Big Data Tutorial

In this Big Data Tutorial, I will give you an in-depth understanding of Big Data. For more information, go to the Big Data Course.

Below are the subjects that I will address within this Big Data Tutorial:

• The story of Big Data
• Big Data Driving Factors
• What exactly is Big Data?
• Big Data Characteristics
• Different types of Big Data
• Some examples of Big Data
• The applications of Big Data
• The challenges of Big Data

Let me begin this Big Data Tutorial by telling an enlightening short story.

The Story of Big Data

In the early days there was a time when people traveled between villages village using a horse-driven cart. However, as times passed, villages turned into towns and towns were spread out. The distances between one town to the next town was also increasing. It became difficult traveling between town as well as the baggage. Then, out of the blue one of the smartest fellas suggested that we groom and feed horses more often, in order to fix the issue. If I think about this suggestion, it’s not too bad however, do you believe horses can be transformed into elephant? I’m not sure. Another smart person said, instead of one horses pulling the cart let us have four horses pulling this same wagon. What are your thoughts of this idea? I believe it’s an excellent solution. Nowadays, people can cover vast distances in a shorter amount of time and carry more baggage.

The same idea applies to Big Data. Big Data says, till the present, we were content in storing data onto our servers due to the size of data was small and the time required to process the data was also acceptable. In the present technological age, data is increasing too quickly and people are dependent on data a lot of times. Due to the speed with which data grows, it’s becoming difficult to store it on any server.

Through this blog , Big Data Tutorial, let us examine the source that are the source of Big Data, which the conventional systems have failed to process and store.

Big Data Driving Factors

The amount of data available on the planet is increasing rapidly due to many reasons. Divers sources and our day-to- every day activities produce a large amount of information. Thanks to the advent of the web all of the globe has gone online and everything we do leaves behind a digital footprint. With smart objects being brought online and data growing, the rate has accelerated. The primary source of Big Data are social media websites, sensor networks, digital images and videos, cell phones transactions, purchase records, web logs health records, archives security for military, commerce complicated scientific research , and more. The totality of these data sources is about Quintillion bits of information. In 2020, the volume of data will reach 40 Zettabytes, which is equal to adding every grain of sand that exists on earth multiplied by seventy-five.

Learn more about Big Data and its concepts through this course. Big Data Hadoop Certification.

What exactly is Big Data?

Big Data is a term that refers to a collection of data sets that are huge and complex. They are difficult to manage and store using the available databases management tools or conventional software for data processing. The difficulty is in the collection, curation, storage and sharing transfer, analyzing, and displaying the data.

Explore Curriculum Big Data Characteristics

The five traits which are the basis of Big Data are: Volume Velocity, Variety Value and Veracity.

Volume

1. Volume refers to the “amount of data’ which is increasing day by day at a rapid pace. The amount of data created by computers, humans, as well as their interactions on social media is enormous. Researchers have estimated that up to forty Zettabytes (40,000 Exabytes) will be created in 2020, which is an increase of 300 times over 2005.Velocity
2. Velocity refers to the rate at which different sources create data each day. The data stream is huge and constant. The number of users on Facebook is 1.03 billion daily active users (Facebook DAU) on Mobile currently this is the increase in 22% from year to year. This shows how rapidly the number of people using Facebook is growing on social media , and how quickly data is being generated every day. If you’re able to deal with the pace and data, you’ll be able to make insights and make actions based on data that is real-time.

Variety

1. There are numerous sources that are making contributions to Big Data and Big Data, the kind of data they produce differs. It could be structured, semi-structured , or unstructured. Thus, there’s an array of data that is generated each day. In the past, we would obtain the data using Excel and databases, but now the data is coming as audios, images video, sensor data, and more. as illustrated in the below image. This is why this type of data that is not structured poses challenges when it comes to capturing, storage as well as mining and analyzing information.

ADVANCED

1. After having discussed volumes, Velocity, Variety and Veracity and Veracity, there is yet another V to take into consideration when analyzing Big Data i.e. Value. It’s all very well and it is great to have access massive amounts of data, but unless we are able to transform it into value , it’s not worth it. In terms of turning it into value I’m referring to, is it contributing to the success of the companies that are studying large amounts of data? Are the companies engaged in Big Data achieving high ROI (Return on Investment)? If it does not add to their earnings through the use of Big Data, it is ineffective.

Watch the Big Data video below to find out the details about Big Data:

Big Data Tutorial For Beginners What is Big Data | Edureka

In Variety the article, there are many kinds of data that are being generated each day. Therefore, let’s know the different types of datathat are available:

The types Big Data

Big Data could be of three kinds:

• Structured
• Semi-Structured
• Unstructured

structured

1. The data that is processed and stored in a predetermined format is referred to as structured data. The data stored in a relational database management software (RDBMS) is an instance of structured data. It is simple for structured data processing because it is a fixed schema. Structured Query Language (SQL) is frequently used to manage these kinds of Data.

Semi-Structured

1. Semi-Structured data is a kind of data that doesn’t possess a formal structure as an data model i.e. an underlying table definition in the relational DBMS However, it is organized by properties such as tags and other markers to differentiate semantic elements, which make it easier to study. XML documents also known as JSON documents provide examples of semi-structured information.

Non-structured

1. The data that has no shape and cannot be saved in RDBMS and can’t be analyzed without being converted to a format that is structured. It is known as unstructured information. Text Files and multimedia files like audios, images videos, and images are examples that are unstructured. Unstructured data is growing faster than other types, and experts claim about 80 percent information in an organization is not structured.

So far, I’ve only covered the basics to Big Data. Additionally the Big Data tutorial talks about some examples, applications and issues within Big Data.

Examples of Big Data

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• Walmart is able to handle over one million customer transactions per hour.
• Facebook stores, retrieves and analyses 30+ petabytes of data generated by users.
• more than 230 million of tweets get generated every single day.
• There are more than five billion people are using mobile phones to call or texting, tweeting, or browsing via mobile phones all over the world.
• The YouTube community uploads every day for 48 hours of fresh video each minute of the day.
• Amazon manages fifteen million users’ click stream information per day in order to suggest products.
• 294 billion emails are sent each day. Services examines these numbers to determine the spams.
• Modern cars are equipped with close to 100 sensors that monitor the level of fuel as well as tire pressure. Every vehicle creates many sensor information.

applications from Big Data

We can’t speak about data without speaking about the people those who are benefits from Big Data applications. Nearly all industries use Big Data applications in one or another way.

• Smarter Healthcare: Utilizing the petabytes of patient data The organization is able to extract valuable information and build applications that identify the condition of the patient prior to the time.
• Telecom Telecom companies collect data, analyzes it, and provides solutions to various issues. Through the use of Big Data applications, telecom firms have been able to drastically reduce the loss of data packets that occurs when networks are overwhelmed, which means they can provide a seamless connectivity with their clients.
• Retail: Retail is among the most slender margins and is one of the biggest users of the big data. The benefit of the use of large-scale data for retailing is the ability to analyze consumer behaviour. Amazon’s recommendation engine offers suggestions that are based on the past browsing habits of the customer.
• Control of traffic The issue of traffic congestion has become a significant problem for cities around the world. Making use of the right sensors and data will be crucial to managing traffic more effectively as cities become more overcrowded.
• Manufacturing Analytics of large amounts of data from manufacturing can help reduce the number of defects in components, enhance the quality of the product, improve efficiency, and help save both time and money.
• High Quality of Search Each time we get data from Google and we’re simultaneously creating data to support it. Google retains this data and utilizes it to enhance its search performance.

A wise person once said “Not all that is in the world will be Rosy!”. In the Big Data tutorial, I have shown you the beautiful picture of Big Data. If it were that simple to harness Big data, wouldn’t you think that every company would be willing to invest in it? Let me be clear that this isn’t the situation. There are a variety of challenges that arise when dealing with Big Data.

Once you’re aware of Big Data and its various capabilities, the following section of this blog about Big Data Tutorial will shed some light on the biggest challenges facing Big Data.

The Challenges of Big Data

Let me give you a some of the issues that arise in Big Data:

1. High Quality Data The issue here lies in the fourth fourth of V i.e. Veracity. The information here is chaotic, inconsistant and uncomplete. Incomplete data is costing $600 billion to companies each annually in the United States.
2. Discovery The HTML0 Discovery Getting insight into Big Data is like finding an unmarked needle in a haystack. Analyzing petabytes and gigabytes of data using powerful algorithms to identify patterns and insight is extremely difficult.
3. Storage The more data an organization stores the more complicated the issues of managing it will be. The main question at this point is “Where to put this data?”. It is essential to have a storage solution that is able to easily scale to the desired size or decrease.
4. Analytics in the context of Big Data, most of the time, we’re not aware of the type of data we’re working with, which means that analyzing the data can be even more difficult.
5. Security Because the information is massive in size security is a further problem. This includes authentication of users by restricting access to the identity of the user, recording access history, the proper usage of data encryption, and more.
6. Insufficient TalentThere are numerous Big Data projects within big companies, but having the ability to build a skilled team consisting of data scientists, developers and analysts who have a enough domain expertise is a major challenge.

Hadoop for the rescue

There is a way to face Big Data challenges – its Hadoop. Hadoop is an open-source Java-based programming framework that allows the processing and storage of huge data sets within an environment of distributed computing. The framework is one of the components of the Apache project that is sponsored by the Apache Software Foundation.

Hadoop thanks to its distributed processing capability, manages massive amounts of unstructured and structured data much more efficiently than the traditional data warehouse for enterprises. Hadoop can run applications on systems that have thousands of hardware nodes that are common to the industry and handle thousands of terabytes worth of data. Many organizations are taking advantage of Hadoop because it’s an open source program that can be run on hardware that is common (your personal PC). The cost savings at first are substantial as common hardware is quite affordable. As the amount of data you need to store grows and you require additional hardware in order to store it. Hence, Hadoop proves to be cost-effective. In addition, Hadoop has a robust Apache community that is constantly contributing to its growth.

As I promised in the beginning of this blog post about Big Data Tutorial, I have provided you with the most comprehensive information about Big Data. This concludes the Big Data Tutorial. The next step is to understand and learn about Hadoop. We’ve got an entire sequence of Hadoop tutorialblogs that will provide specific knowledge about the Hadoop ecosystem.

Once you know the basics of Big Data, check out the Big Data training in Chennai by Edureka which is a reputable online training company that has more than 250,000 satisfied students spread all over the world. This Edureka Big Data Hadoop Certification Training course assists learners in becoming experts on HDFS, Yarn, MapReduce, Pig, Hive, HBase, Oozie, Flume and Sqoop with real-time application cases for Retail, Social Media, Aviation Tourism, Finance, and other domain.