Data Engineering for Beginners: A Step-by-Step Guide

Introduction:
In today's world, data engineering is one of the most important fields. Data engineering is a set of processes, tools and techniques that we use to collect, store, process and analyze data. This is more important in organizations and companies because strategic decisions are made based on accurate and reliable data. In this article I will provide a step-by-step guide to get started and understand the basic concepts of data engineering.
Basic concepts of data engineering
The basic concepts of data engineering include data definition, data sources, data processing and how to use data in decision making. In data engineering, we use the data to get information that can be collected, stored, processed and analyzed. Data sources can range from internal sources of the organization such as databases, file systems and logs to external sources such as sensors, social data and web data. Data processing includes the processes of data cleaning, transformation and analysis which are done to extract useful information and patterns from the data. Finally, data is used as a basis for strategic decision-making in organizations and companies.
Data collection
The data collection part of data engineering is the process in which data is collected from various sources and transferred to a central location. This process includes steps such as extracting, cleaning, combining and storing data. data sources are first identified which may include databases, file systems, sensors, social data and web data. Then, data is extracted from these sources and collected in raw form.
The next step is data cleaning which includes removing duplicate data, filling in blank values, resolving conflicts and converting data to a standard format. This step is important to maintain the accuracy and usability of the data in next steps.
The data is then combined to create a complete and integrated dataset. This process involves merging different data from different sources which may include combinations of rows, columns, tables or irregular data.
Finally the data is moved to a central location, for example a central database or central file system, so that it is organized and accessible. The main goal of this part is to provide an orderly and optimal environment for performing data processing and analysis processes in the next parts. Data collection which is the most basic step in data engineering is necessary for the effective and efficient use of data in strategic and operational decisions of organizations.
data storage
The data warehousing part of data engineering is the process by which data is organized and permanently stored in a central location. This part of the data engineering process makes sure that data is available in a secure and accessible way and that data can be searched, retrieved and updated.
In this section, databases are usually used as the main tool for data storage. Based on the organization's structure databases can be relation-based such as relational databases or non-relation-based such as NoSQL databases.
In database design and selection factors like data volume, access speed, stability, security, analytical and organizational needs are considered. Also, various technologies such as relational databases, columnar databases, document databases and graph databases are also used for data storage.
Data processing
The fourth part of data engineering is the data processing and analysis part where data is analyzed and processed to extract useful information and patterns. This part of the data engineering process enables optimal use of data and helps organizations make better decisions based on data.
Here data is processed with various techniques and algorithms like feature extraction, statistical refinement and analysis, modeling and machine learning, data quality improvement, data mining and artificial intelligence, natural language processing and other related methods.
The main purpose of this section is to extract useful information and hidden patterns in data, predict events and behaviors, identify relationships and meaningfulness between data, improve data quality and provide methods for better decision making.
Using this segment, organizations can use their data to analyze trends, predict performance, improve processes, identify customers and their behavior, increase productivity, reduce risks, and make strategic decisions. This part of data engineering enables organizations to exploit data as a major strategic asset and make decisions based on evidence and more accurate information.
Data maintenance and management
The part of data engineering is the part related to the deployment and implementation of data solutions. In this section the solutions designed in the previous parts of data engineering are transferred to the operational operations of the organization. In this part data solutions are implemented to use the data and information available in the organization. This process includes installation, configuration, testing and commissioning of data solutions.
The main objective of this department is to ensure the successful implementation of data solutions in the organization. At this part the implementation of data solutions is carried out to improve the performance of the organization, make better decisions, increase competitive ability and improve business processes.
To succeed in this sector, change management, training and preparation of employees, communication and coordination between teams, quality control and continuous support of data solutions are very important. Also, maintaining and updating the solutions and adapting them to the needs of the organization over time is of great importance.
By using this segment organizations can implement and operate their data solutions effectively and efficiently in their processes. Deploying data solutions helps organizations use their data as a powerful tool for strategic decision-making and improve performance against competitors and in the marketplace.