Last Updated on January 4, 2021
Google is one of just a few companies that issue Data Engineer Certification.
It has realized that there is an alarming need for data engineering skills and also it finds that there is a shortage of these. So, Google designed its own way of creating Data Engineers.
A recent quote from Google Cloud’s Training Department states that:
“With the market for artificial intelligence and machine learning-powered solutions projected to grow to USD 1.2 billion by 2023, it’s important to consider business needs now and in the future. We’ve heard from our customers and have witnessed internally that the data engineering role has evolved and now requires a larger set of skills. In the past, data engineers worked with distributed systems and Java programming to use Hadoop MapReduce in the data center but now, they need to leverage AI, machine learning, and business intelligence skills to efficiently manage data and analyze it. To address the new skills data engineers now need, we updated our Data Engineering on Google Cloud learning path”.
Apart from Google Cloud, many tech giants such as IBM, SAS, Cloudera, and the Data Science Council of America have followed the suit with creating Data Engineer certifications.
According to DICE’s 2020 Tech Job Report, the fastest-growing job role in 2019 was found to be that of a data engineer, with a 50% growth. It also stated that Amazon, Accenture, and Capital One, which are companies with deep pockets, are hiring data engineers at high salaries.
Moreover, the paucity of skilled and certified data engineers is fueling the demand for talent in 2020.
Obviously, Data Engineering training can be a good option for you to become a certified Data Engineer and become a professional who is most in demand for the tech-giants.
Let us now see what is actually a Data Engineer mean?
Who is a Data Engineer?
Simply put, a person who develops, builds, tests, and maintains the entire architecture of the large-scale processing system is none other than a Data Engineer. As a Data Engineer, you are responsible for collecting information and presenting it in a user-readable format. With experience, you can master different skills, such as programming, system architecture, interface, and system design.
What does a Data Engineer do?
- Design, build, test, deploy, and maintain complete data management and processing system.
- Construct highly scalable, robust, and fault-tolerant systems.
- Oversee the complete process of ETL or Extract, Transform, Load.
- Make sure that the architecture you planned meets all the business requirements.
- Suggest ways to enhance data quality, efficiency, and reliability of the system.
- Deployment of disaster recovery techniques.
- To make the existing system more efficient, you are required to introduce the latest data management tools and technologies.
Professional Data Engineer
A Professional Data Engineer is the one who has passed the certification exam and has got certified. A Professional Data Engineer is capable of enabling data-driven decision-making. This is done by the predefined steps that include collecting, transforming, and publishing data. Being a Data Engineer, you should be able to design, construct, operationalize, protect, and control data processing systems while focussing on security and compliance; efficiency and scalability; fidelity and reliability; portability and flexibility.
You should also be capable of leveraging, deploying, and continuously training already existing models.
When you take up the exam of Professional Data Engineer certification, it makes an assessment of your capabilities regarding :
- Plan and design the data processing system
- Construct and operationalize data processing systems.
- Invoke machine learning models.
- Ensure the quality of the solution designed by you.
As a Data Engineer, you will use five essential programs namely Apache Hadoop and Spark, Amazon Web Services/Redshift, C++, Microsoft Azure, HDFS, and Amazon S3.
Skills Required to Become a Data Engineer
Apart from strong knowledge in software engineering, you are expected to master some of the programming languages required for statistical analysis and modeling, building data pipelines, and creating data warehousing solutions.
- SQL and NoSQL (Database Systems)
For maintaining relational database systems, SQL is the standard programming language. When it comes to non-tabular formats, there are plenty of them; for which you require NoSQL. As a data engineer, you may come across structured or unstructured data, therefore you should master the concepts of both SQL and NoSQL.
- ETL Tools
ETL refers to Extract, Transform, and Load. it is all about how data is extracted from a source, transformed into a format so that it can be analyzed, and then stored or loaded into a data warehouse. You must know the ETL techniques so that data may be collected, transformed by using a certain set of rules, and stored in the company’s data warehouse to be read by anyone in the organization.
- Data Warehousing Solutions
A data warehouse can store massive amounts of data required for query and analysis. This data may be collected from different sources such as ERP software, account software, or the CRM platform. You are required to be acquainted with AWS, Google Cloud, and other cloud computing platforms with the ecosystem of tools regarding data storage.
- Data APIs
An API is an interface used for accessing data by software applications. Being a data engineer, you need to construct APIs in databases to enable data scientists and BI analysts to query the collected data.
- Machine Learning
You need a basic knowledge of machine learning as it allows you to understand the needs of a data scientist and eventually the organization’s needs.
You are required to master the programming languages such as Python, Java, and Scala. Today Python is considered the top programming language used for statistical analysis and modeling. You need a strong knowledge of Java as it is used for data architecture frameworks, also most of the APIs are designed for Java. Scala, the extension of Java, runs on JVM.
- Knowledge of algorithms and data structures is very essential as you need to focus on the big picture of the overall function of the organization.
- Thorough knowledge of the basics of distributed systems like Apache Hadoop and Spark. Spark is the most widely utilized programming language in data science and Hadoop libraries allow you to process large datasets across the clusters of computers.
By now you have come across all skills you need to develop in order to become a Data Engineer, which is considered one of the most in-demand jobs of the century. To achieve the certification, you should go with an online training course provided by an accredited training institute. Apart from getting flexible learning hours, you can choose from the different modes of learning which can be instructor-led, online training, or blended learning. Also, doubt sessions are carried out by the industry experts to make sure that you are prepared well for the exam.
So, go ahead and register yourself.