Highlightsof Our Big Data Course online

  • Industry-Oriented Course
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  • Mentoring sessions
  • Practice Quizzes
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Enroll in our Big Data course to become a pro

At My Learning Course, we offer A to Z information about big data science and this particular industry. You can easily choose as per your interests & requirements to enhance your level of knowledge about the data-oriented industry.

Our Big data course modules are available in two categories; one is for beginners and another for professionals. At My Learning course, learn Big data course including, an Introduction to the big data industry and technologies such as Yarn, Hdfs, Hive, and MapReduce.

And in the second framework, we will introduce you to numerous data formats such as Avro, JSON, Parquet, XML, and Protocol Buffers. The last part of our offered module provides a brief about some essential tools to collect big data.

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How is our Big Data course beneficial to you?

Learn Big Data Course in the most up-to-date industry standards, with My Learning course, which will help you advance your career. 

  • Our cutting-edge program prepares you for professional growth.
  • Become a certified Big Data Engineer without needing to quit your job.
  • Acquire knowledge of the most up-to-date Big Data technology.
  • Feel secure by putting in place a real-time end-to-end project.

How does the Entire Process Work?

  • 1. Choose the Course - In the first step, you select a course from My Learning Course according to your budget, skills, and specialization. It allows you to start the entire subscription that will end whenever you want to stop it in the future. We advised you to pick your learning modules carefully.
  • 2. Hands-on Practical & Projects - Along with theory and applied for course programs, our tutors also provide you with hands-on big data projects where you can learn and practice all the latest and advanced tactics. You have to complete and submit all the undertaking assignments with a deadline.
  • 3. Earn your Certificate - After completing all the projects, you will be eligible to get a certificate that you can use as proof of completing that particular specialization course in big data technologies. You can add those certificates to your CV to enhance the format.
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What is big data, types of big data?

We are already into an advanced era of data science and big data, which directly refers to the data sets analysis process used to collect and find some insights properly. You need new data sources to complete the big data mining process.

The meaning of big data is similar to huge and when you use some of the tools to manage, store and classify these huge sets of data, the process starts. In the educational modules of the My Learning Course, we will let you learn and enhance the advanced knowledge of some of the data mining tools as well, which comes under the advanced level of the big data courses syllabus.

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Types of Big Data

Big data encompasses a wide range of data types; structured, unstructured and semi-structured data

• Structured  - This is a type of data that you find in a proper and fixed format. It is generally stored in a database that follows a simple algorithm. This is also known as one of the easiest data types as users already know its format. The data store in any organization’s datasheet in the form of spreadsheets and tables is the best example of its type.

• Unstructured - Data having unknown or not having a fixed or proper structure is identified as unstructured data type. Suppose, you search something on Google and find various results including images, videos, website links and news related to that particular search keyword, which is known as one of the examples of an unstructured type of big data.

• Semi-structured - Semi-structured refers to the combination and mix of structured and unstructured data types. In this, you will find unstructured data having some relevant and essential tags. A table definition in a DBMS (Database Management System) can be an example of this big data type.

Big data technologies contain numerous characteristics such as veracity, value, volume, variety, validity, visualization, volatility and velocity. You can mention this in various ways including 3 vs of big data, 4 vs of big data or 5 vs of big data industry.

You can mention this in various ways including 3 vs of big data, 4 vs of big data or 5 vs of big data industry.

  • • Veracity - This characteristic denotes the level of accuracy and trustworthiness in various data sets. The main reason to ensure accuracy is to produce meaningful and valid results. If you include wrong or bad outputs into your results, it will decrease the value of business analytics initiatives and show up several errors.
  • • Value- Before maintaining any data science mining procedure, you need to understand that all the collected data never comes with a real value. You have to skip some of the results. And business organizations need to confirm that data that is used in big data analytic projects relates to relevant niches only.
  • • Variety - This refers to different types and forms of data that you gathered from more than one source. And it means you have to manage the variety of your collected data properly and organize it correctly.
  • • Volume - This characteristic shows the amount of data or information you have stored. We can measure the volume in Zettabytes (ZB), Gigabytes and Yottabytes (YB) respectively.
  • • Validity - It prefers that the data is valid and relevant, and it solves a kind of purpose. Data without validity is just a waste and there is no use of it.
  • • Velocity - This simply means the speed of the big data processing and data storing procedure. And it is a crucial one to perform any data collecting process. This option consists of the activity bursts, rate of change, and the linking of incoming data sets.
  • • Volatility - There is a fact when we talk about data; as it always keeps changing. What you get today's search, may not get the next time. It is known as data variability and it can affect data homogenization.
  • • Visualization- When you use visual representations such as graphs, charts or infographics to show or mention your stored data report then it is called visualization. Professionals use these tactics to represent data insights with non-technical users or audiences.

1.Predictive Applications - It is just another word used for identity and access management, which simply means to control the entire process of managing the control of your data access program. With this option, you can also rectify issues including how someone can gain identity with access and how to protect it.

2.Data Processing - This is a process where you can organize and store raw data and convert it into a meaningful outcome. Generally big data professionals use visual diagrams and charts to show the results. And it becomes easy for users to obtain and search from the insights and organized conclusion.

3.Reporting - The main goal of this data processing feature is to keep the customers at the top. They easily can get a minute-to-minute report in a proper dashboard format and find location-based insights.

4.Security - With the help of this feature, you can keep safe and secure your system and login credentials. There is a single-sign-on feature, which allows you to access multiple applications using a single detail of input credentials.

5.Technology Support - One of the best features you can get through data analytics tools is that it should support a variety of different technologies at the same time. This process is also known as bucket testing which tests two or more two versions of a web page or application to determine the best one.

6.Analytics Big data comes with analytic tools which provide users with a set of modules and packages such as risk analytics, text analytics, decision management, content analytics, statistical analytics and predictive analytics.

For example; with risk analytics, users can decrease the negative impact of future or upcoming events.

A big data engineer is someone who is an IT professional who works to maintain, design, test and build a detailed and proper report of sets of any type of data. He should have a deep knowledge of SQL database design and different programming languages.

This is one of the growing sectors in India and the big data engineer salary starts with an average bar of 12 Lakhs per annum. So you can enter into this field and earn a lot of bucks as per your knowledge, skills and experiences.

The role of a data engineer is huge and it includes managing and completing a proper data mining process. You also can master this field, just choose one of the courses provided by My Learning Course and give wings to your career.

Although, the task of a data engineer is not easy at all, that is why our team of experts and tutors offer each possible help to make you understand and learn all about big data analytics and technology.

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What are the Main Challenges of Big Data in 2022?

The big data industry is evolving each day and like other latest technologies, the particular technology is facing some of the challenges, which are  shown here:

  • 1. Data Growth Issues - One of the challenges massive data face is that the database of companies is expanding and increasing day by day, and it becomes very hard to store and organize it. The solution to this issue can be the use of the latest data analytics tool where you will easily find several features and options.
  • 2. Real-time Insights - In the data processing procedure, you don’t even need to store only but you need to complete this entire job promptly to achieve goals. As for the solutions, you just need to start using some of the advanced analytics and ETL tools which surely can help you in finding real-time insights from raw data.
  • 3. Data Management & Validation - As a challenge, you need to check the correctness and completeness of data in the data validation and management section. As usual, some of the data mining and data analytics tools can guide you in tackling this challenge directly.
  • 4. Data Security & Privacy Problem - Securing these huge data sets is one of the daunting and hectic tasks to perform and that is why it turns into a massive problem these days. With the help of some cybersecurity specialists, you can overcome this blunder as well.
  • 5. Data Handling Cost - From start to end, data processing can be an expensive operation. You need to spend on new hardware, the latest technologies, fresh recruitment and some paid tools. In the solution, you can opt for some hybrid big data solutions for your business or make proper research then select specific technology and tactics as per your requirements and business goals.

Student Testimonials

I am fortunate to be able to immediately enroll in My Learning. It has had a huge influence on my professional path. The most effective way to boost your career by taking an online Big Data course.

Frequently Asked Questions (FAQs)

Big Data is among the most appealing professions since it offers a wide range of chances. Data Engineers, Data Analysts, and experts with Big Data experience are in high demand today.

It's a technical position necessitating a bachelor's or master's degree in analytics, computer modeling, science, or mathematics.

Big data technologies are used in various industries including energy, finance, transportation, crime prevention, smart city initiatives, logistics and some other government services. All these fields generally take help from this data mining procedure and keep an eye on risk-management and real-time analysis of big data collected by the organizations.

SQL (Structured Query Language) is a programming language that is used to retrieve, update, insert, manipulate, and alter data. SQL is capable of handling large datasets. Every ambitious data scientist should have this competence. SQL is a requirement in today's Data Science jobs.

Data is the main component of AI, as it powers this technology. Without data analysis, AI and machine learning technologies and applications cannot perform properly. It helps to improve, learn and acquire skills of AI applications.