What is Data Engineering?

Big data has grown so much that it has gained importance like never before. Big data is not an old term, but the data sets have been used for decades in the form of spreadsheets and databases. The growth of social media websites and digitalization has promoted the growth of Big data since 2005.  So big data is used by almost every organization to make better decisions for their customers, better business decisions, optimizing and understanding business processes, etc.

According to Statista. By 2022, annual revenue from the global big data and business analytics market is expected to reach 274.3 billion U.S. dollars. The major providers of big data are Oracle, Microsoft, SAP, IBM, etc. This shows that in the upcoming future, Big data is expected to be used even more, and contributing to greater revenue for the world.

Some of the advanced technologies, such as machine learning, artificial intelligence, and data science, rely heavily on big data. You might be very well aware of how important these technologies are today. So, let us know what data engineering is all about and what options do you have if you are seeking data engineer training.

What is Data Engineering?

Data engineering is a process of finding the patterns and trends in raw data. This is possible with the help of algorithms that are specially constructed in the view of getting information. The goal of data engineering is to provide organized, data-driven work which can be made possible by training machine learning models, exploratory data analysis, populating fields with outside data.

A career in Data Engineering

Below are some details as to what all is required to become a data engineer and the career outlook of it.

Skills required to become a data engineer

  • Structured Query Language(SQL)

SQL forms a very important skill while managing DBMS (Database management systems). You need to learn this very well enough to optimize queries.

  • Data Warehousing

Data warehousing is all about collecting resources from various resources. Then all of this data is compared to get better information.

  • Data Architecture

Data engineers must have the required knowledge to plan, create, and maintain data architectures.

  • Programming Languages

If you are a tech aspirant, coding is a part definitely not to be missed. The same is with data engineering. Programming languages such as R, Python, etc., are mostly used in machine learning algorithms so that a variety of applications (web, mobile, desktop, IoT) can be developed.

  • Operating systems

Some of the operating systems, such as UNIX, LINUX, Solaris, and Windows, are mostly used to host applications.

  • Apache Hadoop-based analytics

Apache and Hadoop are the open-source platforms used mainly for computing distributed processing and storage against datasets. Operations such as data processing, access storage, security, governance, operations, and security. Hadoop, HBase, MapReduce can also be used to upgrade your skillsets.

  • Machine Learning

As a data engineer, you can require the skills of machine learning for the purposes of data modeling and statistical analysis.

Data Engineer Roles and Responsibilities

  • Data Architecture

Data architectures are planned, created, and maintained while keeping them aligned with business requirements.

  • Data collection

Getting the right data can be equally challenging, so the data engineer’s responsibility is to get it from the right and multiple resources.

  • Research

Conducting research is an important part of data engineering because it helps in addressing issues that can occur while solving any problem.

  • Learn advanced skills

It is important for data engineers to keep themselves updated with the essential skills and techniques. So they must make themselves aware of some machine learning algorithms like the random forest, decision tree, k-means, and others. Some of the analytics tools like Tableau, Knime, Apache spark generate insights for all types of industries.

  • Identify patterns

After a sufficient amount of data has been collected and used, it is time for the data engineer to use more of it. Thus, the data engineer must find patterns and use machine learning algorithms. Another part is creating models to find future trends from the stored data.

  • Automate tasks

Automation is an advanced technology in itself whereby repetitive tasks are automated so that human efforts can be put on better areas. It is known to save time and money once automation is executed.

Data engineer courses

Some of the courses that people prefer to learn data engineering are briefly described below:

  • A postgraduate program in data engineering by Simplilearn

This program is offered by Simplilearn in partnership with Purdue University and collaboration with IBM. It covers big data and engineering concepts, the Hadoop ecosystem, AWS EMR Kinesis, Apache Python basics, Sagemaker, Quicksight, the AWS platform, and Azure services.

  • Data engineer Nano degree program by Udacity.

This nanodegree program by Udacity will help you create data models, automate data pipelines, data lakes and work with massive datasets. This also has a capstone project that needs to be made by the end of the course.

  • Coursera

Coursera offers various courses to learn data engineering based on the difficulty level and the topics covered. Some of the popular ones are data engineer foundations, data engineering with google cloud, data engineering, big data and machine learning on GCP, introduction to data engineering.

  • Become a Data Engineer: mastering the concepts by LinkedIn

This course is offered by LinkedIn that offers concepts and tools by which you can master these skills and apply them to real-life challenges. Some of the concepts covered SQL, Apache spark, cloud NoSQL, etc. 

  • Introduction to Data Engineering by DataCamp

It is offered by Datacamp, which aims at making you learn all the basic concepts and analytical tools that will lay a strong foundation in your data science career.    

List of popular certifications of Big Data

Certifications are known to add value to your career. Some of the popular certifications of big data are listed below.

  • Cloudera Certified Associate (CCA) Spark and Hadoop Developer
  • Cloudera Certified Professional (CCP): Data Engineer
  • Data Science Council of America (DASCA) Associate Big Data Engineer
  • Data Science Council of America (DASCA) Senior Big Data Engineer
  • Google Professional Data Engineer
  • IBM Certified Data Engineer – Big Data

As big data and data analytics have gained popularity over the years. It is obvious that the professions related to big data are also offered attractive salary packages. So, according to Payscale, the average data engineer salary in India is ₹825,899/ year.

As you are now very well aware of data engineering and its importance, you must be curious to know more about it. If you are a tech aspirant looking for a wonderful career, then definitely go for data engineering. Enroll yourself in a course and get certifications associated with it.