learning to identify fraud – by comparing customer behavior with the initial available data belonging to normal customers, while singling out outliers. They are now planning to extend their services to brick-and-mortar banking structures that are planning to set their foot in the online lending business.
Today, digital lending is witnessing a steady growth worldwide, and India is not lagging behind. The perks of introducing machine learning and analytics are evident everywhere, so get yourself charged up and ride on the road of analytics. DexLab Analytics offers excellent big data hadoop certification in delhi ncr. Get enrolled today to experience the best!!
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While waiting to board your plane, the last thing you would want to hear is – we regret to inform, your flight has been delayed or worse cancelled, leaving you exasperated at the very hour. Even though your flight takes off on time, while waiting in front of the baggage carousel, you may face some moments of anxiety before your bag arrives on time. Luckily, these distresses are now becoming a thing from the past.
Things are changing, and technology world is evolving drastically. By leveraging Big Data and technology upgrades, aircraft industry has been able to improve their operations and work more smoothly and accurately. In addition, the air travel industry is witnessing several benefits, in terms of revenue and consumer satisfaction.
Here are the ways in which airlines have been using data to derive maximum operational gains everyday:
Smart Maintenance
Wear and tear is common, even the most advanced airplane models equipped with superior technology require time to time maintenance. Owing to this, travelers may experience delays – as per 2014 survey data, mechanical glitches were the second most reason for the majority of flight cancellations and delays. Maintenance takes its toll on airlines potential as the planes need to be grounded for repairing.
With Big Data, airlines can easily track their planes, predict crucial repairs to be done, and provide advice about which parts need to be bought ahead of time and which to keep in reserve on hand for last minute technical issues.
Reducing Jet Fuel Use
It is impossible to predict how much fuel is used onboard for any given route, historically. But Big Data analytics and cloud data storage has made the impossible possible – you can now track how much fuel is being consumed by each airplane, while taking all the factors into consideration. This paves the way for airlines to draw predictions about the amount of fuel required for a trip to how many number of passengers can board at once.
Taking the Boarding Decisions
Remember, airlines lose if they fly with empty seats, so it’s in their best interest to get everyone onboard. With the help of real-time data, airlines can now easily decide whether to wait for a passenger or leave on time so as not to harass other passengers who might catch connecting flights. Smart boarding is now the key, gone are the days when decisions used to be based on instincts. It’s time to enhance efficiency and performance.
Tracking Bags
Travelers who travelled before had to be hopeful about their luggage making it back to them. But now, Big Data revolution and tracking technology has changed a lot of things. Nowadays, airlines ensure its travelers the peace of mind that they will surely receive their luggage as promised. Delta is the first airline that offered tracking data facility for its passengers, using an app format. Customers can easily monitor their bag, reducing the uncertainty revolving luggage arrival.
Flight operations, crew operations, marketing and air cargo are some areas in airlines industry that boast of rich opportunities for Big Data solutions implementation. In our modern economy, competition is at its peak. To make your airfare rates cheaper and save big on jet fuel, shaking hands with Big Data technology is imperative.
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Most us are taking a lot of technological marvels around us for granted these days. We have casually taken note of the things like how our smartphones now help us assign photos of people or organize them, or how Facebook usually knows the right people’s faces to tag. However, it has only happened recently, which most people have not realized, that this technology is not so much of a “cool trick” and will actually shape the way people are conducting their business endeavours.
These latest technological developments are already being tested out in several different industries and for a lot of different purposes. Like for instance, security scanners at the airports are now making use of this technology to allow the e-passport holders clear their customs faster. And with the further development in facial recognition technology, the border and customs officers will be able to recognize and weed out travellers with fake passports better.
Moreover, the facial recognition technology is now being implemented in several government facilities and businesses that require a higher level of security clearance. With the use of this technology, security professionals may easily run the real-time criminal searches with the use of CCTV footage.
Now if you are like us, and constantly purchase things online then you must be aware of the fact that your choice and even your face must be with them in their database as a part of your customer profile. But these days, major retailers in physical stores are using intelligent data and trying to up their game to compete with the shopping sites. This will help them target customers faster and help them provide offers specifically tailored to these people based on their buying preferences just like at online stores.
We have provided Big Data training for Snapdeal, so why not target your customers better with a Big Data certification?
Furthermore, such a technology can also be used to catch shoplifters red handed in the act, a system that Walmart has actually implemented in place in many of its stores
When your face as a customer shows up for the first time on their screens they will start to build a profile of yours, which will be based on your in-store actions. Like for instance, the amount of time one spends in a certain area, the path around the store and items that you choose to buy.
Even the entertainment industry, like theme parks, casinos, etc have already caught up in the use of this technology to not only target marketing activities, but also to keep an eye on suspicious activities. And when it comes to greater applications for facial recognition, like in the industries of banking and fintech we are only just scratching the surface.
Several industry insiders have agreed that facial recognition will allow marketers to effectively know their customers much better, the visitor photos stored may work as the cookies for referencing for identification and for storage of users as well. So, this technology can soon eliminate loyalty cards as an obsolete art.
The moment one walks into a store the staff will already have an idea of what they bought there when visited the store the last time, and thanks to the camera footage with the facial recognition technology, will provide the retailers with an advantage to keep up their pace with ecommerce giants like Amazon, Flipkart, Alibaba etc.
One may also use facial recognition to retarget their customers with several personal offers. Like you decide to buy a certain product at a certain store, but then leave as it is slightly over budget for you. Soon you may find an internet ad or a personal message about a good discount on that product from that retailer offering you a good deal. But for all this to take proper shape, there must be a strong backup strategy, which certainly plays a strong role when it comes to way people collect, use and store data of any kind.
Thus, this will begin a whole new chapter to targeted campaigns be it online or offline or both, through the leveraging of Big Data for even a single customer.
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Taking a stroll around the lanes of Big Data is no cake walk. The main problem being that well, Big Data is big to tackle and on top of that complex to analyze and draw insights from. That is why the world needs more data analysts. Also the many nuances of Big Data architecture make it especially difficult for the concerned personnel to grasp its requirements. Also the concept is relatively new there is a lack of understanding and experience in the field of Big Data which is often the management of major corporations misuse their Big Data.
The best way to learn about how you can use your company’s Big Data effectively is by paying a close attention to how other companies have used their data and by effectively implementing similar practices. One such company who has done so is Amazon.com.
There is no hint of doubt about the data expertise of Amazon.com as it is one of the key innovators in the realm of Big Data technology. This is a giant that has given us a great idea on how to collect, analyze and then successfully implement data analytical reports. Moreover, in addition to using Big Data successfully for its own purpose the company has also leveraged its own data usage tools for helping others with tools like Amazon Elastic MapReduce.
Amazon has taught us several lessons on how to successfully implement Big Data to amplify revenue generation:
Get your eyes on the customer:
The premier uses of Amazon’s Big Data are with its customer recommendations. If one has an Amazon account they use on a regular basis then you will notice that all the recommendations on your homepage are based on your browsing history. Everything including sale items to special discount offers is based on your previous purchases and your product browsing history. Now you may argue that even several other sites including the whole of internet works like that, but while they might a frequent occurrence today Amazon was among the first ones to start this trend.
It was one of the first organizations to provide its customers with a focused and personalized buying recommendation that made them buy more. Who knew the best way to make people buy more than they want was just to tell them that with an enticing deal?! This solution is a simple one and works for several problems.
This is the best lesson that Amazon has taught the business world. For any business to succeed and to use Big Data well the main focus should be on the customers. If your customers are happy then you will be better off at your business. That is the basic rule of thumb when it comes to business after all.
Sniff out all the data you can:
This retailing giant uses Big Data gathering tools and uses it to the best of its advantages. The company gathers a lot of data by the hour or better put by the second. So, it might be easy to lose focus on why data is being gathered and which type is necessary or how it can be useful to the customers. But this company does not let those parts slide. The company gathers and analyzes its data diligently and never fails to upgrade its workings with the findings.
Big Data has worked for Amazon now make sure it works for you take Big Data coursesto better handle your data.
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Algorithms are now like the air we breathe; it has become an inevitable part of our daily lives and is also included in all types of businesses. Experts like Gartner has called this age as the algorithm business which is the key driving force that is overthrowing the traditional ways in which we do our business and manage operations.
In fact the algorithm boom with uber diversification has reached a new high, so much so that now each function in a business has its own algorithm and one can buy their own from the algorithm marketplace. This was developed by algorithm developers at Algorithmia to save the precious time and money of business operators and other fellow developers and offers a plethora of more than 800 algorithms in the fields of machine learning, audio and visual processing and computer vision.
But we as data enthusiasts in the same field with an undying love for algorithm would like to suggest that not all the algorithms from the Algorithmia marketplace may be suitable for your needs. Business needs are highly subjective and environment based. And things as dynamic as algorithms can produce different types of results even in the slightly different situations. Also the use of algorithms depends on a number of factors on how they can be applied and what results one can expect from their application. The variables on which the application of algorithms depends are as follows: type and volume of the data sets, the function the algorithm will be applied for and the industry in which the algorithm will be applied.
Hence, not always reaching for the easy option of buying a readymade algorithm off the shelf and simply tweaking it to fit into your model may not always be the most cost-effective or time saving way to go. So, it is highly recommended for data scientists to educate themselves well on the most important algorithms that must be known by them, as well as the back of their hands. A data scientist must also know how each algorithm is developed and also which purpose calls for which algorithm to be applied.
So, our experts associated with DexLab Analytics developed an infographic to let big data analysts know the 12 most essential algorithms that must still be included in the repertoire of a skilled data scientist. To know more about data science courses drop DexLab Analytics and find your true data-based calling.
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Big Data is big. We have all established that, and now we know that all the noise about Big Data is not just hype but is reality. The data generated on earth is doubling in every 1.2 years and the mountainous heap of data keep streaming in from different sources with the increase in technology.
Let us look at some data to really understand how big, Big Data is growing:
The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device
On an average every day almost 11 billion texts are sent across the globe
8 million videos are watched on YouTube alone
The global number of Google searches everyday is 5 billion
But the balance has long been tipped off as we have only been creating data but not consuming it enough for proper use. What we fail to realize is the fact that we are data agents, as we generate more than 25 quintillion bytes of data everyday through our daily online activities. The behaviors that add more numbers to this monstrous hill of data are – online communications, consumer transactions, online behavior, video streaming services and much more.
The numbers of 2012 suggest that world generated more than 2 Zetabytes of data. In simpler terms that is equal to 2 trillion gigabytes. What’s more alarming is the fact that by the year 2020, we will generate 35 trillions of data. To manage this growing amount of data we will need 10 times the servers we use now by 2020 and at least 50 times more data management systems and 75 times the files to manage it all.
The industry still is not prepared to handle such an explosion of data as 80 percent of this data is mainly unstructured data. Traditional statistical tools cannot handle this amount of data, as it is not only too big, but is also too complicated and unorganized to be analyzed with the limited functions offered by traditional statistical analysis tools.
In the realm of data analysts there are only 500 thousand computer scientists, but less than 3000 mathematicians. Thus, the talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds prepared to untangle the complex knots of intertwined data hiding useful information.
But to truly harness the complete potential of Big Data we need more human resource and more tools. For finding value we need to mine all this data.
Then what is the solution to this even bigger problem of tackling Big Data? We need Big Data Analytics. This is more than just a new technological avenue, but on the contrary this is fresh new way of thinking about the company objectives and the strategies created to achieve them. True understanding of Big data will help organizations understand their customers. Big Data analytics is the answer behind where the hidden opportunities lie.
A few advanced tools that are currently in use in the data analysis industry are: SAS, R Programming, Hadoop, Pig, Spark and Hive. SAS is slowly emerging to be an increasingly popular tool to handle data analysis problems, which is why SAS experts are highly in-demand in the job market presently. To learn more about Big Data training institutesfollow our latest posts in DexLab Analytics.
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