Category: Big Data

History of Big Data- A Technical Comedy

From the most primitive writing forms to the latest data centers, humans have loved gathering information. As technology progressed through the years, data overflow has become the new normal. This massive amount of data requires very sophisticated data storage systems.

As to why Big Data has had what we can call a comedy of sorts, we have taken a different approach and divided the history of Big Data into acts. We have done this in the form of a story, to help you connect and remember what you read for a long time.

Despite its existence for a long time, Big Data has been a topic of puzzlement for a lot of people. The biggest issue is the lack of people qualified enough to handle Big Data and utilize their practices.

Stay assured that this article will help you get into the deeper realms of Big Data and its world.

Big Data Technical Comedy Goodwork Labs


The History of Big Data



All kinds of analytics got done with the help of databases. Every imaginable type of data was present in databases. As a pattern, any of the data good enough for a database used to find itself in a flat file.

Google used to have a dislike for databases. They would have tried storing their data through databases, but it did not work. It would have been successful, but the company to make that happen definitely will have asked for too high a price. As a result, Google used files.

Google is a huge company, so they built an extensive, distributed file system. It was never surprising as it was Google, after all.

The company faced an issue, as there was a need for a query about the large files. The engineers and developers came together and devised a simple solution for a complex problem. This solution made the developers at Google geniuses in their own right. What is surprising is that they spread the news and told about it to everyone, while they made huge money with the devised solution.

The reason why they told everybody is still unclear. To increase the knowledge of people, or let others think as they did so that more people got good jobs. These are a couple of plausible reasons.

Or they knew that MapReduce was not supposed to work in the long run of time and wanted to send their competition in the wrong direction. A good enough third reason?



Whatever may have been the reason, the world got a shock, and when the world faces something entirely new, the level of interest breaks all known barriers to knowing about it a bit more.

This is when some people huddled together and implemented Google’s strategy in open source. Yahoo, the then competitor to Google, helped these people out by funding them to a considerable extent. This was the birth of Hadoop.

Now, the issue was that every company was not Google, and so, the presence of such a massive amount of data was not there. Many companies did not even have sufficient data to fill a MySQL database. Still, everybody liked MapReduce.

People thought about a lot of data and created Big Data. They thought about how a lot of data from the world can be collected, store it in an understandable format and bring about a change in the world.

This was when people also understood that scientists were handling Big Data for a long time, but referring to it as scientific computing. The world now had Big Data in literal terms.



It was apparent that Google was not very fond of databases, and some pointed out the reason to be SQL. But of course, making SQL work with big files was a little tricky. Now, this was what people love to call ‘a window of opportunity.’

Predictably, investors went into a frenzy and poured their money into Hadoop.



Soon after money got invested into Hadoop, Spark came to the fore, beating Hadoop by a large margin on performance. This was the time where Hadoop lost the race finally. The investor money was all put into Spark itself.

It is tough to understand where Hadoop ends and Spark begins. While both are MapReduce technologies, Spark came up to be the future. Because of Spark, the Machine Learning technology got a huge boost, and soon, the advent of data science takes shape.

Now Spark displayed inefficiencies while working with deep learning. Google and other companies did create new techniques, but it did not matter too much as most of the data was small.



This is the time when Big Data/AI/analytics got it all. A huge market, customers, use cases, and even investors. But there still is a difficulty. The difficulty to find out people for building such systems.

Moreover, the difficulty increases more when the architects to think it out and make systems workable are needed. Nobody is thinking about the usability yet, and problems like bias have started to creep up in operations.

Big companies with their rebranding as Big Data companies are not showing the growth expected. The chunk looks to be going in favor of the Blockchain.



There is AI, singularity, and even the robots are taking a dominant stand in our lives. Machines do threaten our presence at doing our jobs. Bots too are making their presence felt, but most of them break down pretty quickly.

However, there is still hope for Big Data. The work is being done for it already. Programmers are getting trained.

The number of people asking for analytics is increasing with each passing day. With more end to end stories and getting the usability right, Big Data can still work very successfully.

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When we talk about the food industry, we also know that it is the biggest and most important sector in the industrial world. The food and Beverage industry is increasing in scale at a high pace in terms of technology. With the addition of Big Data, however, the industry has reached a whole new level.

This new technology has permitted the food industry to improve at a breakneck capacity. Technology, with the added benefit of Big Data, has developed the procurement of insights from data. Not only from data, but also from the marketing campaigns and more interactive development to create an innovative product.

It is not wrong to say that Big Data has helped the food and beverage industry scale new heights.


Food and Beverage Industry, and Big Data


The food industry, under Big Data, is witnessing growth at a high pace.

In fact, as per a report by McKinsey, food retailers witnessed an improvement in their profits by almost 60% with the use of Big Data.

The F n B industry is getting more organized with real-time insights and taking note of many important points.

All this is made possible through Big Data, allowing companies to get plausible leverage for their services.

Even with Big Data though, there is one critical challenge-

The F n B industry today has a shallow degree of customer loyalty, making it more competitive and fragmented. The industry did not depend on the available data. Instead, they relied on a traditional reporting format.

However, the preferences of a customer are bound to change pretty regularly, making it very difficult to keep pace with them. This has led to a revolution of sorts in the food and beverage industry.

Big Data helps to analyze all the structured and unstructured data. This data comes either through modern sources or even traditional methods. Once collected, this data provides many insights for shopping trends, market development identification, and customer behavior.

Big Data analysis provides a competitive edge to the entire food and beverage industry. Many big names are taking advantage of Big Data to stay ahead of their competitors.


It is evident with the impact of Big Data on the food and beverage industry that there are several benefits on offer. With such a dynamic sector under focus, Big Data proves its mettle through the following benefits-


Customer demands today change with every passing second. This makes it difficult for the food and beverage industry to meet their expectations consistently. Big Data, however, can provide the data analysis required and provide insights on changing the behavior of customers.

Through the collected ideas, efforts to improve market efficiency get easily implemented.

With the development of technology online and on smartphones, customers now have a wide array of options to address their needs. The advancement has led to the food industry to collect the maximum data for their choices.

From the particular food items and change in their preferences to order value, there is data for everything today.

It is simpler than ever to grab on the customer information to help get potential value to businesses. With significant growth in this industry over the last decade, the total cost from mobile and online technologies have proven to be immensely useful.

The utility has not only been in monetary terms but also spans through the ease of collecting information to improve the marketing campaigns for companies drastically.


When it comes to the most technologically innovative area in the food and beverage industry, it has to be data analytics. As the industries become more and more focused on the customers, there has been a constant flow of ideas to improve data quality.

This data is used widely to modify product offerings and customer demand, as well. In such a scenario, data analytics has proven to be the core promoter of the food and beverage sector. Presently though, the efficiency and effectiveness of data are not suited well enough to achieve desired results.

The lack of this point has made it all the more important to innovate and open up new doors related to the subject area. Innovations will permit companies to get better insights for the benefit of brands and get help to manage their products.


The bonds of restraint will help you as a business to explore many new options through the help of Big Data. It is the perfect way to boost your sales and business efficiency.

The data-driven nature lends you the flexibility to go with a new trend, thanks to better analysis of data sales.

A better understanding of restaurants with their customers is made possible with better analytics and will improve the brand value of your company.

The improved practices in the food and beverage industry can have a lot of influence on the Big Data sector. An individual restaurant can understand its competition in a better place. Initially, it is going to take some time. But then you will start getting proper data while also tracking your competition’s growth.

You will have every opportunity to get a competitive edge with this improved method of marketing.


It is effortless to track purchase decisions through Big Data for wholesaling. If a product gets picked at an increased rate, you will get a lot of help to increase the sale of your business.

For instance, if the sale of a particular type of food on a discount in a region gets monitored, the data collected can be analyzed in terms of profit and an increase in the purchase of this specific product.

You will get a set of data to help you in setting the quality of the food and beverage offerings. With the help of this data, the sale and marketing plans can be executed efficiently by the companies for their products.


Big Data plays a crucial role in the overall quality of food and beverage. Companies in the sector can effortlessly control the quality of food supply through aggregate data. Customers expect to have the same taste and quality every time they go for a particular product.

Any difference negatively impacts their preference and brands end up losing their customers. In such situations, data collection is your best option. The data collection will regularly update you on the quality of food.


Big Data has eased restaurants and companies to develop more advanced forms of marketing to engage global customers. Adding to that, companies can use the various social media platforms already in use by a huge number of people.

Their reviews and testimonies have the potential to take your business to a whole new level.

Need Big Data solutions for your Food & Beverage business? Get in touch for tailored Big Data solutions for your business at affordable prices.









Interesting Facts About 2019 Elections And The New Age Technology

India’s most anticipated events of 2019 — General Elections of Lok Sabha is right here.


From political campaigning to social good, AI seems to have been actively used for data prediction & accuracy. On the other hand, New Zealand which will be hosting the election for Prime Minister in the year 2020. For this very election, Sam is the frontrunner. He has the right amount of knowledge on education, policy, and immigration and answers all related questions with ease. Sam also is pretty active on social media and responds to messages very quickly. When it comes to being compared with the other politicians; however, there is one huge difference- Sam is an AI-powered politician.




Sam is the world’s first Artificial Intelligence (AI) enabled politician developed by Nick Gerritsen, an entrepreneur driven by the motive to have a politician who is unbiased and does not create an opinion based on emotions, gender and culture.

This is just one of the many instances where AI is playing an increasingly crucial role in politics all over the globe. Political campaigns have been taking the help of AI for quite a long time now.


The most significant advantage of AI in politics can is its ability where it can accurately predict the future. Political campaigns make the use of machine learning, social media bots and even big data to influence the voters and make them vote for their political party.
Apart from just wins and losses on the political front, AI presents with more obvious implications in decisions and policy making. Reports claim that deep learning, an essential aspect of AI, can look after issues that relate to executing the schemes laid down by the government.

The technologies that use AI for social good are also on the rise since some time now. This is why the arrival of AI politicians is not very surprising. As to how big data and deep learning help it all out, we will be discussing it further below.


With such a flurry of content on all social media platforms, it is understandable to get confused in determining which political leader is going to have the best interests of the nation at heart. You will be surprised to know that the leaders know how you think and also what you expect from them. Elections have a lot to do with psychology other than just indulging in political games.
While going through the Internet or mobile apps, you must have noticed that there is a pattern to the kind of videos which pop on your window. Some of these pop-ups are also related to the elections and candidates located within your vicinity. This pattern is backed up by reason.

The Lok Sabha election of 2019 may or may not play a decisive role in creating a bright future of India, but it is a witness to the fact that the use of technology is driving the people to act in a certain kind of way. It essentially is India’s big data election which is underway through several algorithms, analytics, and obviously, Artificial Intelligence.

Though they are not exactly visible in the election, they are more of the channels which are always present when it comes to tracing the online actions of voters, political messaging, customizing the campaigns and create advertisements targeted at the voters.

The Congress political party has provided all its candidates with a data docket which can track on-ground activities by their Ghar Ghar Congress app. The data dockets have information regarding households, missing voters, new voters, and even the local issues which plague the concerned constituency.

At the other end, the BJP looks far ahead in its quest to appeal the citizens to keep their party in power for another tenure. In states of the North, the party is a host to more than 25,000 WhatsApp groups. Ironically though, by the time Congress thought to compete with it, WhatsApp changed their policies, leaving the Opposition out to dry.

The optimal use of neural-network techniques, more often referred to as deep learning allows the political parties to have an unbeatable ability and have a fact-based study as to how such kind of data.

We at GoodWorkLabs are enthusiastic about creating such offbeat solutions using our expertise in AI, ML, Big Data, RPA. If you’ve any requirement which is this interesting & complex in nature, drop us a line and let us help you with a robust solution.

Why Cloud Computing is the future of enterprise application platform?

Cloud computing to store and manage the Big Data


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




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

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

Cloud Computing and Big Data

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

Why Do You Need To SwitchOver To Cloud Computing?

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

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

Anyone Can Achieve Big Data Analytics through Cloud Computing

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

The Shortcomings of Cloud Computing

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

Cloud Computing and Data Analytics

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


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

How to convert Customer Interactions into opportunities with Big Data

Big Data for Customer Success

If you have stumbled upon this article it means that you are curious about Big Data and its credibility into businesses. Well, the good thing is you hit the right blog post.

Technology is continuously changing how customers get in touch with brands. The customers today demand an experience which is nothing short of a great one. With the help of the internet, phones and even emails- people today are more informed than ever in the digital age.

It is more convenient than ever to quickly research a company and the products that are on offer by browsing and social media. It is also important to note that bad customer experiences spread more quickly to tarnish the image of a brand. A negative image also makes it difficult for companies to compete in this cluttered environment.

Customers are a more significant force than ever in determining the success of any business. Most executives agree that companies that succeed in delivering a great customer experience are ahead as they have a competitive advantage.

Big Data for customer success

Big Data for Customer Experience

Big Data is the key to ensure a great customer experience for most of the companies. The impact you can have on your customers by being accurate about their behaviors across many touch points is unimaginable. To get to this point, however, a lot of understanding of past, present and future trends is required in the context of consumer behavior, which in turn improves their experience.

Companies have access to a lot of internal and external data of customers. But it has been difficult to interpret the quality of such data. The speed at which this data accumulates through social media, web, and sensors often beats the rate by which businesses absorb it for their operations by data intelligence operations.

Big Data analytics provides a way where insights can be used across the customer experience life cycle to assist businesses in a better understanding of customer segmentation, profitability and the lifetime value of customer experience.

The feature to collect and analyze a massive amount of structured and unstructured data by many sources gives a better look in the behavior and needs of customers. Even specific insights tend to be more powerful like the “next likely purchase” or “next best action” for fields of marketing and customer support respectively.

A lot of companies are closer than ever in getting a full understanding of their customers, which is aided by experimentation which is no longer just stuck in theory. Such companies are seeking to use new analytical tools and methods for testing and enhancing the customer experience in every aspect of an organization.

The use of Big Data to customize customer offers has a direct impact on converting new and existing customers. Tailoring content which offers an insight to customer behavior, profile and their preferences can mostly help marketing teams of companies lead the way for customer experience and boost sales.


Know more, Sell more.

As mentioned above, developing a comprehensive view of the customer involves getting as many interactions as possible from a company’s primary system such as systems that support sales, marketing, social media, and others. The next step is to build efficient analytical models to find out relationships hidden within the data.

Once marketers combine traditional database modeling methods with unstructured data, they will get a better understanding of a customer’s intentions. Bringing them together though is a challenge.

Through an examination of both types of data in a non-relational environment, forming and testing hypotheses becomes easier for the companies. It results in newer insights which could have been easily missed. The approach brings adjustments to present processes to get better results.

A common way to integrate unstructured and structured data to make it more accessible for analysis is to merge an existing data warehouse with a platform like Hadoop. The platform supports a relational database as it can store and process a massive amount of non-relational data. It helps companies to create active data archives which make both the structured and unstructured data much more accessible and valuable for a company. In this way, companies can look for new insights and get a competitive advantage.

With more accessible data, teams can take the help of a solution like Oracle Big Data which is powered by Xeon processors to produce sophisticated statistical models that lead a more streamlined segmentation and targeting based on real interests, activities, and behaviors.

Once the insights get captured, it is also imperative to organize them on dashboards which help with the decision making. Oracle Business Intelligence Analytic applications consist of more than 80 industrial segments and more than 800 metrics to assist in fast and regular business intelligence reporting as well as in creating dashboards.

To boost the greater adoption of big data, companies are also looking at applications that feature technology where the database is present inside the memory of the applications itself. They enable quick Google-like searches and make it very easy to understand Big Data by heat map views of customer activity on mobile devices.



Companies and businesses are looking for new ways to enhance customer experience and take in maximum benefits from every single interaction. It is also understandable that getting hold of this value needs better insight and better decision-making as well. Oracle gives many flexible analytical tools which help data scientists use their expertise to make more critically important decisions.

The solutions, both relational and non-relational assist companies derive maximum value from the quickly changing sources of customer data. Companies not only gain more insights from data through such solutions but also drive intelligence at a good pace at the point of impact. To ensure success, companies need to either eliminate or compress the time to a great extent which is lapsed from the data acquisition to the analysis of actions based on these insights.

Thus, Big Data is the edge your business needs to succeed. Let us assist you with customized solutions for your business.

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How Big Data can help with Disaster Management

Big Data applications in Disaster Management

Take out a page from history, and you will find that all those numerous policies have not been effective when it comes to rescuing people who are in the middle of a horrifying disaster. As innovations are constantly evolving, it’s time that administrations should focus more to include various Big Data technologies to help in the prediction of disasters and their relief work.

Great innovations like the Internet of Things (IoT) have become more regular today, which was not the case two decades ago. With the frequency of natural disasters increasing, the advancement in ways of communication through this technology has led to a considerable reduction in the number of casualties as well as injuries.

Agencies like NASA and National Oceanic and Atmospheric Administration (NOAA) have used big data technologies for the prediction of these natural disasters and then coordinate with the response personnel in cases of emergency. This technology has also been necessary for the agencies to shortlist a typical disaster response by taking down the locations of staging a rescue location and evacuation routes.

Also, agencies around the storm impact zone use the machine learning algorithms to have an idea about disasters like storms and floods, and the potential damage they could cause.

Big data in disaster management


Big Data and Disaster Management

Big Data technology is a great resource that has been continuously proving its mettle in disaster relief, preparation, and prevention. Big Data helps the response agencies by identifying and tracking populations such as elder groups of people, regions where there is a large concentration of children and infants etc.

Big Data systems help in the purpose of coordinating with the rescue workers to identify the resources which could provide support and do some logistic planning in such emergency cases. The facilitation of real-time communication is also an added advantage in disasters because the use of this technology can forecast the reactions of citizens who will be affected.

Big data systems are now in the stage of growth with an acceleration rate with studies saying that 90% of data in the world was generated within the previous two years, which is simply huge. All this data helps the manager of emergency units make better-informed decisions at the time of a natural disaster.

The reports that are generated consistently prove to be a massive benefit for disaster response management by combining the data used for mapping geographical records and imagery that is real-time. They also give responders information regarding the status in affected areas, providing them a constant stream of real-time data in cases of scenarios which have emergency written all over them.


Benefits of Big Data

Big Data technologies are undoubtedly an important aspect to tackle natural disasters and make emergency responses very efficient.

However, there are a few broad benefits that are explained below with appropriate instances.

  • Crisis Mapping

Nairobi’s non-profit data analysis community known as the Ushahidi, created an open-source platform of software to gather information. This technology works on a mapping platform which was first developed in the year 2008, analyzing the areas that became violent right after the Kenyan presidential elections.

Information at that particular time came through social media and many eyewitnesses. Their team members then put up the same information on a Google map that was interactive, helping the residents get cleared of danger.

The same technology was used again in the year 2010 when Haiti was jolted through an earthquake, proving integral in saving the lives of numerous citizens who were there in the region.


  • Bringing loved ones and families closer

Facebook and Google are genuinely the present leaders in technology, and they too have invested in the development of some advanced resources which have their benefits during the time of natural disasters. Huge online systems have been deployed by them which enable the members of a family to connect again after separation in times of emergency.

The “Person Finder” application by Google was released right after the Haiti earthquake for helping people connect with their family members. The platform works on the function of people entering information about the missing persons and also reconnect with them at the time of a disaster.


  • Prepare for emergency situations

Systems working on Big Data are continually making it better for the agencies to predict or forecast when a particular disaster can happen. The agencies work to ensure a combination of data collection, notification platforms and scenario modeling in forming great disaster management systems.

The residents give out household information which agencies use for the evaluation and allocation of resources at the time of natural disasters. For example, these citizens share information that can be lifesaving, such as the presence of family members that have physical problems inside the household.

The United States is in constant need of scientists who could work with the technologies that can help predict and save lives during a natural disaster. 

A considerable portion of company leaders is of the opinion that a shortage in the number of data scientists is making it pretty tricky for their enterprises for surviving a marketplace which is highly competitive. As the apparent result, firms that succeed in getting good IT people to perform much better due to sheer talent as compared to their rivals.

If the analysis of forecasters is to be believed, the companies in the United States will be creating close to around 500,000 jobs for data scientists who are very talented by the year 2020. The current pool of these scientists, however, points out the availability of only 200,000 of such scientists presently. It can just be good news as it provides new opportunities for all aspiring data scientists in the future.

How Kubernetes Can Help Big Data Applications

Kubernetes in Big Data

Every organization would love to operate in an environment that is simple and free of clutter, as opposed to one that is all lined up with confusion and chaos. However, things in life are never a piece of cake. What you think and want rarely lives up to your choices, and this is also applicable to large companies that churn a massive amount of data every single day.

This is the point. Data governs the era we all live in. It is these data piles that prove to be a burden to a peaceful working process in companies. Every new day, an incredible amount of streaming and transactional data gets into enterprises. No matter how cumbersome it all may be, this data needs to be collected, interpreted, shared and worked on.

Technologies which are assisted by cloud computing offer an unmatchable scale, and also proclaim to be the providers of increased speed. Both of them are very crucial today especially when things are becoming more data sensitive every single day. These cloud-based technologies have brought us to a critical point that can have a long term effect on the ways which we use to take care of enterprise data.

Kubernetes in Big Data

Why Kubernetes?

Known for an excellent orchestration framework, Kubernetes has in recent times become the best platform for container orchestration to help the teams of data engineering. Kubernetes has been widely adopted during the last year or so when it comes to the processing of big data. Enterprises are already utilizing Kubernetes for different kinds of workloads. 

Contemporary applications and micro-services are the two places where Kubernetes has indeed made its presence felt strongly. Moreover, if the present trends are anything to go by, micro-services which are containerized and run on Kubernetes have the future in their hands.

Data workloads which work on the reliance of Kubernetes have a lot of advantages when compared to the machine based data workloads-

  • Superior utilization of cluster resources
  • Better portability between on-premises and cloud
  • Instant upgrades that are selective and simple
  • Quicker cycles of development and deployment
  • A single, unified interface for all kinds of workloads


How Big Data entered the Enterprise Data Centers

To have an idea about the statement above, we need to revisit the days of Hadoop.

When Hadoop was first introduced to the world, one thing soon became evident. It was not capable enough to manage the emerging data sources effectively and the needs of real-time analytics. The primary motive for building Hadoop was to enable batch-processing. This shortcoming of Hadoop was taken care of with the introduction of analytics networks like Spark.

The ever-increasing ecosystem did take care of a lot of significant data needs but also played an essential role in creating chaos in the outcome. A lot of applications that worked with analytics tended to be very volatile and did not follow the rules of traditional uses. Consequently, data analytics applications were kept separately from other enterprise applications.

However, this is the time we can surely say that things headed in the right direction where cloud-native technologies that are open sourced like Kubernetes, prove to be a robust platform to manage both the applications as well as data. Also, explanations are under development which helps to allow the workloads of analytics to run on IT infrastructures which are containerized or virtualized.

During the days of Hadoop, it was data locality which acted as a formula that worked. The data was made available for distribution and then close for computation. In today’s scenario, storage is getting decoupled by computer. From the distribution of data to the delivery of access, the merging of these data analytics workloads and on-demand clusters based on Kubernetes is also on us.

Shared storage repositories are vital for managing workload isolation, providing speed, and enabling the prohibition of data duplication. This helps the teams leading analytics in setting up elaborate customized clusters which meet their requirements without recreating or moving larger sets of data.

Also, data managers and developers can raise queries to structured and unstructured data sources without the assistance of costly and chaotic data movement. The time taken for development gets accelerated, helping the products to enter into markets quickly. This efficiency which brought through a distributed access in a shared repository for storage will result in lesser costs and thorough utilization.


Unlocking Innovations through Data

With the use of a shared data context for isolation of multi-tenant workloads, the data is unlocked and easy to access by anybody who wishes to utilize it. The data engineers can also variably provide these clusters with the right set of resources and data. Teams on data platforms can strive for achieving consistency among multiple groups of analytics, while groups for IT infrastructure can be provided access to the clusters to use in the overall foundations which so far is being used for different traditional kinds of workloads as well.

Applications and data are ultimately getting merged to become one again, leading to the creation of a comprehensive and standardized source to manage both on the same infrastructural level. While this entire process might have used up a few years, today we have finally succeeded in ushering an era where companies can successfully deploy a single infrastructure for the management of big data and many other needed and related resources.

This is possible only because of open-source technologies, which are also based on a cloud system. There is no doubt that such techniques will continue to pave the way ahead, acting as a stepping stone for the evolution of more advanced and concise technologies in the future to come.


How Data Analytics can Grow Your Retail Business by 10X

The Importance of Retail Data Analytics

A correct set of retail data is sufficient to figure out if a customer is going to purchase from a store or will visit once again. Whenever folks go out shopping, they want to have an experience which is convenient, informative, and personalized. A retailer cannot provide a personalized experience if he does not have access to retail data analytics technology for obtaining information about particular shoppers and end users.

Data Analytics Solutions for Retail


As compared to traditional retailers, e-commerce retailers find it easier to track and obtain information related to individual shoppers. This is because web technology makes it very easy for these e-tailers to gather data regarding customer purchases.

Web technology is also able to track the device through which customers access the website. What a customer went searching for from the showcased items to the time he spent on the site, every minute detail can be tracked. All this helps to understand customer persona and preferences at a deeper level.

With all this data, e-commerce websites are then able to deliver targeted emails, advertisements and personalized deals for customers, thus luring the customer to check out and make a purchase.

Access to such retail analytics helps in two things-

1) increases customer stickiness on the website with a personalized shopping experience and

2) increased sales from related items based on previous customer purchasing data

Video streaming services like Netflix are great examples where algorithms related to recommended videos drive more user engagement. All these benefits of data analytics are complicated to be derived from a physical store.

In short, the absence of data analytics technology can impact the physical store retailers negatively as they may not be able to provide a great experience to customers.


The use of Beacon Technology in Retail Analytics 

Store beacons are a piece of retail analytics technology which is becoming more and more popular in retail stores all over, mainly because of the multiple uses these beacons provide to the retailers. For instance, the tags can figure out those parts of your store that are busiest with the maximum number of customers.

This is a small detail, but it can help the retailers to a great extent. Such information can help store owners adjust and modify their display with products which they want the customers to be aware of. Promotional goods, for example, can be placed in areas where the traffic of customers is high usually.

The data collected through these beacons can also be used to monitor the traffic at particular hours of the day. A retailer with such a piece of information can plan out the allocation of his workforce and also the best position inside the store to place his items. 

These beacons can also be well-integrated into the mobile phones of shoppers. Once connected, customers can be sent welcome messages. Not only that, even reminders to purchase some particular items or any promotional offers available can be sent to these customers through the in-store beacons.

We are still not done. The beacons also provide help to trace the physical path through which a customer roams in the store while making his purchases. This is again a useful function as it helps to obtain and analyze specific shopping behaviors of particular customers while visiting a retail store. Walmart and Amazon Go stores have already been utilizing this piece of technology to yield significant benefits.

Amazon Go, in particular, has tweaked the use of beacon technology. Because these stores operate in a cashless manner, every customer gets a barcode scanned while entering the store. This barcode is on the Amazon Go app in the visiting shopper’s mobile phone. The customers then shop for their items and leave the store. A countless number of cameras inside the store track customers and their behavior.

It analyses all information such as what items they picked up, what they finally bought, and the items which they left behind, all of this os crucial information which gets recorded with the help of these cameras. Sophisticated computer systems take the data from the weight sensors to evaluate which products have been removed from a rack.

The moment a particular customer leaves the store, they are automatically charged for all the products which they have purchased. This is the closest a retail store can come to an e-commerce website, all of which is possible with the right technology.


Omnichannel Experience with Retail Data Analytics

If new retail analytics and reporting technology are merged cohesively, a considerable amount of data can easily be collected. This data will be capable enough to monitor every little piece of detail related to the behavior and buying preferences of customers. All of this data can be obtained from just one visit from a consumer.

Thus, with retail data analytics, stores can create an omnichannel shopping experience for customers. With smart stores, customers can have easy and cashless checkouts. Amazon Go is a great example. It gives customers the same online experience with its high-tech offline store.


Challenges with the adoption of Retail  Data Analytics

A lot of innovative products are there to help retailers who wish to have a better understanding of the customers visiting their stores. The issue is that only huge companies have financial resources where they can test and put the new technology-driven solutions to use.

The progress of these latest tools for retail analytics and reporting technology is pretty slow. What cannot be denied though, is the fact that technology is essential for any retailer today. If a customer does not receive an experience which is according to his expectations, no retailer will find the customer coming back to his doorstep.

Retailers should consider technology as an investment through which they can offer customized options according to customers’ preferences. Brick and mortar stores need to have the best retail technology possible without which retailers will not be able to provide the desired customer experience. 

If you are looking for data analytics solutions for your business, then GoodWorkLabs can help! Send us a short message with your requirements and our data analytics team can help you with a free consultation on the best data analytics solution for your business.

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6 trending Big Data Technologies for your Business

Big Data Technologies and Tools

An organization is all about the data it beholds and to make a decision that is valid for years, a massive amount of data is required. This brings us to today’s topic ‘how to handle data influx with Big Data‘ and what are pointers that you should know about Big Data.

Power of the Big Data can be used to elevate the business to new levels and capture market opportunities. Big Data is the term which is used for massive data. As the data inputs are received from a variety of sources, it is diverse, massive, and beyond the capacity of the conventional technologies.

Such quantum of data requires computationally advanced skills and infrastructure to handle it. Once equipped with the appropriate infrastructure the data must be analyzed for patterns and trends. Such trends and patterns aid in formulating marketing campaigns.

big data technologies and tools


Following are some industries that are already ahead in leveraging Big Data for regular operations:

  • Government organizations trace social media insights to get the onset or outbreak of a new disease.
  • Oil and gas companies fit drilling equipment with sensors to assure safe and productive drilling.
  • Retailers use Big Data to track web clicks for identifying the behavioral trends to adjust their ad campaigns.

Below we have listed few Big Data Technologies and big data tools that ought to be aware of

1. Predictive analytics

This technology helps you to discover, assess, optimize, and deploy predictive models, which will improve business performance by moderating business risks.

2. Stream analytics

Stream analytics analyzes the varied data in different formats that come from disparate, multiple, and live data sources. This method helps to aggregate, enrich, filter, and analyze a high throughput of data on a regular basis.

3. NoSQL database

NoSQL database is having an exponential growth curve in comparison to its RDBMS counterparts. This database offers increased customization potential, dynamic schema design, scalability, and flexibility which is a must for storing Big data.

4. In-memory data fabric

This technology lets you process data in bulk and provides low-latency access. Also, it distributes data across SSD, Flash, or dynamic random access memory (DRAM) of a distributed computer system.

5. Data Virtualization

If you require real-time or near real-time analytics to be delivered from various big data sources such as Hadoop and distributed data sources, data virtualization is your best way out.

6. Data integration

Data integration includes tools that enable data orchestration across solutions such as Apache Pig,  Apache Hive, Amazon Elastic Map Reduce (EMR), Couchebase, Hadoop, MongoDB, Apache Spark, etc.

These tools are discussed in detail for you to understand below:

a) Apache Spark

Apache Spark is the fastest and general engine for Big Data processing. It has built-in modules for SQL support, graph processing, streaming, and machine learning. It supports all major Big Data languages including Java, Python, R, and Scala.

The main issue with data processing is the speed. A tool is required to reduce the waiting time between the queries and time taken to run the program. Apache Spark complements to computational computing software process of Hadoop but it is not the extension of the latter. In fact, spark uses Hadoop for storage and processing only.

It has found its utilization in industries which aim to track fraudulent transactions in real time like Financial institutions, e-commerce industry, and healthcare.


b) Apache Flink

Apache Flink was introduced by Professor Volker Markl- Technische University, Germany. Flink is a community-driven open source framework which is known for accurate data streaming and high performance.

Flink is inspired by MPP database technology for functioning like Query Optimizer,  Declaratives, Parallel in-memory, out-of-core algorithms, and Hadoop MapReduce technology for functions like User Defined functions, Massive scale out,  and Schema on Reading.


c) NiFi

NiFi is a powerful and scalable tool with the capacity to process and store data from a variety of sources with minimal coding. Also, it can easily automate the data flow between different systems.

NiFi is used for data extraction and filtering data. Being an NSA project, NiFi is commendable in its performance.


d) Apache Kafka

Kafka is a great glue between various systems from NiFi, Spark, to third-party tools. It enables the data streams to be handled efficiently and in real time. Apache Kafka is an open source, fault-tolerant,  horizontally scalable, extremely fast and safe option.

In the beginning, Kafka was a distributed messaging system built initially at LinkedIn, but today it is part of the Apache Software Foundation and is used by thousands of known companies including Pinterest.


e) Apache Samza

The main purpose to design the Apache Samza is to increase the capabilities of Kafka and is integrated with the features like Durable messaging, Fault Tolerant,  Managed State, Simple API, Processor Isolation, Extensibility, and Scalability.

It uses Kafka for messaging and Apache Hadoop YARN for fault tolerance. Thus, it is a distributed stream processing framework which comes with a pluggable API to run Samza with other messaging systems.


f) Cloud Dataflow

With a simple programming model for both batch-based and streaming data processing tasks, Cloud Dataflow is a native Google cloud data processing integrated service.

This tool cuts your worries about operational tasks including resource management and performance optimization. With its fully managed service, resources can be dynamically provisioned to maintain high utilization efficiency while minimizing latency.


Final Words

All of these tools contribute to real-time, predictive, and integrated insights which are exactly what big data customers want now. For gaining a competitive edge with big data technologies, one needs to infuse analytics everywhere, develop a speed differentiator, and exploit value in all types of data.

For doing all this, an infrastructure is required to manage and process massive volumes of structured and unstructured data. Thus, data engineers require the above mentioned tools to set patterns for data and help data scientists examine these huge data sets.

11 must-have skills to build a career in Data Science

How to build a career in Data Science

Today, data scientists are one among the highest paid professionals. Technology is soon advancing and it is necessary that you constantly pay attention to upgrade your skills and expertise.

Tech giants such as Google, Facebook, Apple etc, all of them are looking for data science experts to build intelligent and path-breaking products.

If you are planning to become a data scientist, then you need to be well-versed in some programming languages. In this blog, we list the top 11 skills that you must possess to become a successful data scientist.

career in data science

1. Education

Data scientists are usually from the highly educated crowd in the college. As a matter of fact, 46% of them have PhDs while 88% of them have a Master’s degree.

You could be from any stream like social science, physical science, computer science,  or statistics in order to be a data scientist. The common field of studies are as follows:

  • Mathematics and Statistics (32%),
  • Computer Science (19%)
  • Engineering (16%).

A degree course in the above fields helps you to develop skills you need to analyze big data. It is highly recommended to obtain a Master’s or Ph.D. after successful completion of the Bachelor’s program. To transit into the data science field, you will require to pursue your master’s degree in Mathematics, Data Science, Astrophysics or any such related field.


2. R Programming

R programming is specially designed for data science needs. Any problem in the field of data science can be solved with R. Currently, 43% of data scientists use R to solve statistical problems. Therefore, it is recommended to learn R.

However, R is tricky to learn especially if you have already mastered a programming language. An online learning program should be taken up to learn R.

3. Python Coding

Along with Java, C/C++, Perl, Python is the most common coding language and is perfect for data scientists. Around 40% of the data scientists use Python as their major programming language. Python is a versatile language and can be used in almost all the steps of the data science processes.

With Python, you can easily import SQL tables into your code and also process various forms of data. Further, it allows you to create your own datasets.

4. Hadoop Platform

This is not a pressing requirement but it is highly preferred in many cases. Also, if you have experience with Pig or Hive or familiarity with cloud tools such as Amazon S3, you will be preferred over other applicants.

Why Hadoop platform is important?

There might be a situation when the volume of data to be processed exceeds your system’s memory and you will require to send data to different servers. In such a situation, you can use Hadoop to transfer your data to various points. Also, Hadoop can be used in data sampling, data exploration, data filtration, and summarization.

5. Apache Spark

Apache Spark is faster than Hadoop with the same big data computation framework. The reason why Apache spark is faster than Hadoop is that Spark caches the computations in memory while Hadoop reads and writes to disk.

Apache Spark helps data scientists to handle complex unstructured data sets and saves time by processing the data faster. It can be used on one machine or a bunch of machines, at once.


6. SQL Database/Coding

SQL stands for Structured Query Language. SQL is a programming language which enables you to carry out operations like delete, add, and extract data from a database. Also, it helps in transforming database structures and carrying out analytical functions.

For becoming a successful scientist, you need to be proficient in SQL. SQL will help you to access, communicate and also work on data. It has brief commands that can help you lessen the amount of programming you need to perform. Additionally, it will help you comprehend relational databases and boost your experience profile.


7. Data Visualization

For a data scientist, it is essential to visualize data to make it easier to understand. This can be done with data visualization tools such as d3.js, Tableau, ggplot, and Matplottlib. These tools can convert data into easy formats.

Data visualization is the need of the contemporary corporate world because of the insights delivered. These insights indicate which business opportunities to grab and how to stay ahead of the competition.


8. Machine Learning and AI

Machine Learning can give you an edge over others as with this you can transform the way data science is functioning. Most data scientists are not proficient in this field. To stand ahead of others, you must learn decision tree, supervised machine learning, logistic regression, etc. Read here for more information on which Machine Learning Algorithm to pick. 

A proficiency in Machine Learning helps you in solving complex data science problems that are based on predictions.

Other examples of advanced machine learning skills that you should consider are Unsupervised machine learning, Natural language processing, Outlier detection, Time series, Recommendation engines, Survival analysis, Reinforcement learning, Computer vision, and Adversarial learning.


9. Unstructured data

A data scientist must essentially be able to work with unstructured data. Basically, the unstructured data are undefined content that can not be put into database tables.

For instance, customer reviews, videos, blog posts,  video feeds, social media posts, audio etc. Such heavy data is difficult to sort because they have no order.

Unstructured data is also referred to as ‘dark analytics’ because of its complex nature. Ability to comprehend and discern unstructured data from several platforms is the prime attribute of a data scientist. It helps you interpret the insights that are useful for decision making.

Apart from the above mentioned technical skills, following non-technical skills will help you to achieve your goals faster.


10. Intellectual curiosity

Curiosity provides you with the thirst to learn something new every day. As a data scientist, you will counter new problems every now and then, at this moment, curiosity will motivate you to find solutions to your problems.

On average, data scientists spend about 80% time in discovering and preparing data. In order to keep pace with the evolving world of data science, you need to keep learning.


11. Communication skills

Data scientists make complex data understandable for normal people which is why it is essential for them to have smooth communication skills. With fluent communication skills, they will be able to explain their technical findings to non-technical teams such as Sales or marketing department.

Thus, with these 11 skills, you will be able to launch your career as a Data Scientist. Even if you are someone who is planning to shift technologies, just spend some time to learn programming languages such as R, Python and the Apache suite and you will be in a good position to start off a career in data science.


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