Introduction to Data Engineering

Big data is data that is so large that you must consider how to deal with it. Big data is distinguished by its volume, velocity, value, diversity, and veracity. That is why data engineers exist. A data engineer is in charge of data ingest, collection, and storage.
The process of ingesting and storing data so that it is accessible and ready for analysis is known as data engineering. In other words, they design and build large-scale data collection, storage, and analysis systems.

Data Engineering Roles

To better understand the role of data engineer, I'll provide a brief overview of the data science process using the image below as a guideline.

1. The first step is to collect and store raw data from around the world.
2. The following step is data pre-processing, which includes cleaning, filtering, querying, and aggregation.
3. Following that, we will look at visualization and EDA (Exploratory Data Analysis), which will assist a machine learning engineer in deciding which models and algorithms to use.
4. Finally, we make decisions such as forecasting and predictions. and generate reports based on the data collected

A data Engineer is responsible for the first two steps in the Data Science Workflow. Data engineers store and ingest data, they are also responsible for setting up a database and building data pipelines.

Why do we need Data Engineers

As previously stated, data engineers are in charge of the first step in the data science workflow. Without them, machine learning engineers, data analysts, and data scientists would be forced to work with raw, unprocessed data. Engineers are required because they process and prepare data for future analysis. Data engineers also assist in the collection, storage, and optimization of data for usability.

Data Engineer Vs Data Scientists

Data engineers transform unstructured data into a more useable format for data analysts while searching through the data for insights. They develop data warehouses for sizable databases, maintain the architecture and design of the data, and construct queries.

On the other side, data scientists gather and organize unstructured data, develop models for using big data, and do big data analysis.

Strong programming skills are required for data engineering, whereas strong analytical skills are required for data science.