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In the future, the demand for skilled big data engineers will increase rapidly. The reality is that no matter which industry the company belongs to, to succeed in today's competitive market environment, you need a strong software architecture to store and access the company's data. it is best to build it from the very beginning of the company.

Nowadays, where there is data, it is sometimes called big data, which is a bit of an exaggeration. In this paper, it is collectively referred to as data engineer and data scientist.

Let's find out, what exactly does a data engineer do? How does one become a data engineer? We will discuss this interesting area and how to become a data engineer.

What do data engineers do?

The data engineer is responsible for creating and maintaining an analytical infrastructure that supports almost all other functions in the data world. They are responsible for the development, construction, maintenance and testing of big data architecture, such as databases and big data processing systems. Engineer big data is also responsible for creating processes for modeling, mining, obtaining and validating data sets.

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Therefore, data engineers need to master general scripting languages and tools, use and improve data analysis systems, and constantly improve the quantity and quality of data.

What is the difference between a data engineer and a data scientist

Although there is a degree of overlap in skills and roles, the two positions are increasingly divided into different roles.

Data scientists are more concerned with interacting with data infrastructure than with creating and maintaining data infrastructure. Usually responsible for conducting market and business operations research to determine trends and relationships, data scientists interact with and act on data using a variety of complex machines and methods.

Data scientists are usually proficient in machine learning and advanced data modeling because they want to transform raw data into operable and understandable content with advanced mathematical models and algorithms. This information is often used as a source of analysis to give decision makers a "bigger picture".

So what makes a data scientist different from a data engineer? The main difference between the two is the target focus. Data engineers focus more on building data generation and data infrastructure; data scientists focus on mathematical and statistical analysis of the generated data.

Key skills of data engineer

Here are some of the key skills that data engineers need.

1. Tools and components of big data Architecture

Data engineers are more focused on analyzing infrastructure, so most of the skills required are architecture-centric.

two。 In-depth understanding of SQL and other database solutions

Data engineers need to be familiar with the database management system, in-depth understanding of SQL is very important. You should also be familiar with other database solutions, such as Cassandra or BigTable, because not every database is built by recognizable standards.

3. Data Warehouse and ETL tools

Experience in data warehousing and ETL is critical for data engineers. Data warehouse solutions such as Redshift or Panoply, as well as ETL tools such as StitchData or Segment, are useful. In addition, data storage and data retrieval experience are equally important, because the amount of data processed is astronomical.

4. Analysis based on Hadoop (HBase,Hive,MapReduce, etc.)

A deep understanding of Apache Hadoop-based analysis is a very necessary requirement in this field, and knowledge storage for HBase,Hive and MapReduce is generally necessary.

5. Coding

When it comes to solutions, coding and development capabilities are an important advantage (which is a requirement for many positions), and it can be very valuable for you to be familiar with Python,C/C++,Java,Perl,Golang or other languages.

6. Machine learning

Although data engineers are mainly concerned with data science, they will gain an understanding of data processing techniques, such as some knowledge of statistical analysis and basic data modeling.

Machine learning has become a standard data science, and knowledge in this field can help us build solutions for similar products. Another advantage of this knowledge is that it gives you great market value in this field, because being able to "wear two hats" in this case will make you a more powerful tool.

7. Multiple operating systems

Finally, we need to have an in-depth understanding of Unix,Linux and Solaris systems, and many mathematical tools are based on these operating systems because they have access rights and special hardware requirements that Windows and Mac system functions do not have.

How to become a data engineer?

Compared with other professions, data engineers need to use more complex learning methods. It is usually better for data engineers to have a degree in computer science and technology before further learning vendor-specific certification programs and training courses.

Although a computer-related degree is important, it is only part of the story. It may be very valuable to obtain a suitable certification. There are also some big data engineer certifications on the market, as follows:

Google certified specialist-data engineering. This certification indicates that students are familiar with the principles of data engineering and can be used as assistants or professionals in this field.

IBM Certified data engineer-big data. This certification focuses more on big data specific applications of the data engineering skill set rather than general skills, which is regarded by many as the gold standard.

Cloudera CCP data engineer: this certification is for Cloudera solutions and reflects students' experience in ETL tools and analytics.

Second-tier skills certification, such as MCSE (Microsoft Certified solution specialist), covers a wider range of topics but has specific sub-certifications, such as MCSE: data management and analysis.

Of course, the online education platform provides important training in this field, Udemy offers a wide range of courses in data engineering and data science, others such as EDX and Memrise offer similar courses, DataCamp focuses on data science and engineering, and Galvanize has a wider range of categories.

Summary

Although these data solutions can help you step into the field of big data engineering, although they distribute or grant certification, they only provide certificates or diplomas. Although they have generally learned enough, they cannot be seen as a substitute for actual certification or practice.

It is hoped that this article will clarify the specific knowledge, skills and requirements required by data engineers. This field is developing rapidly, but it is also full of challenges and obstacles. Fill the gaps in the skill set through appropriate certification at work, and achieve the key step of best learning.

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