Data Engineering For Beginners: A Step-By-Step Guide

Big data has led to increase in demand of real time data processing and analytics. Data engineers play an important role in designing and implementing data pipelines where data travels through from input to storage, therefore a data engineer is a professional technologist who build storage solutions for huge amount of data.

What is data engineering?

Having a great understanding of who is a data engineer, the question could be what is data engineering? Data engineering is a process of designing and implementing systems that collect and analyze data so as to get insight from that data.

What does a data engineer do?

Data engineers have several responsibilities on a day-to-day basics which includes

1. Extracting and integrating data: Data comes from variety of sources such as databases, external APIs among others therefore data engineer integrate data from those sources whether structured or unstructured into a data warehouse.
2. prepare data for analysis: data engineers are responsible of processing the data by applying some transformation, cleaning and validating making data ready for analysis.
3. designing pipelines: a data pipeline is where the data travels through from input to the storage therefore data engineers are responsible for designing and implementing data pipelines to extract, transform, and load (ETL) data from various sources into a centralized data repository.

Tools data engineers should know.

1. scripting and programming language: python is the commonly used language in data engineering due to its simplicity and extensive libraries , which is used in transformation and data cleaning. There are other languages that are used such as ruby, Scala among others.
2. MySQL/PostgreSQL: data engineers use MySQL to store and manage structured data for analytics and reporting.
3. Data visualization tools: to gain insights and patterns from data, a data engineer should be familiar with various tools used in visualization such as tableau and Power BI.
4. Data warehousing and storage tools: the commonly used tool in managing data is snowflake, snowflake is a cloud data warehouse that allow one to store and manage data, snowflake is very flexible since it works with some programming languages such as python

Step-by-step guide

step1: Master the basics

Mastering the fundamentals of data engineering would be the first step. As a data engineer it is advisable to have strong foundations in programming languages such as python and also databases such as MySQL/PostgreSQL, still get to understand data modelling which help in structuring data in a logical manner.

step2: Data manipulation and transformation

Data originates from different sources, therefore data engineer is responsible for extracting ,transforming, loading (ETL) and also cleaning and transforming data to make it ready for analysis.

step3: Getting insights and pattern from data

Data engineers should be familiar with various tools for visualizing the data such as tableau and power BI, so as draw patters and get insights from the given data.

step4: Building data pipelines

Having gotten the insights from data, you design and implement data pipeline where the data will travel through from input to the storage. data pipeline act as a highway for the data. This can be done by help of Apache Airflow to ensure smooth flow of the data.

step5: Data warehousing and data modeling

Data warehousing is the storage system for huge amount of data while data modeling involves organizing data in a logical manner which helps in ensuring efficiency, and consistency throughout the data lifecycle., this can be achieved by the help of snowflake and star schemas.

Conclusion

Data engineering is a critical field that empowers organizations to harness the full potential of their data. As a data engineer you need to have familiarized yourself with basics such as programming, data manipulation that is (ETL), know how to use visualization tools such as tableau or power BI, build pipelines and also get to understand how to structure data in logical manner.

Hope this article gives you a better understanding on how to kickstart with data engineering journey! happy learning.