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An Introduction to Data Engineering: The What, Why and How

What is Data Engineering

An introduction to data engineering

This article will introduce you to the what, why, and how of data engineering. We will discuss the fundamental concepts and principles of data engineering and the tools and techniques used in the field. We will also explore the challenges and opportunities of data engineering in today’s world. 

Finally, you will learn about the skills and qualifications that are necessary for a successful career in data engineering. By the end of this article, you will have a better understanding of the data engineering field and how it can benefit your business.

What is data engineering?

Data engineering is the process of designing and managing the systems that collect, store, process, and optimize large amounts of data. In other words, it is the bridge between software engineering, data science, and business intelligence. Data engineers do all the legwork to get data prepared for data scientists so it’s easily accessible and ready to be analyzed.

Data engineering involves the design, implementation, and operation of a data pipeline. It includes the collection and cleaning of data, the storage of data, and the delivery of data to the right users. It is an iterative process that may require data engineers to go back and forth between different phases. This is because the analysis of collected data will help them understand the strengths and weaknesses of different systems — and the business needs of their clients.

Data Engineering Process

A data pipeline consists of multiple components that work together to process data and deliver it to the right users. Such components include data ingestion, data storage, data processing, and data delivery.

The data ingestion component is responsible for accepting data from different sources. Whereas the data storage component stores the processed data in a format that can be analyzed and used by data scientists and business users. 

The data processing component transforms raw data into processed data that can be used for analytics. And the data delivery component delivers the processed data to the right users in various formats such as visualizations, reports, and recommendations.

The data engineering process

The responsibilities of a data engineer typically involve the design, development, maintenance, and management of an organization’s data systems. This includes the collection, storage, and analysis of large amounts of data from various sources.

Data engineers are responsible for developing and implementing data models, data warehouses, databases, and other data-related systems that support the organization’s business processes. Additionally, data engineers develop data pipelines, ETL processes, and data integration systems to ensure the accuracy and integrity of the data.

Here are some of the most common responsibilities of data engineers:

  • Gathering raw data
  • Performing quality control on data
  • Optimizing databases
  • Building data pipelines 
  • Making data easily accessible for data scientists
  • Having strong development skills in Python, SQL, and/or Java

On the other hand, here are some of the responsibilities that are for data scientists (not data engineers):

  • Creating visualizations
  • Making prediction models
  • Performing statistical analysis
  • Building AI models

The importance of data pipelines

A data pipeline is a set of processes or tasks that are used to automate the data flow and management between various stages of a data system. It enables organizations to access, transform, and analyze data in order to make better decisions. Data pipelines are part of the data engineer toolkit. It typically includes components such as data ingestion, data processing, and data storage. But it can also include data reporting as well. 

Data pipelines move data from one source to another and process it in various ways. For example, a data pipeline can be used to extract data from a database, apply transformations to the data, and load the data into a data warehouse. The data warehouse can then be used to generate reports or perform analytics – typically from a data analyst or business analyst

Data pipelines also help organizations manage their data and ensure it is secure, up-to-date, and accurate. Data pipelines can be used to integrate data from multiple sources and provide a single view of data for analysis. Additionally, data pipelines can also be used to automate tasks such as data quality checks and data cleansing. Overall, they reduce human intervention and error, yet increase productivity.

Data Pipeline Flowchart

Why is data engineering important?

As data volumes grow, businesses need efficient and scalable systems to collect, store, and process data. For example, e-commerce companies collect data about their customers, products, and sales. They also collect data from third-party sources such as social media, news articles, and weather reports. This data can help businesses understand their customers better and make better decisions.

Data engineering uses distributed systems and cloud computing to handle the large amounts of data collected by businesses. First, it often uses distributed systems in order to process data at scale and make the best use of available resources. Secondly, it makes use of cloud computing to lower IT costs and process data optimally.

Big data and data engineering often go hand-in-hand so this is why we refer to the five Vs of big data. Whenever we describe big data, we can characterize it by its volume, variety, velocity, veracity, and value. Data engineering may also use machine learning and artificial intelligence to analyze data and make smart decisions.

What Is Big Data

What are the core principles and concepts of data engineering?

Once you understand the basics of data engineering, you will be able to understand the core principles and concepts that underpin the field. It is important to understand the core concepts of data engineering and how they relate to each other. These concepts are the foundation of data engineering and will help you succeed in the field.

  • Data Ingestion – Data collection starts with data ingestion, which means accepting data from multiple sources. Data ingestion is often the first step in data engineering, and it is followed by data cleaning and data storage. Data ingestion can be done in a distributed manner, and it often makes use of microservices.
  • Data Cleansing – Data ingestion is just the first part of the data collection process. The next step is data cleansing, which is the manual or automated process of removing impurities from the collected data. This step is important because data impurities can skew the insights and decisions that you make from the collected data.
  • Data Transformation – Once you’ve collected the data and removed impurities, you need to transform it into a format that can be processed and analyzed. This is known as data transformation. Data transformation can be done by simple functions or by machine learning algorithms.

Which tools and techniques do data engineers use?

Once you understand the core concepts of data engineering, you can dive into the tools and techniques used in the field. Data engineers use a variety of tools and techniques to process data and deliver value to their clients. Here are some of them:

  • Data Warehouse and Data Lakes – A data warehouse is a centralized repository of data that is optimized for analytical queries. A data lake is a decentralized and sometimes unstructured repository of data that is optimized for operational tasks. Data engineers can use data lakes and data warehouses to store the processed data from their systems.
  • Data Processing Engines – Data engineers can use data processing engines to clean and transform the data collected from their systems. They can choose from a variety of open-source tools that can be used for data processing. Some of these tools include Apache Spark, Apache Hadoop, and Apache Kafka.
  • In-Database Processing – In-database processing is an emerging technique that can be used to clean and transform data. It is executed within the storage engine of a database, which enables it to process large amounts of data much faster than traditional approaches.

What are the challenges and opportunities in data engineering?

The challenges and opportunities of data engineering are very similar to those of data science. As data engineering is a core part of data science, it is important to understand them and navigate them successfully in the field.

The following challenges and opportunities are common in data engineering and data science:

  • Overcoming the Variety and Volume of Data – Data engineers are responsible for collecting and transforming large amounts of data. This is often the core part of their job and something that data scientists also do. Both data engineers and data scientists need to overcome the variety and volume of data in order to make informed decisions. They can do this by standardizing their data collection processes, using metadata, and using automated systems.
  • Building Robust and Scalable Systems – Data engineers are responsible for building robust and scalable systems that can process data at scale. Data engineers can use DevOps techniques and automation to build their systems. They can also use open-source tools to build and scale their systems.

What skills and qualifications are needed for a successful career in data engineering?

In order to become a successful data engineer, you should learn the skills and qualifications of the field. Data engineers need to have a strong understanding of data architecture and design. They should know how different systems fit together and how they can process large amounts of data.

Data engineers should know how data gets collected, transformed, stored, and delivered to users. This will help data engineers build their own systems more efficiently. Data engineers should also know how to build robust and scalable systems that can process data at scale. This will help them meet the needs of their clients and deliver value to them.

A data engineer also needs to understand how machine learning works. They should know how they can transform their data and use algorithms to process it. This will help them use machine learning effectively in their systems. Finally, data engineers need to be aware of emerging techniques and technologies in data engineering. They need to understand new technologies such as in-database processing and how they can make the best use of them.

How can data engineering benefit businesses?

Data engineering is an important field that enables businesses to make smarter decisions based on their data. This can lead to better products, more efficient processes, and happier customers. Data engineering can benefit businesses in many ways:

  • Better Decision Making – Data engineering enables businesses to make better decisions based on their data. Such decisions may include pricing, product design, marketing campaigns, and customer targeting. Data engineering allows businesses to use the data they’ve collected to their advantage.
  • Better Product Design – Data engineering can be used to design better products that can meet the needs of customers. This can be done by understanding the customers’ needs and creating products that fit their requirements. Data engineering can be used to collect data about customers and define the requirements of specific customers.
  • More Efficient Processes – Data engineering can help businesses optimize their processes and collect data about them. This can help businesses identify inefficiencies and fix them. It can also help businesses predict the future based on past data.


Data engineering is an important field that deals with the collection, storage, cleaning, and analysis of large amounts of data. Data engineers use distributed systems and cloud computing as part of their data pipelines and ETL processes.

It is the foundation of modern machine learning, artificial intelligence, and other applications of data science. Overall, data engineering is essential for businesses that need to make informed decisions based on large amounts of data.

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