Overview of Data Engineering

During my journey as a software engineer I decided to change focus to more of a backend, data driven development path and I started my Data Engineering journey today with DataCamp and couldn't be more excited. In my first course which was an overview of data engineering this was my understanding of the basic concepts.

Data engineering is the process of designing and creating systems for the collection, storage, processing, and analysis of data. A data engineer is responsible for developing, constructing, testing, and maintaining data architectures and systems.

Basic Concepts and Understanding

Data Pipeline

A data pipeline is a sequence of steps for moving data from one location to another, transforming it along the way. It consists of several components, including data ingestion, data processing, data storage, and data delivery.

It helped me to think of a pipeline as just simply a highway. There is a lot going on while on the highway just like through a pipeline theres many components in play.

ETL

ETL stands for Extract, Transform, and Load. It is a popular method for moving data between systems. In this process, data is extracted from a source system, transformed into a format that can be used by the target system, and loaded into the target system.

Data Warehouse

A data warehouse is a system used for storing and managing large volumes of data. It is designed to support business intelligence activities such as reporting, data analysis, and data mining.

Data Lake

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It enables you to break down data silos and combine different types of data to gain insights and make better decisions.

For someone completely new to coding, understanding data or anything related to this the differences between data lake and data warehousing can be challenging to grasp.

Conclusion

Data engineering is a crucial discipline for any organization that deals with large volumes of data. Understanding the basic concepts and principles is essential for building robust and scalable data architectures and systems.