The Data Engineer’s Journey: A Comprehensive Road Map for Beginners in 2023
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Welcome to the exciting world of data engineering! As a beginner or “noob,” you may feel a bit overwhelmed by all the new concepts and technologies you need to learn. But don’t worry, with the right guidance and a bit of hard work, you can become a skilled and successful data engineer in no time.
Data engineering is vital in today’s data-driven world, and those who excel at it play a crucial role in collecting, storing, and processing large amounts of data to support business decisions and drive innovation.
This Beginner Level Road is divided into 4 Steps. To make it easy for you, I will also Explain about Skills. Why you will learn them and how they will benefit you in your career. As most people Follow Roadmaps even without knowing why they are actually learning these particular skills.
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This article is for those who are new to this field or wanna shift their career to Data Engineering. For those who have experience of 1 to 3 years and are looking for New things. Don’t just go back. Read the intermediate Road Map here:
Before I move to the main topic I wanna point out the 5 COMMON MISTAKES people make while moving to or starting their career in Data Engineering.
1. Not fully understanding the role & responsibilities of a data engineer:
Data engineering involves more than just writing code. It requires a deep understanding of data architecture, data pipelines, and data storage and retrieval systems.
2. Underestimating the importance of foundational skills:
Data engineering involves a wide range of skills, including programming, database management, and data modeling. It’s important to have a solid foundation in these areas before attempting more advanced tasks.
3. Not keeping up with the latest technologies and trends:
The field of data engineering is constantly evolving, with new technologies and approaches emerging all the time. It’s important to stay up-to-date on the latest developments in order to remain competitive and effective in your role.
4. Not seeking out opportunities for hands-on experience:
The best way to learn data engineering is through real-world experience. Seek out internships work on projects, and other opportunities to apply what you’ve learned in a practical setting.
5. Not seeking out a mentor or support network:
This is the most important One. Data engineering can be a challenging field, and it’s important to have a support network of experienced professionals who can offer guidance and advice as you navigate your career. Don’t be afraid to seek out mentors or join online communities and forums to connect with others in the field.
So let’s get started on your journey to becoming a data engineer!
4- STEP GUIDE
As I mentioned above, for your understanding this beginner guide is divided into 4 Steps.
Step-1: Languages
There is a total of 4 languages that are widely used in Data Engineering.
» Python: Python is a popular language for data engineering due to its flexibility and a large ecosystem of libraries and frameworks for data analysis, machine learning, and visualization.
» Java: Java is another widely used language in data engineering, particularly for building distributed systems and working with large data sets.
» Scala: Scala is a popular language for data engineering due to its ability to handle large data sets and its integration with the Apache Spark framework.
» R: R is a programming language and software environment for statistical computing and graphics, and is often used in data engineering for tasks such as data wrangling, visualization, and statistical analysis.
TIP: Pick any one of the 4 but if you are totally new to this field and have no Coding Background. I will recommend choosing ‘Python’ due to its vast demand and flexibility and is very easy to learn and understand.
Step-2: Basic Data Structures
» What is Data Structure? It’s a way to organize particular data in a computer. So it can be used effectively.
» Why do you need to have Basic concepts Down? Because for two reasons. The first is to crack Coding Interviews as new in the Industry. Interviewers ask questions about it. Second, it helps us while working and understanding a large amount of data.
» Data Structures Topics: Arrays, Linked List, Stack, Queue, Graphs, Trees, Sorting and Searching Algorithms
Step-3: Fundamentals of Data Bases
There are certain queries and statements that as a beginner one should know.
» Relational Databases: Here one should know, how to:
- Create, Update and Delete Tables
- Insert Data into Tables
- Create Database Schema
- Indexes
- ACID Properties
- Creating Views & Stored Procedure
- Normalization
- Data Models and ER Diagram
These operational also called DDL, DCL, DML, and TCL.
Step-4: SQL (STRUCTURE QUERY LANGUAGE)
SQL is a crucial tool for data engineers as it allows them to work with large datasets, manipulate and analyze data, and design and maintain efficient and reliable database systems.
Step 3 and Step 4 are somehow interconnected. As they both talk about databases.
» Things You Need to know about SQL as a Beginner:
- Create, Update and Insert into Tables
- Join Table and Sub Queries
- Group By and Having Clause
- Case Statement and Window Function
- Type Casting
- Aggregate Functions (Such as Min, Max, Avg, etc.)
» Data Base Management Tools:
These tools provide a range of features and functions that can help data engineers design, build, and maintain database systems, including the ability to create and modify database structures, import and export data, and perform complex queries.
TIP: There are many tools such as MYSQL, PostgreSQL, Oracle, etc. You have to learn one and you can easily understand others.
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