Big data analytics is the use of advanced analytic techniques to examine a large amount of structured, semi-structured, and unstructured data and uncover hidden patterns, correlations, and other insights. With the advent of more and more data analytics tools, we can analyze data and get answers from it almost immediately.
Big data analytics helps organizations harness their data and use it to identify new business solutions. It has led industries to smarter business moves, more efficient operations, higher profits, and happier customers. To summarize, big data help businesses in –
Software framework for storing data and running applications on clusters such as Hadoop and cloud-based analytics has brought significant cost advantages with data storage at scale – plus these also help businesses identify more efficient ways of doing business.
Better and faster decision making
With the swiftness of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on actionable insights.
New products and services
Big data analytics provides the ability to gauge customer needs and satisfaction through customer data. A programmatic study of customer data allows businesses to give customers what they want. This has enabled more businesses to create new products to meet customers’ needs.
Reasons students and professionals are willing to start a career in Big Data
Salary and Career Opportunities
Companies are increasing their investment and resources in Big Data, making it one of the leading areas in terms of salaries and career opportunities. A lot of our learners want to make a career in big data is because they saw their peers advancing their careers by getting into Big Data and thought that they can do it too. Professionals in teams other than data engineering saw the data engineering team had open positions, whereas, their team didn’t have any open positions or was laying people off.
As salary and career growth are some of the biggest motivators for people, you can make more, sometimes substantially more, with Big Data skills. People are taking their stagnating or disappearing career path and finding an expanding career path in Big Data.
High Demand/Low Supply
The market position of high demand and low supply is known as the best position in a market for an item. The situation is similar in the case of Big Data, which is the high demand for qualified people who know Big Data technologies. However, there is a scarce supply of these people with correct skill sets in big data. This inequality in the market means several good things for you.
Fascinating Business Problems
Let’s face it while developing most enterprise software, it just gets old after a while when you’re doing the same thing over and over again. But while working with data is different and gives you the freedom to experiment. We have seen good learners for being a Data Engineer are people that are bored with their regular jobs and want to start working on something that doesn’t have a deterministic ending. You get to analyze, create the data pipelines, and consume the data pipelines that give you cool insights into what’s happening within the organization.
Excessive or overwork in other profiles
A lot of data analytics professionals are former software developers, which is technical but demanding at the same time. They switch to data science as they want more ’creative time’.
The Benefits of Getting into Big Data
- Fewer people competing with you for the same job
- Salary is expected to rise as organizations are looking for more qualified people
- A decent entry barrier not everyone can just come up and learn Big Data
- Quick promotion due to new and small data engineering teams
Improving your Big Data Skills
We’ve seen the good things about Big Data, but the first thing we recommend is to assess is the gap between your current skills and the skills that will get you a Big Data job. All our successful students taking a big data program had the only thing in common, they allowed them to be successful at learning Big Data. You need to have an answer to these questions to be successful as early as possible:
- Do I have a desire to learn Big Data?
- Do I have some of the prerequisite skills?
- Do I have the time to dedicate to learning?
- Do I have an expert (or experts) to guide me through the experience?
You will need all four of those points, in their entirety. If you’re missing one of
those points, it will take forever to learn Big Data and you’ll give up. As a learner, you need to look over these points so you don’t waste your time pursuing something you can’t fully realize.
Willingness to Learn Big Data
You will have to put in the effort to learn Big Data. Just hoping and trying to passively learn isn’t going to get you anywhere. You can sit and watch Big Data videos passively and earn certificates but you’ll find yourself in the same position without hands-on experience and an expert as your mentor.
Your current situation has a lot to do with your motivation. If you have a few years of experience, adding Big Data skills to your existing work experience, you become an asset to the organization. Most data engineering teams are inclined towards the senior-level titles. You can also use your existing domain knowledge and your new Big Data skills to get your next job. People with math or statistics background are often good candidates to become Data Scientists.
Although data engineering teams tilt towards senior people, that doesn’t mean that newly graduated people or people who are fresh out of school don’t work on data engineering teams. There are fewer of these people and they’ll need to improve their skills.
Skills required for a successful career in Big Data
First and foremost, you will need programming skills. You should have at least intermediate programming skills. People who are brand new to programming struggle as Data Engineers.
More helpful, but not required, is a background in distributed systems. Big Data frameworks like Hadoop are distributed systems. These frameworks make it easier to work with distributed systems, but don’t completely mask all of the complexity. At some point in your journey, you’ll need to learn these concepts to really master Big Data frameworks.
Another possibility is to build on your existing multi-threading skills. If you have done cross thread and concurrency work, some of Big Data’s concepts will be familiar.
Programming languages for Big Data Development
The majority of Hadoop and Hadoop ecosystem is written in Java. You should have an intermediate to advanced level of Java knowledge. You need to understand concepts like generics, inheritance, and abstract classes. Some Big Data frameworks are written in Scala.
Apache Spark is one of those technologies. Data Engineers and data engineering teams are 95% Java-based. Scala is more popular with data science teams because of its dynamic nature.
Python is another popular language with Data Engineers and Data Scientists. Apache Spark and Apache Beam have native Python support. Python is usually supported as a quasi-first class citizen. Everything gets added and tested in Java/Scala first and then ported to Python. This means that Python will lag in support and not have access to everything.
R is another popular language with analysts. Its support in the Big Data ecosystem is emerging. Most projects will have little to no support of R. Other languages, not mentioned here will work to varying degrees of effectiveness and gotchas.
SQL – It’s just easier to express some things in SQL, thus we include SQL in our data science programs as well.
The Time to Dedicate to Learning
Let’s say for whatever reason a person doesn’t have the time to dedicate to learning. They’ve been misguided by their previous experience with small data technologies. They think they can get to an intermediate, or maybe even an advanced level, in a week or two. Those sorts of timelines don’t carry over into Big Data. They will miss out on job opportunities because they aren’t willing to put in the time and effort necessary to switch careers.
An Expert to Guide You Through the Experience
Learning Big Data isn’t easy and it’s even harder without someone who is a recognized expert to learn from. You probably can’t look through a class or syllabus and spot the signs of a massive waste of time. You will need an expert to guide you through a complex Big Data landscape. Spend the extra time and money to find the right person to learn from.
Industries Using Big Data
Virtually every industry is using Big Data. Some organizations and industries have more data than others. Still, others have been using Big Data for a longer period of time than others and some organizations are just starting out with Big Data.
Let’s talk about how a few industries are using Big Data and where you’d fit into their team with a Big Data background. All of these industries are looking for qualified people and are having difficulty finding them.
The Internet of Things (IoT) is an exciting usage of Big Data. There are two general things most IoT companies need. IoT companies need to ingest or acquire data — and this ingestion needs to happen very fast. This is because so many devices are sending in data at all times and the company is careful that important data doesn’t get lost. Next, they need to analyze that data in some way. This analysis could happen as the data comes in (real-time), later on as a file (batch), or both. The actual analysis will be driven by the use case. A data engineering team is responsible for both the ingestion and analysis of incoming data. They may work with other parts of the organization to write the analysis or understand the sort of data they’re working with.
Financial organizations make extensive use of Big Data. These organizations were some of the first with Big Data problems. They have all sorts of data that needs to be processed. This could be doing end-of-day reports and calculations. It could be providing the data for trading and predicting when to trade based on large amounts of input data.
Working at a financial organization requires a great deal of domain knowledge. If you’re able to mix your in-depth domain knowledge with the Big Data technical knowledge, you’ll be in high demand.
These businesses have very specific Big Data needs and they can’t just go out and hire new people. Usually, it’s more time-efficient to train their existing staff on Big Data because they already know the existing systems. Financial organizations may not be the most exciting places, but they pay well and they’re stable.
Have you ever wondered why social media companies are worth so much despite having a product that is free? The answer is that companies like Facebook, Twitter, and LinkedIn are interested in your data. By making wise use of this data, they can use this data to market products to you. That’s the very basic description of their business model, but how do they do it from a technical perspective? They take their Big Data and process it to understand who you are and what you like. These companies are at the forefront of Big Data and often create their own Big Data technologies to handle their use cases. A few examples of these are Presto from Facebook and Apache Storm from Twitter. These companies want people with the latest skills. If you know the latest cutting edge technology, a social media company can make faster and better analysis about their users.
Marketing and eCommerce
Marketing and eCommerce companies share a common goal. Both types of organizations use data to sell to their customers. They have vast quantities of data about their customers. Most companies will track every online interaction with a site. The real value comes in analyzing those interactions.
Your Checklist for Starting to Learn Big Data
We are listing the precise things that you should be aware of before you prepare to switch your career to Big Data Development
- Put together a clear and achievable goal for switching to Big Data
- Categorize the skills you need to acquire
- Pinpoint the technologies you need to learn
- Enroll in the courses/programs that will teach you the skills and technologies
- Device a pragmatic and workable learning schedule
- Start preparing for your personal project
- Start interacting with peers and network with others in the community
Keys to Getting the Job
- Clearly identifying which position they’re going after
- Figuring out what skills they have now and what skills they’ll need eventually
- Learning and applying the skills they need to get a job
- Continuing to learn and improve those skills
- Thinking that programming is writing out equations in code
- Thinking that the technical skills don’t matter
- Failing to learn to code well before moving on to more complicated problems
- Taking an honest look at your abilities and skills when self-evaluating
Tips for Success
- Take enough time to get to an intermediate-level programming skill
- Find the best learning resources to learn and use them properly
- Being honest about your skill level during an interview
- Having a personal project that showcases your skills and how your different point of view will complement a data science or data engineering team
By making this far in the article, you’ve already done more preparation than most people. Our goal is to help you decide if switching careers to Big Data is right for you, show you the steps to switch, and how to get a job once you’re ready.