How to become a big data expert

21Mar - by aiuniverse - 0 - In Big Data


The use of Big Data and Analytics is catching on in a big way. According to a forecast from IDC, the worldwide revenue from Big Data and Analytics will see a five-year compound annual growth rate (CAGR) growth of 13.2% between 2018-2022 to reach $274.3 billion in revenues by 2022.

Companies are becoming increasingly data-driven and understand the value of big data and analytics in areas such as improving customer service, decreasing expenses, and driving greater innovation. There is a multitude of possible use cases across industries. For instance, in banking, analytics can help predict the probability of default. Retailers can use it to get a 360-degree view of their customers. In manufacturing, big data and analytics can help in preventive and predictive maintenance.

Needless to say, this growth also creates multiple job opportunities for professionals with the right skills.

Evolving Technologies

It was in the early 2000s that SQL Servers were introduced to extract and work on data. Subsequently, the Hadoop Framework was released, which was helpful for processing and storing extremely large data sets.

Then, around 2006, Amazon, Microsoft, and Google launched a low-cost database and compute machines on the cloud – Infrastructure as a Service (IaaS). Over time, these players incrementally released new technologies that allow analysts to use ML and AI on huge volumes of data – Platform as a Servive (PaaS) and Software as a Service (SaaS). Recently, in 2019, Google launched Anthos – a multi-cloud technology platform. Also, the operationalization of Big Data solutions has started to become the key focus.

Emerging Opportunities

The opportunities created by the evolution to cloud fall into two broad buckets – data engineers and data scientists.

As cloud vendors started to put services on top of their infrastructure, it is proving to be a real game-changer in terms of the job profiles that companies are looking for. Earlier, there was a greater demand for database developers, BI developers, database architects, etc. since they played a crucial role in managing the infrastructure. With the emergence of cloud, all these roles have merged into that of a modern data engineer. As a result, the demand for data engineers has gone through the roof. Of course, a data engineer needs to understand all the concepts around building IT infrastructure, but not in as much depth as the past.

Before companies can go ahead and accrue the benefits of data, they need to invest in building the right data infrastructure. This means consolidating data from multiple sources into a consistent format such that it can be consolidated and analysed. For example, a company might have customer data from multiple sources such as the website, mobile app, social media, etc. How do we streamline this information and bring it into a single format? That’s where the data engineering layer comes into play. Companies need to rely on big data architects or data engineers to help manage the disparate data and bring it into a usable format.

A Data Engineer plays a crucial role in building data warehouses, data lakes, etc. and putting the data in a format that is suitable for consumption. In essence, the data engineer is responsible for the creation layer.

Once the data is collated in a usable format, the consumption layer is created. The data scientist steps in to build the right algorithms probably leveraging technologies such as AI/ML to deliver useful and actionable insights.

While the role of a data scientist is often viewed as being a more aspirational role, the fact is that today, opportunities abound for both data engineers as well as data scientists.

Preparing for a Big Data Career

For any professional who is looking to enter into a career in big data, investing in the right training courses can certainly give them a leg up. Anyone from a programming background can easily switch into the field of Data Engineering, A Big Data Engineer master program can help them to build their career in this field.

It is essential to gain a strong grasp of the following skills and technologies such as algorithms and data structures, SQL, Programming knowledge of Python and Java, Cloud platforms and distributed systems, and Data pipelines to pursue a career in this domain. An appropriate master’s program can provide much-needed hands-on exposure, and grounding in the field.

The world of technology is rapidly evolving with new developments every day. Whether it is a cloud, big data, AI or machine learning, new and emerging innovation is changing the landscape rapidly. For professionals who seek an exciting career in big data, the timing couldn’t have been better!

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