Data Engineering for Beginners: A Step-by-Step Guide
Tags: beginners, datascience, data
Published: 2023-10-26 12:34:04


**Introduction**
Even before the role of a data scientist and the analysis part we have the data engineering part. Data engineers are vital parts of any data science project. The engineer should create a framework in place for the data scientist. Data engineering is not so much about a path to it but skills one needs to have in order to be one.

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale.
A data engineer is a technology professional who builds storage solutions for vast amounts of data. The ability to design and build data warehouses is among the top skills clients look for in data engineers. Data warehouses reduce the cost and size of all the tasks a data scientist does.
ETL (Extract, Transform, and Load) are the steps which a data engineer follows to build the data pipelines. ETL is a blueprint for how the collected raw data is processed and transformed into data ready for analysis.

**Roles in Data Engineering**

**Work on Data Architecture**
They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. This role requires knowledge of tools like SQL, XML, Hive, Pig, Spark, etc. 

**Database Administrator**
 A person working in this role requires extensive knowledge of databases. Responsibilities entail ensuring the databases are available to all the required users, is maintained properly and functions seamlessly when new features are added.

**Data Engineer**

Data engineers don’t rely on theoretical database concepts alone. They must have the knowledge and prowess to work in any development environment regardless of their programming language. Similarly, they must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others.  They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries.

**Data Engineer Roles and Responsibilities**
Here is the list of some roles and responsibilities a data engineer might be expected to perform:

**1. Work on Data Architecture**
They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. 

**2. Collect Data**
Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data. 

**3. Conduct Research**
Data engineers conduct research in the industry to address any issues that can arise while tackling a business problem. 

**4. Improve Skills**
Data engineers must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. 

They should be proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights.


**Skills Required to Become a Data Engineer**

**1. SQL**

SQL serves as the fundamental skill-set for data engineers. You cannot manage relational database management system without mastering SQL. You will need to go through an extensive list of queries and how to issue optimized queries.

**2. Data Warehousing**

Get a grasp of building and working with a data warehouse . Data warehousing assists data engineers to aggregate unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations.

**3. Data Architecture**
Data engineers must have the required knowledge to build complex database systems for businesses. It is associated with those operations that are used to tackle data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.

**4. Coding**

To link your database and work with all types of applications – web, mobile, desktop, IoT – you must improve your programming skills. Learn an enterprise language like Java or C#. The former is useful in open source tech stacks, while the latter can help you with data engineering in a Microsoft-based stack. The most necessary ones are Python and R.  

**5. Operating System**
You need to become well-versed in operating systems like UNIX, Linux, Solaris, and Windows. 

**6. Apache Hadoop-Based Analytics**
Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations.

**How to Become a Data Engineer**

Below are some of the ways one can use to become a data engineer:

**Certifications**
Consider obtaining certifications in data engineering, such as AWS Certified Big Data - Specialty, Google Cloud Professional Data Engineer, or Microsoft Certified: Azure Data Engineer Associate. This will help you to demonstrate your expertise to potential employers.

**Education**
Most data engineering roles require a bachelor's degree in computer science, software engineering, or a related field. A degree in mathematics or statistics can also be helpful. 

**Build a Portfolio of Data Engineering Projects**
Gain hands-on experience working on data engineering projects. You can start with open-source projects or participate in hackathons and coding competitions.

**Technical Skills**
You need to be proficient in programming languages like Python, Java, and SQL. They must also be familiar with big data technologies like Hadoop, Spark, and Kafka and experience with cloud computing platforms.

In conclusion data engineering like any other data field requires grit, willingness to learn and persistence. In the long run it will pay off.