Introduction to Data Engineering Preparation Strategies

Undoubtedly, Data Engineering has enormous opportunities and is a demanding field from small to large businesses. Encapsulating data engineering with big data fuels your chances with a much higher package and job security. There are more than 4000 data engineering jobs on LinkedIn. 

With a fancy salary there are various job responsibilities for data engineers. Earlier, there were no separate courses or lessons to enter and provide services in the data engineering field ; a professional with a technical background with learning from experience becomes a data engineer.

But, nowadays, it is not that way. There are entry-level jobs for data engineers and you can directly start to work as a data engineer after completing your graduation with sharpening data engineering skills and knowledge.

With the increasing job vacancy graph for data engineers, you can easily pick a job with a good salary, but for this you need to learn data engineering, this gives birth to the problem; how to become a data engineer. What are the data engineering preparation strategies? 

With this blog, I am presenting a pathway for data engineering preparation strategies. This roadmap is easy to implement and will help you to learn from 0 to data engineer.

Before starting with the data engineering preparation strategy, first know: Whether you are fit for the role of Data engineering. 

Let’s take a quick test based on the following criteria:

• Are you interested in learning and working with new technologies and tools of big data and data mining?
• Enjoy building new infrastructure and discovering new ways to make the software more efficient and reliable?
• Passion and interest in code in Java or the new emerging language: Python?
• Are you ready for the roller-coaster ride of the data engineer job role, a bridge between software programmer and data scientist?
• Have strong database knowledge and interest?
• Have a Degree in CS Engineering or data engineering course?
• With all these last but not least are Determination and Interest.

If your answers to all the above questions is Yes, then you are ready to be welcomed in the data engineering field.

Before providing the best and easy roadmap for data engineering preparation strategies, I would like to describe what data engineering is and why to choose it.

Let’s start…….

Overview of Data Engineering

Data Engineering is a Computer Science discipline that includes storing, processing, and building raw Data infrastructure for use in other levels. According to the name, it is the engineering of Data. 

The one who performs all the operations of data engineering is known as a Data Engineer. Its main purpose is to develop a data architecture like a database so that data scientists can use it to analyze and draw hidden data patterns.

Data is uploading now and then, and small to large-scale companies need data engineers to:

• Remove duplicate data; data redundancy.
• Convert all raw data into a uniform format and make a data architecture for use by anyone.
• Collecting and Processing raw data from various sources.
• Removing errors and making them interpretable in all formats.
• Perform ETL operations; Extract, Transform, and Load the data.
• Improving data quality by processing with new methods and tools.
• Focus on building robust data infrastructure to meet all business requirements and help data scientists in data analysis.
• Work in a team, closely related and involved in the working of software developers and data scientists.

The data engineer works in collaboration with software engineers and data scientists. Data engineers are a bridge to programmers and data scientists, so they need to know programming languages and databases.

Difference Between Data Engineer and Data Scientist

Both data scientists and data engineers work together to complete their operation of making data-driven decisions. But both have different tasks.

Sometimes, data engineers are incorrectly interpreted as data scientists or vise-versa. Let me clear the differences between both of these job roles.

S.No	Title	Data Engineer	Data Scientist
	Key Responsibility	They focus on developing and maintaining data infrastructure for analysis.	Provide insight of the data, that helps in data driven decision making.
	Primary Goal	Data Engineers focus on ETL i.e Extract,Transform,and Load of data.	Data Scientists focus on analyzing the data and finding hidden patterns in the data.
	Dependency	They are independent and work on removing human and machine errors from the raw data.	They are dependent on Data engineers and will start work only when data engineers complete their task.
	Programming Knowledge	Need strong programming knowledge.	Familiar with python and R languages.

RoadMap to Data Engineering Preparation Strategies

The shortage of data engineering professionals created a massive demand for them. With increasing data uploading speed and more internet dependency, companies want professionals who can build a robust data infrastructure with uniformly formated data that data scientists can use to provide data insight. 

It’s right that there is great demand and a good package for data engineers, but the obstacle is what are the data engineering preparation strategies? 

Here, in this section, we will discuss the central area of this blog, here is a straightforward pathway for data engineering preparation strategies:

The various steps of Data Engineering preparation strategies are as follows:

1. Skills for Data Engineering

To learn the concepts of data engineering, you need to build a strong foundation for Computer Science terminologies and be familiar with the working of the internet. You need to know how to work with Git versions like GitHub, GitLab, or Bitbucket. The control on Git versions will help you update with the coding changes.

2. Programming Language

Knowledge of programming
languages is mandatory for data engineers, they need to know coding in one of the programming languages, like: Java, python, and Go. 

But nowadays, the most required programming language is Python due to its use in data science and ease of building data pipelines. The libraries of Python, like NumPy and Pandas, are vital and play an essential role.

In building data pipelines and using Hadoop, data engineers need to code and understand the software’s coding. 

You should get a strong command of one of the coding languages like Python or Java. Well, I recommend Python.

3. Testing

Data engineers also need to possess software testing skills. They should have knowledge of SDLC (Software Development Life Cycle) and STLC (Software Testing Life Cycle). Data engineers develop data architecture that needs to be tested with unit testing, integration testing, and functional testing. 

You can quickly learn these basic concepts with defined operational modes like manual testing or automation testing.

4. Master Database

You should learn the terminologies of relational databases and database languages like MySQL, PostgreSQL, MariaDB, or Amazon Aurora. You can master any of them. 

To interact with the data, query the data, and perform ETL tasks, you need SQL(Standard Query Language) or another to build a robust platform along with ER diagrams and normalization.

You should know the following:

Database patterns

Horizontal or vertical Scaling

CAP theorem

OLTP or OLAP

Learn the concepts and working of the data warehouse; there are various tools for data warehouses like Snowflake, Google BigQuery, Amazon RedShift, and more. I recommend you go with Google BigQuery as it is in demand and more secure.

5. Big Data Tools

With massive data and complexity, data engineers work on big data, defined as data of data. You can learn the working of the latest big data tools like Apache Spark, Apache Hive, Kafka, Apache Airflow, etc. Master any of them to get a beautiful job with a handsome salary.

6. Machine Learning

For Data engineering preparation strategies, you need to focus on developing Machine Learning terminologies while working on TensorFlow, which will add stars to your resume.

Data engineers work parallely with data scientists, and ML is one of the integral parts of their work; having knowledge of ML tools and techniques will help you to work comfortably.

7. Cloud Computing

Cloud Computing is attracting companies with its easy-to-use and economic services. You need to be familiar with cloud computing terminologies, their services, and their various benefits. So, if your organization is working on a cloud platform, you can easily do your task of ETL and more.

You build the knowledge of working on AWS and Google cloud as they are most used.

8. Professional Certification for Data Engineering

After collecting all the basic requirements and knowledge of tools, you can apply for some certifications to test your eligibility. 

1. AWS certification
2. CDP (Cloudera Data Platform) Generalist Certification
3. DASCA (Data Science Council of America) Associate Big Data Certification
4. Google Professional Data Engineer
5. IBM Certified Solution Architect- Cloud Pak for Data v4.x.

These certifications will help you get a job easily and measure your skills. Grab any of these or other certifications to make your journey as a professional data engineer easy.

8. Start with Internship

After building the base and gaining all the needed knowledge of data engineering, you can start your journey in data engineering with an internship. The internship helps you to get industry knowledge and experience. You will get an understanding of the job roles and responsibilities of the data engineers, and this will be your nearest step to the job for data engineering preparation strategies.

Various MNCs, like TCS, Capgemini, Wipro, etc., also provide internship facilities with certificates.

Interview Tips in Data Engineering Preparation Strategies

Getting the first job has no predefined path; you can find a job on various online job portals. There are different sub-titles under the main title data engineers like cloud data engineer, data architect, and big data engineer. Find your perfect match as per your skills.

Moving ahead, an essential step in the data engineering preparation strategy is the interview, after taking all courses and building a solid knowledge base of data engineering with all its tools and technical skills. 

The next step is the interview. The pre-interview steps include:

1. Professional Resume

A resume is the first impression of a candidate; any recruiting company will make a rough sketch of your resume before the interview. So build a highly professional resume with a straight-to-point description of your experience and capabilities.

To build a professional resume, you can take references from Google, friends, or technical experts.

2. Company Profile

To screw the interview round, first, thoroughly research the company. Get answers to all the questions like What is the company’s work profile? When was the company established? What is the motive of the company? Why do you fit this role, and why does the company hire you? 

3. Brush up all Data Engineering Concepts

Before reaching the interview gate, revise all your data engineering concepts with a particular focus on programming language, SQL fundamentals, and working with the knowledge of the latest big data tools, ML models, and database queries.

Revisit all the technical concepts and prepare to do the best in the technical round. You can also search Google for the mostly asked technical questions for data engineering.

4. Coding

The foremost requirement of Data engineering preparation strategies is to have a robust programming language holding, with a particular preference for Python.

Before the interview, go through all priority syntax and meanings of defined methods and packages in Java, Libraries in Python.

5. Confidence

If you are confident, no one can stop you. In case you fail to answer some questions, don’t be nervous, keep calm, and have a positive attitude with full confidence to face your pitfalls.

The determination and willingness for the job will help to easily crack an i
nterview, and no one is 100% perfect. Show enthusiasm, that yes, I will do with my best possible efforts.

You can also Read Related Topics:- Data Science & Artificial Intelligence

Learn How to Prepare for Data Science interview :- Data Science Interview Series

Conclusion:

There is a good demand for professional data engineers, and the industry needs more data engineering experts. As per the latest reports, data engineering is the new face of the IT and non-IT industries.

Through this blog, we tried to provide you with the best path-defining data engineering preparation strategies. If you are interested in data and keep learning innovations, tools, and methods of refining data, this highly demanding data engineering field is for you.

Have a bright future and an easy learning path.

Frequently Asked Questions

1. How stressful is data engineer?

With the correct and result-oriented data engineering preparation strategies, it will be easy to become an industry ready data engineer. There is no stress in learning data engineering if you are interested in this field.

Undoubtedly, data engineering demands lots of learning with learning programming languages and database knowledge. Along with learning, this job has immense possibilities and requirements in the industry. Earlier, there were no separate paths or courses to be a data engineer, a person with an IT background and experience becomes a data engineer, but now the picture has turned 360 degrees.

By following these data engineering preparation strategies, you can easily master this field:

1. Learn Data Engineering Basics
2. Learn Programming languages
3. Learn Database language
4. Learn Data Warehouse tools
5. Learn Cloud Computing tools
6. Learn ML concepts

2. What are some common problems faced by data engineers?

The industry is ready to welcome skilled data engineers, but this field is very challenging due to workload and changing tools. The unstructured and vast data made it difficult and mind-warming to handle increasing job responsibilities.
The next problem is proper data engineering preparation strategies. There are multiple jobs, but how to become an industry ready data engineer, there is no defined pathway that helps to become a data engineer.
You need to follow very clear data engineering preparation strategies to be a data engineer, and this includes: learning data engineering concepts, programming language, SQL, data warehouse tools, working with relational databases, testing, and always learning new technologies.

3. How can I start learning data engineering?

You can start learning data engineering by enrolling in some data engineering courses and following data engineering preparation strategies. Those data engineering preparation strategies will guide you at every step and provide a roadmap to reach the ultimate destination of data engineering.

The essential steps of data engineering preparation strategies are as follows:

1. Start learning the CS and internet terminologies and working.
2. Learn Java or Python.
3. SQL will help you to query the database.
4. Knowledge of data warehouse and cloud computing tools.
5. Knowing Machine Learning Modeling and tools is a benefit.
6. Keep updating and learning.

4. Can I become a data engineer with no experience?

With no experience, you can start as an entry-level data engineer, but you should have experience working with data-related jobs. If you completed some internship, it would be a plus point.

The easiest way to enter the data engineering field is to start with an internship, where you learn and adapt to the industry’s work culture. Internships related to data engineering, data science, or any data-related profile will help you get your dream job as a data engineer.

To become a data engineer, you must follow good data engineering preparation strategies that provide you with a complete path.