What is data engineering? Complex guide with examples

Maria Chojnowska

3 March 2023, 9 min read

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What's inside

  1. Data engineering - definition
  2. Why do companies need data engineering?
  3. What is data science, and how is it related to data engineering?
  4. Tools used in data engineering
  5. Data engineering trends
  6. How to find a qualified data engineer?
  7. Contact us
  8. Read more

Today's business and personal data are generated at a dizzying pace. It is estimated that in 2025 they will reach a size of as much as 175 zettabytes, nearly four times more than in 2019. Such a significant increase results from the dynamic development of communication and digital business. This is one of the reasons why enterprises worldwide need services that help in the appropriate collection and processing of data and assess its usefulness, allowing them to make the right business decisions and develop their business. One such process is data engineering.

But what is it really about? Let’s tackle this issue in more detail.

Data engineering - definition

Data engineering is preparing, building, and maintaining infrastructure and systems that store, process, and analyze data. It's an interdisciplinary field that combines software engineering, data science, machine learning, statistics, databases, and computer science skills. It requires a mix of theoretical knowledge and practical skills in tools, libraries, and frameworks to handle extensive scale data and make it available to others.

Data engineering can include tasks such as:

  1. Designing and implementing data storage solutions, such as databases and data warehouses.
  2. Building and maintaining data pipelines to ingest, clean, and transform data from various sources.
  3. Designing and implementing data processing systems, such as batch processing and stream processing systems.
  4. Building and maintaining data infrastructure, such as clusters and distributed systems.
  5. Implementing security and compliance controls for data.
  6. Managing and monitoring the performance and scalability of data systems.
  7. Optimizing data structures and algorithms for efficient data processing and analysis.

Why do companies need data engineering?

Data engineering enables companies to turn data into insights and actions. By providing the infrastructure and systems needed to store, process, and analyze data, data engineering enables data scientists, analysts, and other users to make data-driven decisions that can enable business growth and innovation.


Storing and managing large amounts of data

As companies collect more data from various sources, they need systems to store and manage that data efficiently. Data engineering can help them design and implement data storage solutions, such as databases and data warehouses, that can handle large volumes of data and support their organization's specific needs.

Making data accessible and useful

Data engineering can help companies create data pipelines that can ingest, clean, and transform data from various sources into a format that can be easily analyzed and used to make business decisions.

Enabling real-time analysis

With an increasing demand for real-time insights, data engineering can help companies design and implement systems that can process data in real time, such as stream processing systems for applications like fraud detection, anomaly detection, recommendation systems, and other use cases requiring real-time insights.

Scaling with growing data volumes

Companies must ensure that their data systems can scale to handle the increased volume as they collect more data. Data engineering can help companies build and maintain data infrastructure, such as clusters and distributed systems, that can handle the increased load and ensure that data systems continue to perform well as data volumes grow.

Meeting compliance and security requirements

Data engineering can help companies implement security and compliance controls for data, such as encryption and access controls, to protect sensitive information and meet regulatory requirements.

While data engineering handles technical aspects of working with data, data science is focused on extracting insights and knowledge from data using statistical and computational methods.

It includes:

  1. Data exploration and analysis to understand patterns and relationships, identify potential areas of interest, and clean and preprocess data to prepare for further analysis.
  2. Modeling and algorithm development to make predictions or identify trends based on the data. It can involve selecting and training machine learning models, building statistical models, and developing custom algorithms.
  3. Evaluation and refinement of models and algorithms performance, fine-tuning and improving them based on the results, and validating the models to ensure they are working as intended.
  4. Communication and presentation of findings and insights to others, stakeholders, or team members, in a way that is easily understood by non-technical audiences.
  5. Deploy and maintain models and algorithms in a production environment, monitor and maintain them over time, and update them as necessary based on new data or changes in the business environment.
  6. Domain expertise helps understand the problem in more detail and select appropriate approaches.

In addition to these core tasks, data science may involve database management, big data technologies, cloud computing, and more. The same tasks and activities can vary depending on the application and the organization's needs.

Tools used in data engineering

Data engineering is a broad field involving various tools and technologies, depending on the specific task or pipeline being built.

The most common ones include the following:

  1. Data storage systems: databases such as MySQL, PostgreSQL, MongoDB, and Cassandra, and distributed file systems, such as HDFS, and S3, are used to store large amounts of structured and unstructured data.
  2. Data processing platforms: Apache Hadoop and Apache Spark are popular open-source frameworks for processing and analyzing large amounts of data.
  3. Data pipeline and workflow management tools: Apache NiFi, Apache Airflow, and Apache Kafka are used to build data pipelines, schedule jobs, and manage data flow between systems.
  4. Programming languages: Python is used for data cleaning, data visualization, and machine learning tasks, and SQL is used for working with relational databases.
  5. Data visualization and reporting tools: Tableau, Power BI, and Looker are commonly used to create visualizations and reports from data stored in databases and warehouses.
  6. Cloud-based tools and services: many organizations use cloud-based tools and services such as AWS Glue, Azure Data Factory, and Google Cloud Dataflow for data engineering tasks, particularly for scalability and cost-effectiveness.
  7. Containerization tools: tools such as Docker and Kubernetes are used for packaging dependencies and deploying data engineering systems in a reproducible and scalable way.
  8. Version Control Systems: Git and SVN are used to keep track of different versions of the data pipeline and code.

This is a partial list, and many other tools are available, but the above are some of the most widely used in the field.

Data engineering is a rapidly evolving field. New trends and technologies are constantly emerging, so data engineers must stay updated to use the latest tools and techniques to build and maintain robust, efficient, and effective data pipelines.

Here are a few of the current trends in data engineering:

  1. Cloud-based data engineering

Many organizations are moving their data engineering workloads to the cloud to take advantage of cloud-based services' scalability, cost-effectiveness, and flexibility. AWS, Azure, and Google Cloud offer a wide range of data engineering tools and services that can be used to build and manage data pipelines and warehouses.

  1. Streaming data processing

The rise of real-time data, such as IoT and social media, has increased the use of streaming data processing technologies, like Apache Kafka, Apache NiFi, and Apache Pulsar, to process and analyze data in real time.

  1. Machine Learning and AI

With the increasing availability of powerful machine learning tools and the growth of big data, many organizations are using data engineering to build and support machine learning pipelines. This trend is expected to continue and leads to creation of new open-source libraries and frameworks that can handle the entire pipeline from data collection to model training and deployment.

  1. Data Governance

With the increase in data generation and governance, the policies and procedures governing data management are becoming increasingly important. Data governance tools and techniques are used to ensure data quality, lineage, and security. It is also crucial for compliance with laws such as GDPR and CCPA.

  1. Containerization and Kubernetes

Containerization and Kubernetes have become increasingly popular for data engineering, as they make it easy to package and deploy data engineering systems in a reproducible and scalable way.

  1. Serverless

Serverless architectures allow running code without the need to provision or manage servers. This can enable data engineers to focus on writing code rather than managing infrastructure.

How to find a qualified data engineer?

Finding a qualified data engineer can be a challenging task, but there are several steps you can follow to find the right candidate for your organization.

Firstly, identify the specific skills and qualifications required for your organization's data engineering role. This could include experiences with particular technologies, such as Hadoop, and broader skills, like data modeling, data warehousing, or data pipeline design.

Consider candidates with relevant certifications, such as the Cloudera Certified Data Engineer or the AWS Certified Big Data - Specialty. They demonstrate that a person has a piece of specific knowledge and expertise in the field.

As data engineering is a problem-solving role, look for candidates with experience solving complex data-related problems. Ask for examples of issues they have solved in the past and how they approached them. Data engineering requires a solid technical skill set, good communication skills, and the ability to work in a team and under pressure.

By following these steps, you can increase the chances of finding a qualified data engineer who has the skills and experience needed to help your organization achieve its data-related goals.

Contact us

Choose Sunscrapers for your data engineering or data science project to benefit from our versatile experience and world-class expertise in Python.

We can help you unlock the hidden potential of your data to improve decision-making processes, automate tasks, and boost operational efficiency.

Contact us at hello@sunscrapers.com

Read more

  1. How To Become A Data Scientist?
  2. Data Mining vs. Machine Learning: What do they have in common and how are they different?
  3. 24 Python machine learning libraries for data science projects
  4. Data warehouses - what they are and how to classify them (Part 1)
  5. Data visualization in Python, Part 1: Cellular Automata


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