With the rise of technology, digital information from people, businesses, and machines have increased exponentially over the decades. You can consider data is the petroleum of the 21st century.
Organizations producing megabytes of data a few decades back are now producing petabytes of new data every hour—for example, more than 3.3 billion smartphone users in the world.
Introducing Big Data
Every person consumes, interacts, and therefore creates data across thousands of apps and content monetization platforms, leading to the creation of 2.5 quintillion bytes of data every day. Data scientists estimate that by 2020, each person in the world will create 1.7MB of data every second.
Imagine you are responsible for managing all the data all people and machines create in the world in all possible mediums like social networks, search engines, jets, ships, and every other object connected directly or indirectly with a computer.
This massive amount of data will require tools, processes, infrastructure, and expertise necessary to find meaning and patterns that could illuminate fundamental questions.
This study, research, and work on such excessively vast amounts of data comprise the term Big Data.
Why Should You Learn Big Data?
Before answering how it’s important to answer why. Why should you or anyone learn Big Data? Technology like humans, evolved to adapt to its surroundings. Charles Babbage invented computers to solve mathematical problems.
As computers became powerful, they were applied across disciplines and industries to solve problems that are either difficult or impossible to solve by humans.
Eventually, the computers shrunk in size, and the widespread use of GUIs became commonplace among people.
Computers shrunk further to form gadgets like smartphones that today allow us to achieve wonderful things.
The data generated from the smartphones were stored, computed, and analyzed to find patterns that explain human behavior.
Complex algorithms compute vast amounts of data generated at stock exchanges, search engines, social networks, rocket launches, supermarkets, and nearly every major human endeavor.
As a Big Data practitioner, you will be responsible for finding answers to questions like value created by users on social networks, patterns in health care systems around the world, or even answers to simple questions like the number of visitors to a website that buy a product and their characteristics.
Finding answers to such questions will help your organization grow and compete on a global scale.
Therefore you should learn Big Data if you like finding answers to such questions from vast datasets generated from similar enterprises.
Who Should Learn Big Data?
Learning is for everyone regardless of the subjects or the complexity of the topics. Every technology or concept is built on top of simpler concepts.
Big Data is also built on top of similar simpler topics like mathematics, statistics, algorithms, programming languages, databases, and several other similar concepts and frameworks.
Therefore learning Big Data often suits people who come from backgrounds like mathematics, statistics, and computer science.
You can master Big Data even if you’re not from any of the above fields, but it might take you more time based on your learning speed.
The Way To Master Big Data
It often becomes quite difficult to find a definite learning path for huge topics like Big Data.
However with the democratized way of learning now offered by the internet and its billions of users and thousands of content creators means there is no dearth of information, guides, courses, and experts to guide you when you stumble.
This guide further elaborates on the different ways you can learn Big Data and start your career in this quickly growing field of technology.
It is always beneficial to learning if you immerse yourself in the environment of the topic while you’re learning about it. So start by searching about Big Data and reading about it from the top blogs, websites, research papers, and other similar sources.
Phases Of Learning Big Data
Learning a subject always happens in stages that eventually build our expertise. We begin from the very beginning by understanding the simplest concepts and progress slowly to higher-order and more complex topics that help us understand and apply the concepts in real life. Big Data can also be classified into three distinct phases where you begin by understanding data and its types.
You learn how to manage, import, and use data based on your requirements. By using the common tools for storing, importing, and visualizing data sets, you can get comfortable with the building blocks of Big Data itself.
Then you can delve deeper into understanding how organizations use the data for finding answers to problems.
By using the right tools and applications, you can practice on large datasets and gain intermediate expertise on the subject.
This is the phase you spend the most time. Regardless of how sophisticated the data or how large a dataset might be, you learn how to manage and derive meaning out of it.
The more problems you solve in this stage, the closer you get to expertise.
Once you have mastered the intermediate concepts and are comfortable with managing the data across large systems and tools, you can move forward to applying expert techniques in predictive analytics. Until now, you’ve been using data to find meaning.
Now you’ll be using data to predict what will happen in the future based on what has already happened in the past. Your overall level of expertise in Big Data depends on your expertise in each of the underlying technologies.
You start by getting acquainted with data, both small and large. As you cannot store terabytes of data on your system, you will be using smaller datasets or sometimes subsets of a larger database.
If you work with different types of data generated by organizations and applications, you gain a better understanding of what to expect and how to manage such huge datasets.
By incrementally increasing the size of the dataset, you can learn more about the necessary tools required and the challenges you face while computing large datasets.
This is when you need Cloud servers to store and compute large datasets. Besides, you will be learning about analyzing and visualizing the data through BI tools like PowerBI and Excel.
You’ll also be querying data from the datasets with SQL and SPSS. By getting deeper into Data Mining practices, you will gain the necessary skills for employability.
By the time you reach the intermediate level, you will be comfortable with all kinds of data and will be able to find effective ways to visualize them at scale.
Now you have to use larger datasets on the cloud to find solutions to even more complex problems. You will be using cloud services from Amazon, Google, and Microsoft to interact and find solutions from real-time data.
As organizations produce data every second, using real-time data to make decisions is a huge skill in demand.
By using the right tools for analysis and exploration, you will be responsible for finding how consumers behave and how it changes with time.
You will also be responsible for adapting your strategies and analytical abilities by using industry-standard tools and frameworks like Hadoop on the cloud.
Moving from intermediate to an expert level at Big Data requires extensive experience across multiple domains and technologies.
It might take you months or even years to move to the expert level, depending on how many problems you solve at your basic and intermediate level.
As an expert, you will have to implement technologies from Data Science, Machine Learning, and Artificial Intelligence to find effective solutions for problems on both real-time and static datasets.
Your expertise in Machine Learning algorithms and their application in Big Data will be the deciding factor in how good you are at finding solutions to large problems in organizations.
By implementing cloud-based machine learning algorithms, you will be able to crunch, analyze, and compute large sets of data while producing results for simple managerial uses.
You might also be responsible for managing customer expectations, and therefore your soft skills will also play a huge role in your success.
As always, your expertise in each of the technologies below will determine how you are valuable to an organization and the domain you will be working on.
Starting from basic computing on Unix based systems to managing cluster of cloud instances to compute large scale datasets is necessary for your growth as a Big Data professional.
- Linux: The most commonly used operating systems in organizations and also on cloud systems will be an incredible tool at your disposal. The better you can manage your Linux instances, the better you can utilize your resources at hand to grow and scale machine learning algorithms to compute Big Data.
- Data Science: Statistics and Data Science are the building blocks of Big Data. Therefore a clear understanding of the underlying principles of analysis is very important to understand higher-order functions and concepts in Big Data.
- Java and Python: Big Data experts. widely use Java programming language. You will be responsible for writing custom code to use API’s from multiple sources while analyzing and computing datasets. Python is another popular language and is relatively easier to learn. However, you will often need expertise in both languages as some environments do not support Python as of yet.
- SQL and NoSQL: The fundamental query language is necessary for all aspects of data science, analysis, and Big Data. Without SQL and NoSQL, it will be impossible to query even the smallest databases efficiently.
- Machine Learning: Algorithms that learn from your dataset to produce actionable results while continuing to learn when new data is available is very important to manage both static and real-time datasets.
- Hadoop: Hadoop is right now the most popular and widely used Big Data platform used for storing enterprise data in distributed clusters. Most of your machine learning algorithms will be applied to datasets stored on Hadoop. Underlying technologies like MapReduce will further help you glean into deeper aspects of data.
- Others technologies like HDFS, Hive, Pig, Spark, HBase, Drill, ZooKeeper, Kafka, Storm
Courses, Certifications, and Career Paths
Now you can find thousands of sources for Big Data, Hadoop, Spark, and other related technologies on the web.
Marketplaces like Udemy, Pluralsight, Lynda, and others have hundreds of courses ready to consume.
Some other websites also provide extensive courses on all topics related to Big Data and their application across industries.
Even cloud service providers have training modules and free computing resources to help you get started with Big Data and related technology stacks.
Numerous universities now provide a bachelor’s and masters in analytics and big data to help prepare students for the sudden demand in data science-related opportunities.
If you prefer classroom-based learning, enrolling in any of the top courses in the universities can provide you the necessary environment to learn and grow along with like-minded people.
If you like online courses, you can find some of the best ones on Edx, Udemy, Coursera, and other major marketplaces.
The online learning environments also provide the necessary ecosystem for you to learn and interact with course providers and learners from all over the world.
The best-rated ones are often the most recommended courses. There are numerous free resources to learn from on the internet too, that use content monetization to provide quality content.
Certifications will be necessary when you search for jobs in the field. Certifications from Amazon, Google, and Microsoft have a special place and therefore, they’re highly recommended.
Along with Big Data and related technologies, you will also have to gain expertise in cloud environments that are widely used across enterprises. These certifications are, therefore in huge demand by employers.
After you believe you have gained the necessary expertise in any of the subfields of Big Data, it’s time to choose a career path.
Some of the common and in-demand roles are Database Administrators, Database Developers, Data Analysts, Data Scientists, Data Modeller, Big Data engineers, and numerous more.
Learning on the job is always preferable as you will gain the necessary skills used in organizations while also getting exposed to environments and challenges that you cannot get in a classroom or online learning environment.
Mokhtar is the founder of LikeGeeks.com. He works as a Linux system administrator since 2010. He is responsible for maintaining, securing, and troubleshooting Linux servers for multiple clients around the world. He loves writing shell and Python scripts to automate his work.