Chapter 1: Understanding Database Technologies

To thrive as a Data Engineer and enhance your marketability and compensation, possessing specific skills is crucial. This article will delve into these skills, particularly emphasizing database technologies.

The primary responsibility of a Data Engineer is to ensure the delivery of high-quality data from source systems to environments like Data Warehouses, Data Lakes, or Data Lakehouses. Such data typically originates from sources like ERP or CRM systems, social media, or production systems, and is commonly stored in relational databases. However, it can also include unstructured data from NoSQL databases.

This indicates that an effective Data Engineer should not only be proficient in Structured Query Language (SQL) and have a solid understanding of relational relationships, modeling, and normalization principles. For further reading, you might find these articles helpful: What is a Snowflake Schema? What is a Star Schema?

In addition, familiarity with other database types, specifically NoSQL databases, is essential. These databases, which can be file-based, column-oriented (often utilized in contemporary Data Warehouses), or graph-oriented, are becoming increasingly significant—examples include MongoDB and Cassandra. Hybrid systems, such as Amazon Redshift or Google BigQuery, are frequently employed in Data Warehousing.

NoSQL vs. SQL — Image Source: TechTarget

Understanding how to access these systems is equally vital. Whether through basic exports like CSV files or direct data connections using interfaces such as REST, ODBC, or JDBC, knowledge of socket connections and client-server architectures is beneficial. Additionally, becoming familiar with the characteristics and quirks of source systems is invaluable. Engaging with product owners or IT personnel who possess the necessary expertise can prove advantageous. Here, I have compiled some helpful tips in this article.

However, one critical takeaway is the importance of being aware of potential issues within source systems. Neglecting these factors can severely disrupt your data integration processes, leading to complications such as duplicate and inconsistent data. As mentioned earlier, understanding the source systems and their challenges is crucial.

If you're curious about the earnings of Data Engineers, you may find this article informative.

I hope this overview provides insight into a fundamental aspect of a Data Engineer's role—the understanding of database technologies. This knowledge serves as a foundational element for successful data integration. In the coming weeks, I will share more articles covering additional areas essential for Data Engineers, so stay tuned.

Chapter 2: Essential Skills for Data Engineers

In the first video, "What Skills Do Data Engineers Need To Know," the presenter outlines the essential skills necessary for Data Engineers to excel in their roles and improve their job marketability.

The second video, "What skills do you need as a Data Engineer?" provides further insights into the competencies that are critical for success in the field of data engineering.