The importance of Big Data doesn't depend only on how much data you have, it's rather what you are going to do with the data. Data can be sourced and analyzed from unpredictable sources and can be used to address many things. Let's see use cases with real-life importance made on renowned scenarios with the help of Big Data.

The following image helps us understand a Big Data solution serving various industries. Though it's not an extensive list of industries where Big Data has been playing a prominent role in business decisions, let's discuss a few of the industries:

Social media content is information, and so are engagements such as views, likes, demographics, shares, follows, unique visitors, comments, and downloads. So, in regards to social media and Big Data, they are interrelated. At the end of the day, what matters is how your social media-related efforts contribute to business.

One notable example of Big Data possibilities on Facebook is providing insights about consumers lifestyles, search patterns, likes, demographics, purchasing habits, and so on. Facebook stores around 100PBs of data and piles up 500TB of data almost daily. Considering the number of subscribers and data collected, it is expected to be more than 60 zettabytes in the next three years. The more data you have, the more analysis you can have with sophisticated precision approaches for better Return on Investment (ROI). Information fetched from social media is also leveraged when targeting audiences for attractive and profitable ads.

Facebook has a service called Graph Search, which can help you do advanced searches with multiple criteria. For instance, you can search for people of male gender living in Ahmedabad who work with KNOWARTH Technologies. Google also helps you refine the search. Such searches and filters are not limited to these; it might also contain school, political views, age, and name. In the same way, you can also try for hotels, photos, songs, and more. So here, you have the business ROI of the Facebook company, which provides Facebook ad services which can be based on specific criteria such as regions, interests, or other specific features of user data. Google also provides a similar platform called Google AdWords.

The era of Big Data has been playing a significant role in politics too; political parties have been using various sources of data to target voters and better their election campaigns. Big Data analytics also made a significant contribution to the 2012 re-election of Barack Obama by enhancing engagement and speaking about the precise things that were significant for voters.

Narendra Modi is considered one of the most technology and social media-savvy politicians in the world! He has almost 500 million views on Google+, 30 million followers on Twitter, and 35 million likes on Facebook! Narendra Modi belongs to the Bhartiya Janta Party (BJP); Big Data analysis carried major responsibility for the BJP party and its associates for their successful Indian General Election in 2014, using open source tools that helped them get in direct touch with their voters. BJP reached their fluctuating voters and negative voters too, as they kept monitoring social media conversations and accordingly sent messages and used tactics to improve their vision for the election campaign.

Narendra Modi made a statement about prioritizing toilets before temples seven months earlier, after which the digital team closely monitored social media conversations around this. It was noticed that at least 50% of users were in line with the statement. This was when the opportunity to win the hearts of voters was converted to the mission of Swacch Bharat, which means hygienic India. The results were astonishing; BJP party support rose to around 30% in merely 50 hours.

Did you know that with the help of Big Data, human genome decoding, which actually took 10 years to process, is now decoded in hardly a day, and there is almost a 100 times reduction in cost predicted by Moore's Law? Back in the year 2000, when the Sloan Digital Sky Survey (SDSS) started gathering astronomical data, it was with a rate of around 200 GB per night, which, at that time, was much higher than the data collected in astronomy history.

National Aeronautics and Space Administration (NASA) uses Big Data extensively considering the huge amount of science and research done. NASA gathers data from across the solar system to reveal unknown information about the universe; its massive collection of data is a prominent asset for science and research, and has been a benefit to humankind in diverse ways. The way NASA fetches data, stores it, and uses it in effective ways is enormous. There are so many use cases of NASA that it would be difficult to elaborate here!

One of the leading energy management companies that helps improve energy consumption with the help of Big Data predictive analysis, which helps build stronger relationships and retaining of customers. This company connects with more than 150 utilities and serves more than 35 million household customers to improve energy usage and reduce costs and carbon emissions. It also provides analytical reports to utility providers, from more than 10 million data points each day, for a holistic overview of usage for analysis. Household customers get these reports in invoices, which provide areas where energy usage can be reduced and directly helps consumers optimize energy costs.

When it comes to security, fraud detection, or compliance, then Big Data is your soulmate, and precisely if your soulmate helps you in identifying and preventing issues before they strike, then it becomes a sweet spot for business. Most of the time, fraud detection happens a long time after the fraud has happened, when you might have already been damaged. The next steps would be obviously to minimize the impact and improve areas that could help you prevent this from being repeated.

Many companies who are into any type of transaction processing or claims are using fraud detection techniques extensively. Big Data platforms help them analyze transactions, claims, and so on in real-time, along with trends or anomalous behavior to prevent fraudulent activities.

The National Security Agency (NSA) also does Big Data analytics to foil terrorist plans. With the help of advanced Big Data fraudulent techniques, many security agencies use Big Data tools to predict criminal activity, credit card fraud, catch criminals, and prevent cyber attacks, among others. Day by day, as security, compliance, and fraud change their patterns, accordingly security agencies and fraud transaction techniques are becoming richer to keep a step ahead for such unwanted scenarios.

Nowadays, a wrist-based health tracker is a very common thing; however, with the help of Big Data, it not only shows your personal dashboard or changes over time, but also gives you relevant suggestions based on the medical data it collects to improve your diet, and analytic facts about people like you. So, from simple wrist-based health trackers, there are a lot of signs that can improve the healthcare of a patient. Companies providing these kinds of services also analyze how health is impacted by analyzing trends. Gradually, such wearables are also being used in Critical Care Units to quickly analyze the trend of doctors' immediate remediations.

By leveraging data accumulated from government agencies, social services files, accident reports, and clinical data, hospitals can help evaluate healthcare needs. Geographical statistics based on numerous factors, from population growth and disease rate to enhancing the quality of human life, are compared to determine the availability of medical services, ambulances, emergency services, pandemic plans, and other relevant health services. This can unbox probable environmental hazards, health risks, and trends that are being done by few agencies on a regular basis to forecast flu epidemics.

Netflix has millions of subscribers; it uses Big Data and analytics about a subscriber's habits based on age, gender, and geographical location to customize, which has proven to generate more business as per its expectations.

Amazon, back in 2011, started awarding $5 to its customers who use the Amazon Price Check Mobile App--scanning products in the store, grab a picture, and searching to find the lowest prices. It also had a feature to submit the in-store price for the products. It was then Big Data's role to have all the information on products could can be compared with Amazon products for price comparison and customer trends, and accordingly plan marketing campaigns and offers based on valuable data that was collected to dominate a rapidly developing e-commerce competitive market.

McDonalds has more than 35,000 local restaurants that cater to around 75 million customers in more than 120 countries. It uses Big Data to gain insights to improve customer experience and offers McDonalds key factors such as menu, queue timings, order size, and the pattern of orders by customers, which helps them optimize the effectiveness of their operations and customization based on geographical locations for lucrative business.

There are many real-world Big Data use cases that have changed humanity, technology, predictions, health, science and research, law and order, sports, customer experience, power and energy, financial trading, robotics, and many more fields. Big Data is an integral part of our daily routine, which is not evident all the time, but yes, it plays a significant role in the back to what we do in many ways. It's time to start looking in detail at how the life cycle of Big Data is structured, which would give an inside story of many areas that play a significant role in getting data to a place that might be used for processing.

Many organizations are considering Big Data as not only just a buzzword, but a smart system to improve business and get relevant marked information and insights. Big Data is a term that refers to managing huge amounts of complex unprocessed data from diverse sources like databases, social media, images, sensor-driven equipment, log files, human sentiments, and so on. This data can be in a structured, semi-structured, or unstructured form. Thus, to process this data, Big Data tools are used to analyze, which is a difficult and time-intensive process using traditional processing procedures.

The life cycle of Big Data can be segmented into Volume, Variety, Velocity, and Veracity--commonly known as the FOUR V's OF BIG DATA. Let's look at them quickly and then move on to the four phases of the Big Data life cycle, that is, collecting data, storing data, analyzing data, and governing data.

The following illustrates a few real-world scenarios, which gives us a much better understanding of the four Vs defining Big Data:

Volume refers to the vast amount of data generated and stored every second. The size of data in enterprises is not in terabytes--it does an accrual of zettabytes or brontobytes. New Big Data tools are now generally using distributed systems that might be sometimes diversified across the world.

The amount of data generated across the globe by year 2008 is expected to be generated in just a minute by year 2020.

Variety refers to several types and natures of data such as click streams, text, sensors, images, voice, video, log files, social media conversations, and more. This helps people who scrutinize it to effectively use it for insights.

70% of the data in the world is unstructured such as text, images, voice, and so on. However, earlier structured data was popular for being analyzed, as it fits in files, databases, or such traditional data storing procedures.

Velocity refers to the speed of the data generated, ingested, and processed to meet the demands and challenges that exist in the pathway towards evolution and expansion.

New age communication channels such as social media, emails, and mobiles have added velocity to the data in Big Data. To scrutinize around 1TB of trading event information every day for fraud detection is a time sensitive process, where sometimes every minute matters to prevent fraud. Just think of social media conversations going viral in a matter of seconds; analysis helps us get trends on such platforms.

Veracity refers to the inconsistency of data that can be found; it can affect the way data is being managed and handled effectively. Managing such data and making it valuable is where Big Data can help.

Quality and accuracy has been a major challenge when we talk about Big Data, as that's what it's all about. The amount of Twitter feeds is an appropriate use case where hashtags, typos, informal text, and abbreviations abound; however, we daily come across scenarios where Big Data does its work in the backend and lets us work with this type of data.

The effective use of Big Data with exponential growth in data types and data volumes has the potential to transform economies useful business and marketing information and customer surplus. Big Data has become a key success mantra for current competitive markets for existing companies, and a game changer for new companies in the competition. This all can be proven true if VALUE FROM DATA is leveraged. Let's look at the following figure:

As this figure explains, the Big Data life cycle can be divided into four stages. Let's study them in detail.

This section is key in a Big Data life cycle; it defines which type of data is captured at the source. Some examples are gathering logs from the server, fetching user profiles, crawling reviews of organizations for sentiment analysis, and order information. Examples that we have mentioned might involve dealing with local language, text, unstructured data, and images, which will be taken care of as we move forward in the Big Data life cycle.

With an increased level of automating data collection streams, organizations that have been classically spending a lot of effort on gathering structured data to analyze and estimate key success data points for business are changing. Mature organizations now use data that was generally ignored because of either its size or format, which, in Big Data terminology, is often referred to as unstructured data. These organizations always try to use the maximum amount of information whether it is structured or unstructured, as for them, data is value.

You can use data to be transferred and consolidated into Big Data platform like HDFS (Hadoop Distributed File System). Once data is processed with the help of tools like Apache Spark, you can load it back to the MySQL database, which can help you populate relevant data to show which MySQL consists.

With the amount of data volume and velocity increasing, Oracle now has a NoSQL interface for the InnoDB storage engine and MySQL cluster. A MySQL cluster additionally bypasses the SQL layer entirely. Without SQL parsing and optimization, Key-value data can be directly inserted nine times faster into MySQL tables.

In this section, we will discuss storing data that has been collected from various sources. Let's consider an example of crawling reviews of organizations for sentiment analysis, wherein each gathers data from different sites with each of them having data uniquely displayed.

Traditionally, data was processed using the ETL (Extract, Transform, and Load) procedure, which used to gather data from various sources, modify it according to the requirements, and upload it to the store for further processing or display. Tools that were every so often used for such scenarios were spreadsheets, relational databases, business intelligence tools, and so on, and sometimes manual effort was also a part of it.

The most common storage used in Big Data platform is HDFS. HDFS also provides HQL (Hive Query Language), which helps us do many analytical tasks that are traditionally done in business intelligence tools. A few other storage options that can be considered are Apache Spark, Redis, and MongoDB. Each storage option has their own way of working in the backend; however, most storage providers exposes SQL APIs which can be used to do further data analysis.

There might be a case where we need to gather real-time data and showcase in real time, which practically doesn't need the data to be stored for future purposes and can run real-time analytics to produce results based on the requests.

In this section, we will discuss how these various data types are being analyzed with a common question starting with what if...? The way organizations have evolved with data also has impacted new metadata standards, organizing it for initial detection and reprocessing for structural approaches to be matured on the value of data being created.

Most mature organizations reliably provide accessibility, superiority, and value across business units with a constant automated process of structuring metadata and outcomes to be processed for analysis. A mature data-driven organization's analyzing engine generally works on multiple sources of data and data types, which also includes real-time data.

During the analysis phase, raw data is processed, for which MySQL has Map/Reduce jobs in Hadoop, to analyze and give the output. With MySQL data lying in HDFS, it can be accessed by the rest of the ecosystem of Big Data platform-related tools for further analysis.

Value for data cannot be expected for a business without an established governance policy in practice. In the absence of a mature data governance policy, businesses can experience misinterpreted information, which could ultimately cause unpredictable damages to the business. With the help of Big Data governance, an organization can achieve consistent, precise, and actionable awareness of data.

Data governance is all about managing data to meet compliance, privacy, regulatory, legal, and anything that is specifically obligatory as per business requirements. For data governance, continuous monitoring, studying, revising, and optimizing the quality of the process should also respect data security needs. So far, data governance has been taken with ease where Big Data is concerned; however, with data growing rapidly and being used in various places, this has drawn attention to data governance. It is gradually becoming a must-considerable factor for any Big Data project.

As we have now got a good understanding of the life cycle of Big Data, let's take a closer look at MySQL basics, benefits, and some of the excellent features introduced.

Many organizations use a structured database to store their data in an organized way with the formatted repository. Basically, data in a structured database has a fixed field, predefined data length, and defines what kind of data is to be stored such as numbers, date, time, address, currency, and so on. In short, the structure is already defined before data gets inserted, which gives a cleaner idea of what data can reside there. The key advantage of using a structured database is data being easily stored, queried, and analyzed.

An unstructured database is the opposite of this; it has no identifiable internal structure. It can have a massive unorganized agglomerate or various objects. Mainly, the source of structured data is machine-generated, which means information generated from the machine and without human intervention, whereas unstructured data is human-generated data. Organizations use structured databases for data such as ATM transactions, airline reservations, inventory systems, and so on. In the same way, some organizations use unstructured data such as emails, multimedia content, word processing documents, webpages, business documents, and so on.

Structured databases are traditional databases that used by many enterprises for more than 40 years. However, in the modern world, data volume is becoming bigger and bigger and a common need has taken its place--data analytics. Analytics is becoming difficult with structured databases as the volume and velocity of digital data grows faster by the day; we need to find a way to achieve such needs in an effective and efficient way. The most common database that is used as a structured database in the open source world is MySQL. You will learn how to achieve this structured database as Big Data that makes complex analysis easy. First, let's look into some insights of MySQL in the next section.