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How India is driving towards Data Governance

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Data is power – it’s the quintessential key to proper planning, governance, policy decisions and empowering communities. In the recent times, technological expansion is found to be contributing immensely towards ensuring a sustainable future and building promising IT base. Robust developments in IT related services have resulted into key breakthroughs, including Big Data, which as a result have triggered smooth data governance.

How India is driving towards Data Governance

According to a NASSCOM report, India’s analytics market is expected to grow from $1 billion to $2.3 billion in the year 2017-18. However, the fuller benefits of data analytics are yet to be channelized by the public sector.

In a varied country like India, data collection is a lengthy procedure. At present, information is being collected by various government departments straight from Panchayat levels to state levels. Though, most of the data remains trapped within department walls, it is largely used to pan out performance reports. Also, certain issues in timely collection of data pops up, while sometimes the quality of data collected becomes questionable, hence delaying the entire analysis.

 

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Quality data plays an integral role, if analyzed properly at the proper time. They can be crucial for decision-making, delivery of services and important policy revisions. As a matter of fact, last year, Comptroller and Auditor General (CAG) initiated Centre for Data Management and Analytics (CDMA) to combine and incorporate relevant data for the purpose of auditing. The main purpose here is to exploit the data available in government archives to build a more formidable and powerful Indian audit and accounts department.

Indian government is taking several steps to utilize the power of data – Digital India and Smart Cities initiatives aim to employ data for designing, planning, managing, implementing and governing programs for a better, digital India. Many experts are of the opinion that government reforms would best work if they are properly synchronized with data to determine the impact of services, take better decisions, boost monitoring programmes and improve system performances.

Open Data Policy is the need of the hour. Our government is working towards it, under the jurisdiction of the Department of Information and Technology (DIT) to boost the perks of sharing information across departments and ministries. Harnessing data eases out the load amongst the team members, while ensuring better accountability.

Tech startups and companies that probe into data and looks for solutions in data hoarding and analytics to collect and manage complicated data streams need to be supported. The government along with local players should encourage citizens to help them in collecting adequate information that could help them in long-run. India is walking towards a rapid economic development phase, where commitment towards information technology, data governance and open-source data is of prime importance. For the overall economy, bulk investments in capacity building, technology implementation and data-facilitating structures should be considered and implementable to bring plans and participation into place to hit off a better tech-inspired reality.

For data analyst certification in Delhi NCR, drop by DexLab Analytics – it’s a prime data science online training centre situated in the heart of Delhi.

The original article appeared on – https://economictimes.indiatimes.com/small-biz/security-tech/technology/indias-investment-in-big-data-will-ensure-governance/articleshow/57960046.cms

 

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New Intelligence is being added to Massive Storage Management System

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Pioneers of High Performance Storage System (HPSS) are devising ways to streamline and rationalize data management products for its upcoming eighth generation. 25 years back, US Department of Energy research laboratories and IBM together built HPSS to support massive government science research projects. Why? The Hierarchical storage solution is undeniably a rewarding concept which uses organization policies and software automatic tricks to decide which data to save, the location where it should be saved, the best time to move it to different storage devices and when to delete it.

New Intelligence is being added to Massive Storage Management System

“How do you know what you’re archiving? We’re talking about archives now that are hundreds of petabytes to an exabyte. We think we’re going to be there in 2-3 years,” asked Todd Herr, a storage architect for supercomputing from Lawrence Livermore National Laboratory, CA.

The HPSS website catalogues 37 publicly disclosed customers, while other customers are kept discreet. At present, version 7.5.1 from last year is on the run, but version 7.5.2 might be hit, while the next year will see 7.5.3, as given in the online roadmap.

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However, version 8 is not yet available on the official roadmap, but here’s what the insiders have to say about it…

“What I think our challenge is, is to become good data curators. And I think that’s where we’re going to point the product,” Herr shared. This will turn HPSS become more capable for data mining and assign metadata to itself.

In order to do that, the first thing to be done is to reveal information in the archive about a few overarching namespace applications. Herr explained, “Right now we are working on that (referring to software made by companies such as Atempo, Robinhood, Starfish, and StrongLink). I think the next step there is scaling out metadata performance, such as database partitioning and virtualizing multiple processors when performing searches.”

Another important part of HPSS is related to the software that works with tape storage – “What we’re trying to do is enable fast access to tape. If you look across the industry spectrum, the words fast and tape generally don’t go together,” Herr intimidated. The scientists at Livermore are capable of accessing research data on tape, even that existed more than 50 years ago.

Speed-matching buffers can save the day – when placed between primary disk storage and archive tape storage, they can be used to both read and write. Some other physical improvements include faster head placements and tape motors.

“We’re going to hit a problem way faster than most sites, and certainly faster than the vendors themselves because they cannot replicate our environment in most testing,” Herr asserted.

Herr’s employer’s next supercomputer, Sierra is going to operate at up to 125 petaflops and will have a 125-petabyte file system for performing ample tests to find new ways of speeding up performance and administer advanced data storage mechanisms.

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The article has been sourced from – https://www.techrepublic.com/article/fed-and-ibm-researchers-adding-new-intelligence-to-massive-storage-management-system

For more such interesting ideas and discussions, stay tuned to DexLab Analytics. It is a premier analytics training institute headquartered in Delhi, NCR. Their data science certification courses are excellent.

 

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Watch Out: Top Retail Trends 2018 That Might Redefine Industry Goals

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They must change – retailers finally understood this basic but true fact. For years, the retail honchos were averse to change – they preferred everything to be smooth and consistent like they were in previous years.

Watch Out: Top Retail Trends 2018 That Might Redefine Industry Goals

Now, the retail game-play is changing altogether. Today, it’s the customer who defines the entire shopping experience. No longer, storing data in traditional silos is termed as a viable option – the integration of omni-channel trade and tech-inspired merchandizing is the go-to option. Already, several well-funded retailers and global store giants are on their way to exploit the data power – they are adjusting their working mechanisms and resorting to assortment and innovations because that’s the only way to survive and sail away!

Looking ahead, here are some of the biggest retail trends to watch in 2018:

A dramatic evolution in technology

Technological transformation holds a fresh can of possibilities for retailers, but its implementation demands a lot of attention. While 2017 was reckoned to be the year of digital discovery, 2018 is going to be the year when retailers will adapt with the changing market and experience evolution in their customer’s needs. Hence, evolution will be the key to success.

Opportunities in AI are also on the rise. Chatbots, robotics and facial recognition and image recognition technologies are unleashing robust opportunities this year. Retailers are hoarding large chunks of data to curate personalized experiences for customers, and win their hearts away. More data means improved algorithm performance, and the best thing is that retailers are going on generating significant amounts of data, through both offline and online mediums. Artificial intelligence in retail can be utilized in many ways, right from improving product specifications and enhancing customer service experience.

Artificial intelligence coupled with machine learning and Internet of Things supports customer experience – there exists amazing opportunity for retailers to gain by using these new age concepts. For better data utilization, get yourself an excellent data analyst training from DexLab Analytics.

Mobile payments will usher us into a cashless economy

China has already gone cashless; thanks to AliPay and WeChat Pay. Following that, the rest of the world is looking up to the likes of Amazon Pay, Walmart Pay, Apple Pay and other types of cryptocurrencies. It’s only a matter of time before global consumers replace their plastic debit cards with more efficient and faster mobile payment options.

Work on improving offline experiences too

Not only online, but retailers should consider looking into offline experiences – how they can keep shopping as human, real and visual as possible. The mode of shopping might be transforming, but humans and their preferences are still the same. Customer experience is still important and offline experience will just focus on that.

Robotic retail is scaling up

In the E-commerce industry, the robot to human ratio is fast changing. While Walmart is testing retail robots, drone delivery is increasingly becoming popular and a viable solution. By 2020, its predicted consumer facing robots will show up in retail stores, all over.

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Improvements in technology mean a lot of retail growth. And when its technology, we can’t leave behind DATA. It’s like the new currency in the retail scenario – for a comprehensive Retail Analytics Courses, visit DexLab Analytics.

The article has been sourced from:

https://www.forbes.com/sites/pamdanziger/2017/12/27/retail-shopping-predictions-2018/#1116fcdafb33

 

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Breaking the Misconceptions: 4 Myths Regarding Data-Driven Financial Marketing

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A majority of low-mid financial services companies toil under the wrong notion that owing to their capacity, size and scope, the complex data-driven marketing tactics are simply out of their reach – this is not true and frankly speaking quite a shame to consider even.

BREAKING THE MISCONCEPTIONS: 4 MYTHS REGARDING DATA-DRIVEN FINANCIAL MARKETING

Over the past decade, the whole concept of data analytics has undergone a massive transformation – the reason being an extensive democratization of marketing tactics. Today’s mid-size financial service providers can easily implement marketing initiatives used by dominant players without any glitch.

Besides, there are several other misconceptions regarding data and its effect on financial marketing that we hear so often and few of them are as follows:

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Myth1 – Legally, banks are only allowed to run broad-based advertising

While it’s partially true that there are certain restrictions on banking institutions when it comes to target consumers, based on income, age, ethnicity and other factors, marketers can still practice an array of tactics, both online and offline.

Marketers can leverage a pool of data for online and offline marketing to formulate data models, keeping in mind the existing customers need and preferences. Once you have an understanding of their online behavior, how they use the data power to carry out transactions, these insights can be applied to attract new customers, who exhibit similar behaviors.

Myth 2 – Data-driven marketing doesn’t bolster customer relationship

It’s a fact, Millennials, especially wants to be aware about financial services and its associated products, and are keen to understand how can banks lend an additional support to their living and social life. Companies can start building relationship based out of it, while implementing data-driven marketing perspective into them.

Myth 3 – You need a huge budget and an encompassing database to drive marketing campaigns

Corporate honchos and digital natives certainly maintain sprawling in-house database to boost marketing activities, but don’t be under the impression that mid-size institutions cannot leverage much from virtual datamart. The impressive SaaS-based solutions houses first-party data, safely and securely and offer you mechanisms that let you integrate with other third-party data, both online and offline.

Datamarts let mid-size marketers achieve a lot of crucial task success. Firstly, you will be able to link online user IDs with offline data – this lets you derive insights about your current customers, including their intents, interests and other details. The most important thing is that it will usher you to build customer models that could target newer customers for your bank.

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Myth 4 – Data-driven marketing is too much time-consuming

A lot of conventional marketers are of the opinion data-driven marketing is a huge concept – time-consuming and labor-intensive. But, that’s nothing but a myth. Hundreds and thousands of mid-size companies develop models, formulate offers and execute campaigns within a 30-day window using a cool datamart.

However, the design and execution part of campaigns need no time, whereas the learning part needs some time. You need to learn how to develop such intricate models, and that’s where time is involved.

To ace on financial models, get hands-on training from credit risk analysis course online – DexLab Analytics offers superior credit risk management courses, along with data analytics, data science, python and R-Programming courses.

In the end, all that matters is prudent marketing campaigns powered by data yields better results than holding onto these misconceptions. So, break the shackles and embrace the power of data analytics.

The article has been sourced from – http://dataconomy.com/2017/08/5-misconceptions-data-driven-marketing

 

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Quantum Computing Going Commercial: IBM and Google Leading the Trail

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Quantum computing is all set to make a debut in the commercial world – tech bigwigs, like IBM and Google are making an attempt to commercialize quantum computing. Julian Kelly, a top notch research scientist at Google’s Quantum AI Lab announced with a joint collaboration with Bristlecone, a quantum processor that offers a testbed for various research activities on quantum technology and machine learning, quantum supremacy can be achieved and this could be a great stepping stone for building larger scale quantum computers.

QUANTUM COMPUTING GOING COMMERCIAL: IBM AND GOOGLE LEADING THE TRAIL

After Google, IBM is also making significant progress in commercializing quantum computing technology by taking it to the cloud in 2016 with a 5 qubit quantum computer. Also, last year, November they raised the bar by declaring that they are going to launch third generation quantum computer equipped with a 50 quibit prototype, but they were not sure if it will be launched on commercial platforms, as well. However, they created another 20 qubit system available on its cloud computing platform.  

Reasons Behind Making Quantum Computing Commercialized:

Might lead to fourth industrial revolution

Quantum computing has seeped in to an engineering development phase from just a mere theoretical research – with significant technological power and constant R&D efforts it can develop the ability to trigger a fourth industrial revolution.

Beyond classic computing technology

Areas where conventional computers fail to work, quantum computing will instill a profound impact – such as in industrial processes where innovative steps in machine learning or novel cryptography are involved.

Higher revenue

Revenues from quantum computing are expected to increase from US$1.9 billion in 2023 to US$8.0 billion by 2027 – as forecasted by Communications Industry Researchers (CIR).

Market expansion

The scopes of quantum computing have broadened beyond expectations – it has expanded to drug discovery, health care, power and energy, financial services and aerospace industry.

From cloud to on-premise quantum technology

To incorporate quantum computing into the heart of the business operations’ computing strategy, the companies are contemplating to add a new stream of revenue by implementing quantum computing via cloud. In the future, it’s expected to see a rise in on-premise quantum computing – because the technology is already gaining a lot of accolades.

Better growth forecasts

In the current scenario, the quantum enterprise market is still at a nascent stage with a large user base in the R&D space. But by 2024, it has been forecasted that this share would be somewhere around 30% and the powerful revenue drivers will be industries, like defense, banking, aerospace, pharmaceutical and chemical.

IBM or Google? Who is a clear winner?

In the race to win quantum supremacy, IBM is a sure winner and has made stunning progress in this arena, even though it is receiving stiff competition by Google recently. Google’s new quantum processor Bristlecone has the ability to become a “compelling proof-of-principle for building larger scale quantum computers”. For this, Julian Kelly suggested, “operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself. Getting this right requires careful systems engineering over several iterations.”

 

As last notes, quantum computing has come out from being a fundamental scientific research to a structural engineering concept. Follow a full-stack approach, coupled with rapid testing and innovative practices and establish winning control over this future tool of success.

In this endeavor, DexLab Analytics can for sure be of help! Their business analytics certification online courses are mindblowing. They also offer machine learning using python courses and market risk training – all of them are student-friendly and prepared after thorough research and fact-finding.

 

The article has been sourced from – https://analyticsindiamag.com/why-are-big-tech-giants-like-google-ibm-rushing-to-commercialize-quantum-computing

 

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How to Develop a Data-Driven Culture among the Employees and Organization?

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Data creation and consumption is exploding. So is the challenge of analyzing data and transforming it into actionable insights.

How to Develop a Data-Driven Culture among the Employees and Organization?

The expert consultants at IBM say – 90% of the data in the world has been created in the last couple of years at a rate of 2.5 quintillion bytes per day. Just imagine, when such gold mines of information go unused, what toll does it take on companies who harbors them? The loss is insurmountable, isn’t it? 

But the question that makes us brood is how do companies empower employees to use such vast pools of data to reveal hidden business insights?

To keep pace with an accelerating growth of data generation and robust competitive landscape, new age companies need to shift their focus to sound Business Intelligence solutions that helps in collecting and analyzing data to determine patterns and alert users, in case of anomalies in the nature of business. And the good news is that they are doing so.

Once companies start off with data handling and data mining, the issue of utilization needs to be addressed next – the trickiest problem with data utilization is the insights that take place within the departments –while creating data silos. Siloed data results in creating a lot of issues, but the larger one is that it creates only a partial view of whatever is happening within an organization and a bigger picture is not available.

As a result, a data-driven culture is to be adapted – but how?

 

Let’s Take Your Data Dreams to the Next Level

Right from the top-level

The true DNA of any organization lies within its top-level management team, including the founders and managing directors – and that’s where the foundation stone of data-driven culture should be implanted. Implementing something new and offbeat is intimidating, but when the company leaders promote it, the idea gains familiarity and merges with other data-driven decisions.

Empowerment

Employees need to feel empowered – then only they can independently mine data and share crucial findings with colleagues and seniors without asking for help from the IT guys. A common misconception that exists around is that data analytics is a cumbersome task which requires heavy involvement from IT- but in reality, things are changing – BI tools are being revamped to make users more independent and self-sufficient to take better business decisions.

Time and again, it’s important for the executive management to show some token of appreciation to employees for their hard work. It is in these subtle ways the data-driven culture gets promoted across company walls.

 

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Sharing is caring

Now, the data siloes come into the picture. Once employees are comfortable with using data and bearing the fruits of success – insights need to be shared across the business fronts to draw a much larger picture.  Not only it promotes cross-team and departmental collaboration, but also brings in newer data into the limelight that wouldn’t have been possible before. Hence, sharing of insights is crucial for business success.

From the above discussion it is clear, insights gained from data is extensively beneficial for business, as they offer new answers for innovation and development. However, achieving data nirvana is no mean feat – the steps highlighted above should be followed, and then only the companies would be able to achieve their desired goal, i.e. a seamless data-driven culture.

Accelerate your career with business analyst training Delhi NCR. DexLab Analytics offers 360-degree Data Science Online training in Delhi – interested candidates please visit the website.

Sources: http://dataconomy.com/2017/05/data-nirvana-develop-data-driven-culture

How This Bengaluru Startup Is Using AI to Detect Early Stage Breast Cancer in Women

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The World Health Organization says – one out of two women diagnosed with breast cancer dies within five years in India. In the US, the fatality is less than one out of five, and in China, one out of four. Shortages of technology for early detection and radiographers coupled with the expense of regular screening, which normal people find too expensive to afford in India have led to an increasing number of breast cancer cases of late. Today, breast cancer has outstripped cervical cancer as the major cause of cancer death among women in this country.

 
How This Bengaluru Startup Is Using AI to Detect Early Stage Breast Cancer in Women
 

A Bengaluru-based tech startup and the brainchild of Geetha Manjunatha (CEO) and Nidhi Mathur (COO), NIRAMAI offers breast cancer screening solution by combining artificial intelligence, machine learning and cloud. It aims at tackling the issue of accessibility and expenses of breast cancer screening. These two dynamic women had seen cancer very closely in their family and feel an emotional connect with anyone who is diagnosed with this deadly disease. This led to the conceptualization of NIRAMAI, which means BEING WITHOUT DISEASES in Sanskrit. Also, it’s an acronym for “Non-Invasive Risk Assessment through MAchine Intelligence”.

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The Working Principle

The breast cancer screening solution by NIRAMAI is non-invasive, non-contact and non-radiation process of detecting early stage breast cancer amongst women of all ages. The deep technology that it claims to have patented is Thermalytix technology – a fusion of top-grade machine learning algorithms over thermal images.

“Thermography is well known to sense earliest signs of cancer. However, traditional manual interpretation of a thermogram has not been accurate enough to become accepted as a standard of care. Interpreting 400000 colour values in thermograms and to diagnose breast abnormality is a huge cognitive overload to a radiologist – use of machine learning enables automated analysis and helps in better interpretation of thermal images and considerably improves the overall accuracy of diagnosis”, says Geetha, one of the cofounders of NIRAMAI.

The working mechanism of screening in NIRAMAI is quite simple, and effective. The women who want to get screened need to relax for the first 10 minutes before taking up the test. Then a high resolution thermal sensor is kept at a distance of 3 feet from her to measure the temperature distribution on her chest and generate thermal images. Next, the NIRAMAI software scans these thermal images to automatically initiate a screening/diagnostic report and hands over a radiologist-certified report to the women. The test is performed in a highly intimate manner, the women undertaking the screening is neither touched nor seen by anyone.

“This is unlike mammography which is based on X-Ray and is recommended for women above 45 years only once in 2 years. It is also noncontact and doesn’t require any breast compression; hence not painful. Since the equipment is very portable, it is amenable to be used in outreach programs being a rural camp or urban corporate screening,” she shares.

Overcoming challenges

In healthcare space, analytics and AI are dubious topics. It takes a lot to coax a doctor to use an AI tool as an aid in his diagnostic procedure – countless discussions, several experimental trials and after a lot of effort, NIRAMAI could finally step into and create a niche of their own.

Another challenge was to have an edge over their competitors, who once knew that they are out with a revolutionizing technology, would like to sell everything to copy that. For that, they have armed themselves with 10 patents in this area, which is somewhat protecting them from other players.

Since breast cancer is a big health issue in India, the NIRAMAI team feels that it is extremely important for women to go for regular screening. It is safe and in most cases, early detection helps keep cancer at bay.

The power of analytics is huge. Arm yourself with a powerful data analyst certification Delhi NCR. It will help you go a long way!

Some parts in this blog have been sourced from:

https://analyticsindiamag.com/this-women-led-startup-is-using-ai-thermal-imaging-to-detect-breast-cancer

https://www.techinasia.com/startup-patented-ai-tech-breast-cancer-screening

 

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Data Analytics: The Key to Track and Curb Leakages in GST

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Though our country may have got a One Nation, One Tax Policy, in the face of GST, its revenue collection figures are not so encouraging. In the beginning, GST revenue collection for the first three months went over 90000 crore, but the figures started dropping from October to 83346. And in November, it further slipped to 80808 crore. Since then, the figures mostly lingered around 86000 in the recent months.

 
Data Analytics: The Key to Track and Curb Leakages in GST
 

The Union Ministry of Finance had to figure out the reason of this discrepancy, the reason behind such huge revenue leakage in GST collection before it’s too late, and for that data analytics came to the rescue. After carrying out a thorough analysis, on its 26th meeting on Saturday, GST Council discovered several major data gaps between the self-declared liability in FORM GSTR-1 and FORM GSTR-3B.

 

Highlighting the outcome of basic data analysis, the GST Council stated that the GST Network (GSTN) and the Central Board of Excise and Customs have found some inconsistency between the amount of Integrated GST (IGST) and Compensation cess paid by importers at customs ports and input tax credit of the same claimed in GSTR-3B.

 

 

“Data analytics and better administration controls can help solve GST collection challenges” – said Pratik Jain, a national leader and partner, Indirect Tax at PricewaterhouseCoopers (PwC).

 

He added, “Government has a lot of data now. They can use the data analytics to find out what the problem areas are, and then try and resolve that.” He also said that to stop the leakage, the government need to be a lot more vigilant and practice better controls over the administration.

 

Moreover, of late a parliamentary committee has found that the monthly collection from GST is not up to the mark due to constant revisions of the rates, which has undoubtedly affected the stability of the tax structure and had led to an adverse impact for trade and business verticals.  

 

 

“The Committee is constrained to observe the not-so-encouraging monthly revenue collections from GST, which still have not stabilised with frequent changes in rates and issue of notifications every now and then. Further, the Committee is surprised to learn that no GST revenue targets have been fixed by the government,” said M Veerappa Moily, the head of Standing Committee on Finance and a veteran Congress leader in a recent report presented in the Parliament.

 

The original article appeared in – analyticsindiamag.com/government-using-data-analytics-to-track-leakages-in-gst/

To experience the full power of data analytics and the potentials it withholds, find a good data analyst training institute in Delhi NCR. A reliable data analytics training institute like DexLab Analytics can help you unearth the true potentials of a fascinating field of science – go get details now.

 

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How Data Analytics Is Shaping and Developing Improved Storage Solutions

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Technology has penetrated deep into our lives – the last 5 decades of IT sector have been characterized by intense development in electronic storing solutions for recordkeeping.

 
How Data Analytics Is Shaping and Developing Improved Storage Solutions
 

Today, every file, every document is stored and archived safely and efficiently – rows of data are tabled in spreadsheets and stored in SQL relational databases for smooth access anytime by anyone, of course the authorized persons. Data is omnipresent. It is being found in data warehouses, data lakes, data mines and in pools. It is so much large in volume nowadays, that it can even be calculated in something like a Brontobyte.

 

Information is power. Data stored in archives are used to make accurate forecasts. And the data evaluation has begun within a subset of mathematics powered by a discipline named probability and statistical analysis.

 

Slowly, this discipline evolved into Business Intelligence that further into Data Science. The latter is the most sought after and well-paid career option for today’s tech-inspired generation. Grab a data science certification in Gurgaon and push your career to success.

 

Big Data Storage Challenges and Solutions

The responsibility of storage, ensuring security and provide accessibility for data is huge. Managing volumes and volumes of data is posing a challenge in itself – for example, even powering and cooling enough HDD RAID arrays to keep an Exabyte of raw data tends to break the bank for many companies.

 

Software-defined storage and flash devices are being deployed for big data storage. They promise of better direct business benefit. Also, increasingly Apache Spark Hadoop or simply Spark is taking care of the software side of big data analytics. Whether your big data cluster is developed on these open-source architectures or some other big data frameworks, it will for sure impact your storage decisions.

 

Hadoop is in this business of storage for big data for quite some time now. It is a robust open-source framework opted for suave processing of big data. It led to the emergence of server clusters and Facebook is known to have the largest Hadoop cluster containing millions of nodes.

 
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Now, the question remains where and how you proceed with Hadoop – there are so many differing opinions about how you approach Hadoop clusters, at times it may leave you exasperated. For that, we can help you here.

 

With a huge array of data at play, we suggest to deploy a dedicated processing, storage and networking system in different racks to avoid latency or performance issues. It is for the same reasons, we ask you to stay away running Hadoop in a virtual environment.

Instead, implement HDFS (Hadoop Distributed File System) – it is perfect for distributed storage and processing with the help of commodity hardware. The structure is simple, tolerant, expandable and scalable.

 

Besides, the cost of data storage should also be given a look at – cost should be kept low and data compression features should likely to be implemented.

For Big Data Hadoop certification in Delhi NCR, drop by DexLab Analytics.

 
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The Takeaway

Times are changing, and so are we. Big data analytics are becoming more real-time, hence better you scale up to real-time analytics. Today, data analytics have gone way beyond the conventional desktop considerations – it has now become a lot more, and to keep pace with the analytics evolution, you need to have sound storage infrastructure, where possible upgrades to computing, storage and networking is easily available and implementable.

 

To answer about big data or Hadoop, power yourself up with a good certification in Big Data Hadoop from DexLab Anlaytics – such intensive big data courses do help!

 

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