There's nothing more valuable than data —and that's true. According to the World Economic Forum, the world's global data was worth more than $3 trillion in 2017, an amount that has grown steadily over the past five years.
Data engineers, in this case, are one of the key players in building and designing the system to collect, store, and analyze bid data that is—of course—important for every industry.
In simple words, data engineering is the process of creating and maintaining systems that collect, store, and analyze large-scale data. As we all know, data is a key thing that has relevance for almost every sector, no matter how small or large.
Organizations worldwide have huge amounts of data, and they require the right people and technology to ensure it is in good shape for data scientists and analysts to use.
What is a data engineer?
A data engineer is an IT professional who prepares data for analysis or operation. These software engineers are mainly in charge of building data pipelines that connect information from different sources. They merge, clean and structure data for analytics applications. They make data more accessible and improve their organization’s big data ecosystem.
The amount of data an engineer handles depends on the organization's size. The larger the company, the more complex the analytics architecture and the more data the engineer will handle.
Some industries have more data-intensive needs, like healthcare, retail and financial services. On the other hand, data engineers collaborate with data science teams, enhancing data transparency and enabling businesses to make more reliable business decisions.
What does a data engineer do?
Data engineers build the foundation of a database and its architecture. They evaluate various requirements and apply suitable database techniques to create a solid architecture. Then, the data engineer starts the implementation process and develops the database from the ground up.
Data engineersengineers also perform regular testing to detect bugs or performance issues. A data engineer is responsible for maintaining the database and ensuring that it runs smoothly without any disruption. When a database fails, it affects the related IT infrastructure.
The expertise of a data engineer is especially needed to manage large-scale processing systems where performance and scalability issues need constant maintenance.
Data engineers can also assist the data science team by creating dataset procedures to help with data mining, modeling, and production. In this way, their role is essential in improving data quality.
Roles and responsibilities of a data engineer:
- Import data from RDBMS to HDFS using SQOOP for data reliability.
- Automat test, analysis, plotting and reporting with Python scripts.
- Use Linux shell scripts to automate the build process and file transfers between hosts.
- Develop SAS programs to automate manual testing procedures and increase audit efficiency.
- Manage warehousing operations and parallel processing with the Teradata database system.
- Configured and managed the JobScope ERP system for make-to-order/make-to-stock design and manufacturing.
- Develop SSRS reports for SSAS database documentation and data warehouse data dictionary.
- Use Java scripts, CSS, and HTML for web pages.
- Program core Java application with Eclipse IDE.
- Use DataFrame API in Scala to convert distributed data into named columns.
- Import and export data into HDFS and RDBMS using SQOOP.
- Develop SSAS multidimensional cubes from the data warehouse.
- Define OOZIE job flows.
- Connect to the database with SQOOP.
- Use Eclipse as IDE for development.
Why should you pursue a data engineering career?
If you are looking for a fulfilling and challenging career, you must consider data engineering. As a data engineer, you will play an important role in your organization’s success, making data more accessible and useful for data scientists, analysts, and decision-makers.
To succeed in this career, you will use your programming and problem-solving skills to create solutions that can handle large amounts of data.
And there is no doubt that data is becoming a lifeblood for every organization in the world. As an Indian citizen, you must know that the Indian government supports the Digital India program to boost digital development.
As data plays a crucial role in many industries, the demand for professionals who can understand, manage, and analyze data will likely increase.
How to become a successful data engineer?
Many data engineers have computer science, information technology, or applied math backgrounds. A formal degree, such as from a university or college, can help you develop the math and data skills you need to deal with complex tasks in this rapidly changing field.
You can also pursue a postgraduate degree to improve your career and make more money. Apart from earning a degree, there are a few other things you can consider to achieve your goals.
Online degree program:
To become a data engineer, your first move can be to earn an online degree. There are many bachelor’s online degrees, such as mathematics, computer science, physics or engineering, that you can take into consideration.
Apart from the bachelor's degree program, you can enroll in a master’s degree program such as computer science or computer engineering.
Technical skills:
Coding: You need to be good at coding languages for this role. Some common programming languages you must be good at are SQL, NoSQL, Python, Java, R, and Scala.
Relational and non-relational databases: Databases are one of the most common solutions for data storage. You should know both relational and non-relational databases and how they work.
ETL (extract, transform, and load) systems: ETL moves data from databases and other sources into a single repository, like a data warehouse. Some common ETL tools are Xplenty, Stitch, Alooma, and Talend.
Data storage: Data storage is another important skill. Working as a data engineer, you should keep in mind that some types of data should be stored differently. As you design data solutions for your organization, you’ll need to know when to use a data lake versus a data warehouse.
Automation and scripting: Automation is important to work with big data because organizations can collect so much information. As a data engineer, you must write scripts to automate repetitive tasks.
Data analytics and business intelligence systems: You will implement operational system data flows.
Machine learning: Nowadays, Machine Learning (ML) is a hot skill. To become a data engineer, you must possess this skill to understand the basic concepts better in order to understand the needs of data scientists on your team.
Big data tools: A data engineer doesn't work with regular data and manages big data daily. However, they utilize tools and technologies like Hadoop, MongoDB, and Kafka.
Data security: Data security is also an important chapter when it comes to becoming a data engineer. In some companies, there may be dedicated data security teams, but data engineers also need to manage and store data securely.
Presenting findings to non-technical audiences: As a data engineer, you must be able to describe what you are designing or fixing and why it will benefit the organization.
Earn certifications: One of the most suitable ways to become a successful data engineer is to earn the relevant certification programs. The next section teaches you about the best data engineer certifications.
What are the best data engineer certifications?
If you want to stand apart from the crowd as a data engineer, you can earn data engineer certification. Certifications generally validate that you have the right skills and knowledge to handle big data.
To help you choose the best certification for your career goals, we have compiled a list of the most in-demand data engineer certifications:
Google Professional Data Engineer
The Google Professional Data Engineer certification validates your ability to create, manage, secure, and monitor data systems. You must pass a two-hour exam consisting of multiple-choice and multiple-select questions.
The Google Professional Data Engineer certification exam has no prerequisites. Still, Google suggests you have at least three years of industry experience, including one year working with Google Cloud Platform solutions. You can take the exam online from anywhere or at a testing center in English or Japanese.
IBM Certified Solution Architect – Cloud Pak for Data v4.x
The IBM Certified Solution Architect – Cloud Pak for Data v4.x certification demonstrates your skills to design, plan, and architect a hybrid cloud solution that involves data and AI.
You can lead and guide the implementation and operationalization of a solution that may include data governance, analytics, data science, machine learning, and AI. You must pass a test with six sections and 63 multiple-choice questions.
IBM Certified Solution Architect – Data Warehouse V1
The IBM Certified Solution Architect – Data Warehouse V1 certification proves your ability to design, plan, and architect a data warehouse solution.
You must have a working knowledge of data governance, data processing approaches, data stores and virtualization, real-time processing solutions, and more.
To earn the certified IBM Certified Solution Architect – Data Warehouse V1 professionals badge, you need to pass the exam with seven sections and 62 multiple-choice questions.
Amazon Web Services (AWS) Certified Data Analytics – Specialty
The AWS Certified Data Analytics – Specialty certification shows your technical skills and experience in AWS data lakes and analytics services.
It tests your ability to identify AWS data analytics services and understand how they work together. This certification also tests your ability to know how AWS data analytics services fit in the collection, storage, processing, and visualization data life cycle.
The AWS Certified Data Analytics – Specialty certification used to be called AWS Certified Big Data – Specialty, and it is valid for three years from the date you pass the exam.
Cloudera Data Platform Generalist Certification
Cloudera has replaced its CCP and CCA certifications with the new Cloudera Data Platform (CDP) Generalist Certification, which tests your proficiency with the platform.
The new exam covers general knowledge of the platform for different roles, such as administrator, developer, data analyst, data engineer, data scientist, and system architect.
The Cloudera Data Platform Generalist certification exam has 60 questions; you have 90 minutes to finish it. Other specialized certifications include CDP Administrator – Private Cloud Base, CDP Data Developer, CDP Data Analyst, and CDP Administrator – Public Cloud.
Data Science Council of America (DASCA) Associate Big Data Engineer
The DASCA Associate Big Data Engineer certification is among the most sought-after certifications. This certification validates your knowledge of popular big data platforms, such as Hadoop and Spark, and your knowledge of various developer tools, both proprietary and open source (such as HBase, Hive, Pig, and HiveQL).
You must pass an online exam with 75 questions to earn this certification. There are three ways to qualify for the exam based on your education and work experience.
Data Science Council of America (DASCA) Senior Big Data Engineer
Another best certification from the house of DASCA is the DASCA Senior Big Data Engineer certification.
The Data Science Council of America (DASCA) Senior Big Data Engineer certification is designed for experienced professionals who want to advance their skills.
To earn this certification, candidates must pass an exam of 85 questions. There are four ways to qualify for the exam based on your education and work experience.
SAS Certified Data Integration Developer
The SAS Certified Data Integration Developer certification program validates your data integration development skills in the SAS 9 environment.
The SAS Certified Data Integration Developer certification program covers how to define the platform's architecture for SAS Business Analytics, create metadata for source and target data, work with transformations, and more. This certification program requires you to pass the SAS and Pearson Vue certification exam.
What is the career path for data engineers?
Data engineer (entry-level ):
To begin your career as a data engineer, you will need a bachelor’s degree in computer science or a related field and some basic skills in programming languages, databases, and big data technologies. A data engineer works on simple data engineering projects under the supervision of senior data engineers.
Junior data engineer:
As you gain more experience and knowledge in data engineering, you will become a junior data engineer. You will have more proficiency in one or more programming languages, databases, and big data technologies. As a junior data engineer, you will work on more challenging projects and have more autonomy in designing and implementing data solutions.
Senior data engineer
You will reach the senior level after several years of working as a data engineer. You will have expertise in multiple programming languages, databases, and big data technologies. In this position, you will lead projects and teams of data engineers and create complex data solutions.
Lead data engineer
If you have demonstrated leadership skills and extensive experience in data engineering, you can become a lead data engineer. As a lead data engineer, you will oversee a team of engineers responsible for designing and implementing data solutions across the organization.
Data architect
A data architect is a role that focuses on designing and creating data architectures that support the organization’s business objectives. A data architect works closely with business stakeholders and engineers to ensure the data solutions are scalable, reliable, and secure.
Data infrastructure manager
When it comes to the data infrastructure manager role, these managers focus on managing the organization’s data infrastructure, including databases, data warehouses, and big data technologies. They manage data engineering teams and collaborate with other IT departments to ensure that the data solutions are integrated with other systems.
Chief data officer
A chief data officer is a senior executive in data engineering. In this role, you manage the organization’s data strategy and ensure that the data is used effectively to support business goals. Also, you oversee the functions of data engineering, data analytics, and data governance and work closely with other executives to ensure that the data is a strategic asset for the organization.
The final say
Undoubtedly, data engineering is a hot career path. In this field, you will need different skills and dedication to solve real-world problems and build the right solutions based on the data.
To boost your career and enhance your knowledge base, you must consider earning the data engineer certification.
In this blog post, we have worked hard for you and selected some of the best data engineer certifications you can get in the industry.
The data engineering certification mentioned in this post will validate your knowledge and skills in data management and analysis and make you stand apart from the crowd.
So, if you are looking for a reliable proxy exam center for data engineering certification, you have come to the right place. CBT Proxy is your one-stop destination for any IT certification and has been in this field for over a decade now.
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