Fundamentals of Data Engineering

4 min read
18 October 2023

Data Engineering Services

Today’s industrial revolution is digital, mobile, and dynamic. This situation has encouraged organizations to embrace data-driven decisions. However, the reliability of this attitude depends on data quality. So, data engineers ensure the stakeholders get correct, relevant, and practical datasets. This post revisits the fundamentals of data engineering. 

What is Data Engineering? 

Data engineering includes digital infrastructure design, development, testing, maintenance, and upgrading for robust extract-transform-load (ETL) pipelines. Also, data engineers might leverage cloud computing systems and multiple programming languages to deliver data engineering services suitable for enterprise applications. 

An ETL workflow consists of hardware and software components. It can lead to data loss risks due to cybersecurity and file corruption. So, professional data engineers integrate data backup technologies. 

They also customize data sorting and cleansing methods based on analysts’ quality standards. Moreover, global companies will hire data architects to assist data engineers in finding the best tech specifications for a proposed ETL pipeline. 

What Are the Fundamentals of Data Engineering? 

1| Data Storage and Backup 

A small company will have limited data needs. Meanwhile, an enterprise with a global consumer base will need more computing resources. So, data engineers must estimate clients’ data requirements and offer data storage assistance. 

Data engineers provide data lake implementation services to companies planning to undertake big data analytics. A data lake can store extensive and unprocessed data volumes. It is also integral to big data since its storage capacity is scalable. After all, flexible resource configuration powered by multi-cloud virtualization is the salient feature of data lakes. 

2| Cybersecurity 

Corporate espionage, unlike competitive intelligence, is unethical and illegal. However, malicious individuals engage in it to steal other company’s intellectual properties and trade secrets. Likewise, ransomware and phishing attacks can endanger a firm’s confidential resources. 

Employees and consumers are also at risk of data loss, identity theft, and unauthorized bank transactions because of companies’ governance failures. So, data engineering fundamentals emphasize the need for advanced encryption across all communication touchpoints. 

Other measures range from firewalls to virtual private networks (VPNs). Data engineers can also collaborate with IT administrators to block suspicious software and web resources. Besides, maintaining access logs and requiring time-stamped user authentication will help them. 

These methods will prevent analysts and managers from unknowingly exposing ETL components. Nevertheless, stakeholder education regarding data safety and responsible device usage is crucial. 

3| Machine Learning 

Machine learning (ML) recreates the logic in the training datasets to perform a task like humans. An ML model allows for process automation at a modular level. So, data engineers can optimize some ETL components to complete their role irrespective of human supervisors’ availability. 

ML can assist data engineering professionals in reducing the time and effort spent on reformatting raw data into a more structured and well-sorted digital asset. Since unstructured and mixed data increases analysts’ challenges, machine learning streamlines their work. In short, it enables data engineers to develop faster ETL pipelines. 

Consider multilingual and unstructured data objects. A data object can be a descriptive customer review containing words in multiple languages. So, language-based ML models can translate them into one language, identify the review writer’s intended meaning, and label it as positive or negative client feedback. 

Conclusion 

Discoverability, accessibility, resilience against data loss, and extensive automation are the fundamentals guiding data engineering professionals. They demand robust data storage ecosystems, cutting-edge cybersecurity provisions, and modern ML innovations. 

Depending on the enterprise, data engineers work alongside data architects and managers to ensure the ETL pipelines meet the data analysts’ requirements. They employ several coding tools and hardware components to upgrade the infrastructure. 

As technologies like big data and predictive analytics gain momentum, the importance of data engineers and data lake strategies will increase. However, only experienced professionals who understand the data engineering fundamentals can benefit from this trend. 

In case you have found a mistake in the text, please send a message to the author by selecting the mistake and pressing Ctrl-Enter.
David Starc 3
Joined: 8 months ago
Comments (0)

    No comments yet

You must be logged in to comment.

Sign In / Sign Up