A decade ago, companies used to deal with a limited amount of data. Traditional relational database management systems were appropriate to handle that data. What about today? People generate over 2.5 quintillion bytes of data each day, and companies gain insights from it to make better decisions. Experts refer to such large amounts of data, including structured and unstructured types, as big data. These massive datasets are often large, change quickly, and are quite complex, that processing them with traditional methods is almost impossible.
The top reasons companies are relying on big data are reducing costs and time to market for products, make better business decisions, offer cutting-edge services, and ensure increased customer satisfaction. Organizations that combine big data with powerful analytics achieve many benefits like finding the root causes for real-time issues, offering services based on customer’s purchase habits, optimizing their resources, and detecting fraudulent activities before impacting the business.
A big data engineer is one of the common job roles you will find in this field. Organizations hire big data engineers to develop algorithms and uncover hidden trends from raw data. They put efforts into creating and maintaining the environment that allows the operation of other functions in the data world. Additionally, they build the infrastructure and data pipeline to make the data in a readily available format. They write complex scripts to prepare, clean, filter and modify data collected from disparate sources.
Big Data engineering has become such a popular career field these days that many training providers have stepped up to guide professionals through their big data courses. This article is for all the readers seeking details regarding the steps to take to become a big data engineer.
Contents
Big Data Engineer Career Path – Responsibilities, Skills, and Qualifications Required
When you start looking for data-related job roles, you will come across data engineers, data analysts, data scientists, and more. These terms may seem confusing to you at first. As we discuss the role of a data engineer here, his key responsibility is to make sure that data is available in the right format to a data scientist to start with analysis. Companies collect data from a variety of sources that may have different formats and may usually be unstructured. So, data engineers collect large data sets, manipulate, store, and utilize them when necessary.
Though a big data engineer’s responsibilities may vary in every organization and the type of project they work on, they primarily build robust systems for ingestion and data processing. They carry out Extract Transform Load (ETL) processes and research new ways to extract data and improve its quality. Additionally, they understand the business requirements clearly and build data architectures that meet them. They integrate various tools and programming languages to mine data from disparate sources and create efficient business models.
Qualifications needed – Big data engineers usually have a bachelor’s degree in computer science, mathematics, or a related field. A master’s degree in data engineering is also beneficial to become eligible for this role. Even if you have graduated in a different stream but are interested in a big data career, you can opt for training courses or industry-recognized big data certifications and demonstrate your skills.
Most Importantly, the big data engineer skill set required
Now that you are aware of the responsibilities and qualifications related to a big data engineer, you must know the in-demand skills for this role.
- Knowledge of core math concepts like linear algebra, calculus, and probability is required, along with statistics.
- Familiarity with any one programming language is essential. Python, R, C++, and Java are the preferred languages.
- Working knowledge of operating systems like Windows, Linux, and Solaris is important for a big data engineer.
- Candidates should know how to build a data warehouse and carry out ETL operations.
- As data needs to be collected and handled, one should know how to write SQL queries and know about databases like MySQL and Microsoft SQL Server.
- Another obvious skill of a big data engineer is experience in Apache Hadoop, which is one of the widely-used data engineering tools. Related concepts like HDFS, Apache Pig, HBase, and MapReduce are also must-have skills.
- Apart from Hadoop, you should know about the real-time processing framework, namely Apache Spark. An analytics engine can take live streaming data from different sources and allow both batch and real-time processing.
Apart from the skills mentioned above, there may be some additional skills which companies would expect big data engineers to have.
The Next Step
Knowing the recommended skills and developing them isn’t enough to get hired as a big data engineer. Many professionals don’t have the required background or experience in data engineering and often struggle to land a decent job in this field. This is where training courses and certifications can help.
Here are some of the industry-wide acknowledged certifications that can validate your data engineering skills and grab the attention of hiring managers:
- Google Cloud Certified Data Engineer
- Cloudera Certified Professional (CCP) Data Engineer
- IBM Certified Data Engineer – Big Data
- AWS Certified Data Analytics – Specialty
- SAS Certified Big Data Professional
Feel free to take online big data courses to learn all the important data engineering concepts and gain the necessary skills. Some reputed training providers also guide you to pass the exams of big data certifications mentioned above and other such popular credentials. Employers will also understand that you are serious about the role and completed the groundwork.
