You’ve probably heard of data analysts and data scientists. But what about big data engineers? Today, everybody wants to become a data scientist, but why negate big data engineering? In a nutshell, big data engineering is a hybrid between a data analyst and data scientist.
If you deeply define data engineers, they’re big data professionals who are capable of managing data workflow, pipelines, and ETL processes. Data engineers are responsible for building huge reservoirs for big data.
Ever since the data outflow, there has been a huge demand for professionals in this field. Irrespective of what your company deals with to gain success, you will still require an infrastructure to build and store the company’s data and access it when required.
- But what does a big data professional do?
- What are the skills required to become a big data engineer?
- Where can I learn these skills?
These are common questions you may come across while looking to start a career in the big data field.
Big data engineer
Professionals from the big data domain are responsible for creating and maintaining the analytics infrastructure enabling most of the other functions in the data world. They construct, develop, maintain, and do the testing of architectures – databases and large scale processing systems, etc.
Besides this, they have a solid command of the common scripting languages and tools and are expected to use this expertise to improve data quality by leveraging the data analytics system.
Must-have skillsets
To launch a career in big data, these are the top-most skills you need to grasp.
- Coding: Extensive knowledge in coding is necessary – this includes programming languages such as Python, C++, Java, Golang, Perl or other languages.
- Machine learning: Big data engineering in itself has a large scope. Moreover, skills such as machine learning and data mining play a major role in the contribution of the big data world. Though there’s a shortage of talent in the machine learning field, developing skills in ML will help data professionals in personalizing the systems and carry out predictive analytics.
Expert professionals in this field are typically in-demand in top companies such as Spotify, Netflix, and Amazon.
- Apache Hadoop: Over the past years Apache Hadoop has seen extensive growth. Components such as Pig, Hive, HBase, and HDFS are currently the most sought-after skillset. Though Hadoop is said to be a decade old, it still holds a crucial role in most industries and companies.
- Cloud clusters: Due to the large reliability function that big data offers on the network, most of the work is outsourced to the other cloud to avoid discrepancies. These huge volumes of data can be stored in several cloud clusters to avoid the hassle.
- NoSQL: NoSQL databases are now replacing the tradition databases such as Oracle and DB2. The only reason is that these databases are much better with storing data whose volume is large. Along with skills in Hadoop, these developers are in high demand.
Based on a report by IBM, it is seen that there are already 83% of the world’s organizations have started adopting big data in their projects.
In terms of upskilling, tech professionals must take up big data engineer certification. The internet is a tech-driven era, and acquiring newer skillsets is always an added advantage.
It is not a cakewalk to get into this field, it requires more than just formal education. A hybrid approach such as professional certification is important.
The future is unpredictable, and things will not be the same. The world is undergoing a tremendous shift and it is going to be imperative that you get inclined to technology. Thus said, keep learning and keep upskilling.