Data Engineering for Beginners: A Step-by-Step Guide
Published: 2023-11-11 12:39:25

## Introduction

The data age has come with challenges that require innovations and the development of existing technological services to handle huge amounts of data. Data from various sources also require faster processing to make it available for different use cases. Therefore, data engineering has become part of the ecosystem in different organizations, especially those handling huge streaming data. It is now common to find organizations setting up a team of data engineers to ensure they properly capture the data as it streams in for the different cases.

## What is Data Engineering?

Data engineering refers to designing, building, and maintaining the data infrastructure required to collect, store, process, and analyze large data. This data may usually come from various sources, and then it gets to a centralized warehouse for processing and storage for other uses by the different data engineering teams. Hence, a data engineer is a professional tasked with conducting the data engineering process to ensure quality and availability.

## Roles and Responsibilities of Data Engineers

Below are some of the core responsibilities handled by data engineers;
1. Designing and deploying data pipelines to extract, transform, and load (ETL) data from various sources.
2. Managing data warehouses to store huge volumes of data and scale the data warehouses and data lakes to perform optimally.
3. Database design and data modeling handle different data types ingested in the data warehouse.
4. Collaboration with analytics team members, such as data scientists, to ensure efficient data collection, proper data quality checks, and data analytics.
5. Monitoring and maintaining the built data pipelines to ensure accuracy and consistency in data processing and ingestion.

## Skills for Data Engineer

Aspiring data engineers need the following skills to become proficient in the process.

**1. Programming languages (Python and SQL):** Python is necessary for writing code to automate workflows. SQL is also important for querying data from databases. 
**2. Databases:** Database knowledge is important for understanding the different types, such as structured and NoSQL databases. A data engineer should know when to implement the use of each type with the necessary tools.
**3. Data warehousing:** Data warehousing is necessary to build databases for handling large data. This knowledge should also include learning Amazon Redshift and Google BigQuery to handle large warehouse data.
**4. ETL processes:** Learning the extract, load, and transform process helps determine how to fetch data from the different sources and prepare for different use cases.
**5. Big data frameworks:** A data engineer should also learn to manage big data using frameworks such as Apache Spark and Apache Hadoop.
**6. Data pipeline orchestration:** Data pipeline orchestration is necessary with tools such as Apache Airflow to manage the workflow. This process ensures data will move through different stages smoothly to the required database.
**7. Data modeling and design:** A data engineer should learn data modeling and design to know how the different data relate to each other and where and how to store the information.
**8. Streaming data:** Data engineers also need tools like Apache Kafka for real-time data streaming.
**9. Infrastructure and cloud services:** Know about platforms like AWS, Microsoft Azure, and GCP, where you can use computers in the cloud to help manage and store your data without needing your servers.
**10. Data quality and governance:** It is also important for data engineers to ensure data is accurate and reliable by implementing the best data quality practices. Besides, implementing data security ensures data is protected from security breaches.

## Summary

Data engineering is vital for most organizations that deal with big data and require automation and consistency in data collection, preparation, and analysis. There is also an increasing demand for data engineers, and data availability pushes most of these organizations to set up the infrastructure to handle the information. Thus, aspiring data engineers need to understand the basics of data engineering to build reliable data pipelines.