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Big Data: 4 Myths and 4 Methods to Improve It

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

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Big Myths of Big Data

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

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

Now the real picture:

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

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

Here are 3 main problems with that notion:

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

How to Improve:

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

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

What all get’s better:

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

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

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

 

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The Success Story of Big Data Tooling

The Success Story of Big Data Tooling

The world of hadoop data tooling is flourishing. It’s being said, Hadoop is shifting from possible data warehousing to an accomplished big data analytics set-up.

Back in the day, right after Hadoop at Yahoo was first invented, proponents of big data asserted its potential for substituting enterprise data warehouses, framed on business intelligence.

Open source Hadoop data tooling became a preferred choice more as an alternative to those insanely expensive existing systems – as a result, over time, the focus shifted to expanding existing data warehouses and more. Intricate Hadoop applications today are known as data lakes and of late big data tooling is found swelling beyond meager data warehouses.

“We are seeing increasing capabilities on the Hadoop and open source side to take over more and more of the corporation’s data and workloads, including BI,” said Mike Matchett, an analyst and founder of the Small World Big Data consultancy.

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Self Service and Big Data

In August, Cloudera launched Workload XM management services designed exclusively for cloud-based analytics. Alternatively, the company built a hybrid Cloudera Data Warehouse and a Cloudera Altus Data Warehouse, capable of running over both Microsoft Azure clouds and AWS.

The main objective of management services is to bring forth some visibility into various data workloads. Workload XM is constructed to aid administrators in presenting reliable service-level agreements for self-service analytics applications – says Anupam Singh, GM of Analytics at Cloudera, Palo Alto, Calif.

Importantly, Singh also mentioned that the cloud warehouse offers encryption for data both at still and in motion, and provides a better view into the trajectory of data sets in analytics workloads. Such potentials have gained momentum and recognition as well as GDPR and other programs.

However, all these discussions boil down to one point, which is how to increase the use of big data analytics. “Customers don’t look at buzzwords like Hadoop and cloud. But they do want more business units to access the data,” he added.

Data on the Wheels

Hadoop player, Hortonworks is a Cloud aficionado. In June, the company broadened its Google Cloud existence with Google Cloud Storage support. Enhancing real-time data analytics and management is a priority.

Meanwhile, in August, Hortonworks churned out Streams Messaging Manager (SMM) with an objective of handling data streaming and provide administrators comprehensive views into Kafka messaging clusters. They have increasingly become popular amongst big data pipelines.

These management tools are crucial for moving Hadoop-inspired big data analytics into production capacities, where in data warehouses fails performing – thus, recommendation engines and fraud detection appears to be a saving grace!

Meanwhile, Kafka-related capabilities in SMM are going on getting advanced and with recently released Hortonworks DataFlow 3.2, the performance for data streaming amplified.

R Adaptability

Similar to its competitors, MapR has bolstered its capabilities beyond its original scope of being used as a mere data warehouse replacement. Early this year, the organizers released a new version of its MapR Data Platform equipped with better streaming data analytics and new item data services that would easily work on cloud as well as premises.

As final thoughts, the horizon of Hadoop is expanding, while data tooling keeps modifying. However, today, unlike before, Hadoop is not only the sole choice for doing data analytics – the choice includes Apache Spark and Machine Learning. All being extremely superior and effective when put to use.

If you are looking for Apache Spark Certification, drop by DexLab Analytics. Their Apache Spark Training program is extremely well-crafted and in sync with industry demands. For more, visit the site.

 

The article has been sourced from — searchdatamanagement.techtarget.com/news/252448331/Big-data-tooling-rolls-with-the-changing-seas-of-analytics

 

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How Google Is Fighting Floods in India with AI

How Google Is Fighting Floods in India with AI

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

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

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

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

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

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

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

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

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

 

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6 Indian Union Ministries That Are Using Artificial Intelligence

6 Indian Union Ministries That Are Using Artificial Intelligence

It’s no brainer; artificial intelligence has seeped deep into our lives and it has even caught the attention of Indian government. Indian government initiatives are proof of that. The NDA-led BJP government is an admirer of new technology and has given enough importance to AI by setting up an AI Task Force to arm India for the Industrial Revolution 4.0.

In fact, if you follow through a series of events or speeches, you’ll find how frequently our Prime Minister, Narendra Modi projects India and his government to be technologically-driven. In this blog, we’ll share how our top 6 Union Ministries are using Artificial Intelligence on a wider scale for better economy and powerful country presence:

Ministry of Defense

AI Task Force of the Ministry of Defense led by Tata Sons Chairman N Chandrasekaran filed its final report to Defense Minister Nirmala Sitharaman about employing AI for military superiority. The report includes recommendations regarding making India AI-empowered, both in terms of offensive and defensive needs, across naval, aviation, cyber, land, nuclear and biological warfare verticals.

NITI Aayog (the former Planning Commission)

The National Institution for Transforming India recently identified 5 sectors, namely ealthcare, agriculture, education, smart cities and infrastructure, smart mobility and transportation where profound importance will given towards AI implementation to serve societal needs.

Ministry of Information and Broadcasting

Recently, BECIL (Broadcast Engineering Consultants India Limited), functioning under the Ministry of Information and Broadcasting unveiled a tender showing the present government is more likely to take public opinion and media seriously. The government has chalked out a proposal for a respective “technology platform”, which would tap into public emotions by analyzing social media blogs, accounts, posts and even emails to promote nationalism and negate any media bickering by India’s adversaries.

Ministry of Railways

Indian Railways has been in a line of fire for its food catering services. Say thanks to Artificial Intelligence – AI is transforming the way the food is prepared. Not only is it revamping the entire food menu in trains but also promoting a greener environment by going bio-degradable and delivering food in environment-friendly containers.

Ministry of Home Affairs

First of its kind, Intelligence Traffic Management System is going to be installed by the Delhi Police, under the supervision of Home Ministry. This initiative will initiate smart traffic signals, with the help of AI and their first phase is going to be completed by April 2019.

Ministry of External Affairs

With intent to enhance the flow of information between countries, the Ministry of External Affairs had recently conducted a confidential meeting including global AI stalwarts to discuss how to drag attention of Indian Diaspora.

A quick bite: Niti Aayog, under the guidance of CEO Amitabh Kant has been a key stimulator of numerous digital campaigns across the country, including Aadhaar, encompassing biometric programme and India Chain Project.

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The blog has been sourced from – analyticsindiamag.com/7-indian-union-ministries-who-have-embraced-artificial-intelligence-big-time

 

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How Big Data Analytics Power Profits for the Hospitality Industry

How Big Data Analytics Power Profits for the Hospitality Industry

The hospitality industry is highly dependent on customer satisfaction. And the analysis of big data can help this industry predict customer behavior by understanding their needs and expectations. This in turn enables the hotels and restaurants to provide personalized customer service and retain loyal customers.

Hospitality service provider Airbnb is making the most of the new ‘’mobile first’’ approach where responsive designs are created for the smallest screens. This mode allows customers to engage in Airbnb business through phones. Although big data and its analysis is a large part of the success for this industry, many companies are yet to fully understand the gains associated with big data.

Here are some ways how big data enables the hospitality industry to drive profits:

Take better control of business:

Effectively analyzing big data drastically changes how the business runs. The hotel industry is a data rich sector with massive volumes of web, audio and video content. However, many hotels don’t use their data to its full potential. For instance, hoteliers collect loyalty information but few exploit the data for making business decisions. Through analytical data exploitation, hotels can deepen their understanding of the behavior, needs and expectations of guests and develop better loyalty programs.

Customer segmentation and Targeting:

Hotels must use customer data to provide better customer service as that is essential for ensuring that customers return to avail their services again. Analyzing this data is crucial to segment customers based on booking and travel trends, preferences, chance of responding to promotions, etc. Targeting clients with wrong offers can hinder business growth. Data analysis allows them to retain their best repeat clients by good incentives and promotions. It also allows them to build separate deals for customers who don’t visit them often with the hopes of converting them to loyal customers.

Set best prices for rooms:

Big data analytics is very important for setting competitive hotel prices so that it attracts more guests. Apart from setting the best price for rooms, hospitality-driven businesses can optimize the budget for utilities through analysis of weather data and energy rates.

On-time delivery:

As big data tools become more and more advanced, it shall enable better collection of data from traffic, temperature, weather, route and other sources. This will improve food delivery by providing better estimates of time taken to deliver. Moreover, it shall help restaurants understand how all the aforementioned sources affect the quality of food. Thus, it helps to plan the transportation beforehand and optimizes the utilization of resources.

Menu enhancement:

Using the customer data on food preferences, restaurants can build a customer profile that contains their favorite food and drinks. From the data gathered through feedback forms and online surveys, they can identify the most popular items in their menu and determine whether their menu needs to be improvised or completely reengineered.

Hence, new sources of data and emerging technologies like IoT (Internet of Things) and AI (Artificial Intelligence) enable the hospitality industry to understand the current trends in the market and boost the overall profit of the enterprise.

Companies who are embracing the power of big data are reaping huge profits, and students who are enrolling for big data Hadoop courses are earning big bucks! So, unlock your career with a big data Hadoop certification in Gurgaon. And follow DexLab Analytics for the latest big data related blogs and information.

References:

www.smartdatacollective.com/hospitality-industry-emergence-big-data

www.hiddenbrains.com/blog/big-data-analytics-driving-restaurant-industry-towards-profitable-growth.html

insidebigdata.com/2018/08/03/three-industries-profiting-big-data

 

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Tapping Into Big Data for Better Talent Acquisition

Tapping Into Big Data for Better Talent Acquisition

There are many variables that need to be considered while making hiring decisions. Most importantly, there’s need to fill skill gap. Other factors are candidate behavior and financial aspects of hiring, like cost for training new employees. Big data and analytics help form valuable insights into the job market. Consider the example of IBM acquiring the services of consulting firm Kenexa. It was used to access data of 40 million workers in order to find the personality trait most suitable for a sales job. All kinds of information starting from the workers’ job applications to managerial level was analyzed and it was determined that ’persistence’ is the most valued trait.

Here are some important ways big data helps firms attract promising candidates:

Automate HR Affairs

Talent Acquisition encompasses a wide variety of tasks and when HR teams work in tandem with AI then many day-to-day tasks gets simplified. It helps with tasks like filtering and tracking application status of candidates, getting new hires onboard and making future decisions about employees by analyzing data of previous employees. Data enabled systems saves a lot of time and makes tedious tasks much easier.

Predictive Analytics for Better Hiring Decisions

Hiring professionals need 360 degree information about a particular situation in order to make the best decision possible. They need to analyze everything starting from the human capital requirement in the organization to the economics. Big data enables them to form a clear idea about the skill gaps in the company’s workforce, analyze current trends in the market, follow the financial KPI’s and demographic traits associated with hiring, set the hiring quota and identify the skills and talents to look for in new hires.

Discard ‘’Eleventh Hour’’ Hiring Method

The urgency to fill skill gaps often pressurizes HR professionals to make quick hires, which can be impulsive and not the best. With the help of predictive analytics, these last minute situations can be completely avoided. It allows HR teams to form long-term hiring strategies that align with company goals and also enables them to make timely hires. Using the power of big data, you can be aware of the future needs of your company and job market trends. Hence, it helps eliminate panic situations where you make a hire only to realize later that he/she doesn’t fit the bill.

Social Media for Insights

Big data helps firms attract the right candidates that fit a role. The hard data available on the social media platforms of promising candidates and their search behavior online give organizations crucial information that help them make right decisions. Talent Bin is one of the many employment websites that use information from social media to form insights.

Targeted Job Ads

With the help of analytics, companies can create target groups and rope them in by showing relevant ads. For example, if there’s a financial service provider who enjoys a large talent network interested in marketing on LinkedIn, then they can take this opportunity to post marketing-specific job advertisements. Many potential candidates might find these posts engaging and the company will find the right fit for the job.

Wrapping up, we can say that big data has opened up fresh avenues to make better hires. The influence of big data in every aspect of the modern corporate sector is truly astounding. The smartest candidates are enrolling for big data courses to build skills that sell the most in today’s world of work. For expert-guided big data Hadoop training in Gurgaon, visit DexLab Analytics.

 

Reference: insidebigdata.com/2018/07/20/big-data-talent-acquisition-effective-synergy-make-better-hires

 

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Big Data Analytics: 10 Data-Slurping Things Everyone Should Know

Big Data Analytics: 10 Data-Slurping Things Everyone Should Know

Big Data is no more a fleeting obsession. With numbers. It’s the beginning of a cognitive revolution that’s touching every facet of life and business on this planet.

Thanks to technological advancement, we’re churning more data than ever before. In fact, a lot more. And for good reason.

Every second we create new data – Don’t believe me?

Here are 10 mindboggling stats about big data – how it’s created, ways it’s being used and how much of it is still out there waiting to AMAZEEE us!!!

In short, this convinces us why we can’t afford to ignore big data and analytics:

Data volumes are exploding – the way it’s growing, by the end of 2020, it would generate 1.7 megabytes of new data every second for every human being living on this planet.

In a couple of years, our aggregated digital reservoir of data will expand from 4.4 zettabytes to approximately 44 zettabytes, or 44 trillion gigabytes.

Facebook users view 2.77 million videos and send 31.25 million messages, on an average every minute.

Whaaaat??!!!

Too much to process??

Wait, till you hear this!

Every minute up to 300 hours of video are being uploaded on YouTube, alone.

 Yes, while you are reading this blog, loads of users are already uploading chunks of content online.

For that reason or other, within the next 5 years, there will be more than 50 billion smart connected devices in the world, all powered by cutting edge data analysis technology.

Be ready to collect, analyze and share data without batting eyelids.

By 2020, one third of total data will roll over to the cloud (a concentrated network of servers all connected through the Internet)

Distributed Computing is the future. Google uses it involving 1000 computers to solve a single search query within 0.2 seconds. Woah!!!

Hadoop market is expected to grow at a compound annual growth rate 58% beating $1 billion mark by 2020.

The White House is heard to have invested more than $200 million in big data projects.

Now, the most spectacular fact is that less than 0.5% of all data is analyzed and used till now… So, just imagine the potential it withholds!!

In the next five years, big data is going to touch the moon, and what about you?! Don’t you feel like joining the data-inspired bandwagon?

Go, grab a quick Big Data Hadoop training in Gurgaon — and fill in oodles of knowledge, skill and expertise for improving your career graph and business performance. For more information on big data hadoop certification, drop by our expert website of DexLab Analytics.

The blog has been sourced from 

https://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#6a5cef7817b1

https://www.datasciencecentral.com/profiles/blogs/15-astonishing-tweetable-facts-about-analytics

 

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6 Mind-Blowing Facts on Big Data Everyone Must Know

6 Mind-Blowing Facts on Big Data Everyone Must Know


The hot topic in today’s business world is Big Data. The ability to access and analyze the massive amount of data generated every second is crucial for the growth of a business. In this blog, we highlight the rapid growth in data and its significance in business decisions through some incredible statistics.

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  1. The amount of information man created from the dawn of civilization until 2003 is currently created every two days!

    And the instant messages, tweets and pictures you exchange every second contributes to this data. Ex CEO of Google, Eric Schmidt wonders if the world is ready for the big data-driven technological revolution that is imminent.

  1. 5 quintillion bytes of data are generated by internet users on a daily basis.

    If the data generated in a day was burned onto DVDs, the number would be so massive that it could be piled on top of each other to reach the moon twice!

  1. Out of all the data we create, only about 0.5% is analyzed and put to use.

There’s a huge amount of data that remains untapped. For all the Silicon Valley big shots, like Google, LinkedIn and Facebook, the aim is to link big data with personal data and create products that are highly personalized.

  1. 40,000 search queries are processed by Google every second and these add up to 3.5 billion searches each day and 1.2 trillion searches every year globally.

Google was founded in 1998 and back then it was catering to only ten thousand search queries every day. However, you shall be astonished after knowing that since 2006 more than ten thousand search queries have been performed through Google per second!

  1. Big data has the potential to create 6 million jobs in the U.S.

    LinkedIn’s head of data recruiting, Sherry Shah, described the big data job market as being ‘’very hot right now’’.

  1. A 10% increase in the data accessibility for Fortune 1000 companies is likely to increase their income by $65 million!

    And this is exactly why companies care so much about big data.

The job market for big data and analytics looks promising. Especially if you are fresher skilled in big data Hadoop then there’s a lot of scope for you. Compared to the current demand for professionals with Hadoop training, the number of available candidates is low. So, what are you waiting for? Enroll for Hadoop training in Gurgaon and grab amazing discounts on big data certifications.

 

This article has been sourced from:

ediscovery.co/ediscoverydaily/electronic-discovery/date-fun-facts-big-data-ediscovery-trends

 

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How Can Big Data Tools Complement a Data Warehouse?

How Can Big Data Tools Complement a Data Warehouse?

Every person believes that he/she is above average. Businesses feel the same way about their best asset— data. They want to believe that their big data is above average and perfect for implementing advanced big data tools. But, that’s not the case always.

Do you really need big data tools?

In the data world, big data tools like Hadoop Spark and NoSQL are like freight trains delivering goods. Freight trains are powerful, but they’ve limited routes and a slow start. They are great for delivering goods in bulk regularly. However, if you need a swift delivery, freight train might not be the best choice.

So firs of all, it is important to understand if there’s a big data scenario in your business or not.

A 100 times increase in data velocity, volume or variety indicates that you have a big data situation at hand. For example, if data velocity increases to hundreds of thousands of transactions per hour from thousands of transactions, or if the data sources shoot up from dozens to hundreds, you can safely conclude that your business is dealing with big data.

In such scenarios, you are likely to get frustrated with traditional SQL tools. A complete revamp or moderate tuning of existing big data tools is needed to effectively handle such massive data sets.

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What tools to use?

The tool to be used depends on the task at hand. For main business outcomes like sales, payments, etc., traditional reporting tools employed within the data warehouse architecture are suitable. For secondary business outcomes like following the customer journey in detail, tracking browsing history and monitoring device activity, big data tools within data warehouse are necessary. In a data warehouse these events are aggregated into models that show the summarized business processes.

Incorporating Big Data Tools in Data Warehouse

Consider an alarm company with sensors that are connected though the internet across an entire country. Storing the response of individual sensors in a SQL data warehouse would incur huge expenses, but no value. An alternative storage solution is retaining this information in data lake environments that are cheaper and later aggregating them in a data warehouse. For example, the company could define sensor events that constitute a person locking up a house. A fact table recording departures and arrivals could be stoked up in a data warehouse as an aggregate event.

There are many other use cases. Some are given below:

Sum up and filter IoT data: A leading bed manufacturing company uses biometric sensors in their range of luxury mattresses. Apache Hadoop could be used to store individual sensor readings and Apache Spark can be employed to amass and filter signals. The aggregated data in data warehouses can be used to create time-trended reports once the boundary metrics are surpassed.

Merge real-time data with past data: Financial institutes need live access to market data. However, they also need to store that data and use it for identifying historical trends in future. Merging these two types of data with tools like Apache Kafka or Amazon Kinesis is important because, with these tools the data can be directly streamed to visualization tools and there’s hardly any delay.

The ultimate goal is to form a balance between the two sides of the data pipeline. While it is important to collect as much raw data about customers as possible, it is equally important to use the right tool for the right job.

To read more blogs on the latest developments in the field of big data, follow DexLab Analytics. We are a premier Hadoop training institute in Gurgaon. To aid your big data dreams, we have started a new admission drive #BigDataIngestion where we offer flat 10% discount to all students interested in our big data Hadoop courses. Enroll now!

 

Reference: https://tdwi.org/articles/2018/07/20/arch-all-5-use-cases-integrating-big-data-tools-with-data-warehouse.aspx

 

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